Accuracy Of Clinical Diagnosis Research Articles

Pulmonary microbial spectrum in late-stage SARS-CoV-2 infection: a case series.

Coronavirus disease 2019 (COVID-19), a kind of respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), primarily spreads through the respiratory tract from human to human. Its extensive and rapid spread has led to a global pandemic, causing great harm to human health and economic development all over the world. Current known evidence indicates that SARS-CoV-2 has evolved accumulating multiple mutations, with altered infectivity and viral replication capacity. A better understanding of the complications of COVID-19 and its relationship with underlying diseases is crucial for the prevention and treatment of SARS-CoV-2. This case series reviewed case data of our 4 recent patients with severe or critical COVID-19, including treatment plan, status of pulmonary infection and their microbiology workup with metagenomic next-generation sequencing with bronchoalveolar lavage fluid. This report shed light on the significance of rapid and accurate clinical diagnosis and treatment on COVID-19.

Kniest Dysplasia without Ocular and Auditory Abnormalities in a Boy of 12 Months

Introduction: Kniest dysplasia is a rare skeletal disorder, characterized by mild dysmorphic features, cleft palate, short stature, short limbs, prominent joints, restricted joint mobility, hearing impairment, and ocular manifestations such as high-degree myopia, retinal detachment, and cataract. Typical radiological findings include platyspondyly, coronal clefts, and dumbbell-shaped long tubular bones. Case Presentation: Herein, we report on an 8-month-old boy who was referred to the pediatric genetic department due to narrow thorax and short extremities. He had mild dysmorphic features, cleft palate, narrow thorax, short extremities, and short stature. On radiographies, platyspondyly, hemivertebra, and dumbbell-shaped long tubular bones were detected. Clinical and radiological findings were consistent with Kniest dysplasia. Clinical exome sequencing was performed and revealed a heterozygous, pathogenic c.905C>T (p.Ala302Val) variant in the COL2A1 gene, confirming the initial clinical diagnosis. Discussion: Kniest dysplasia is a very rare skeletal dysplasia, and an accurate clinical diagnosis is important to provide the best possible follow-up.

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.

Brain Evaluation by Dual PET/CT with [18F] FDOPA and [18F] FDG in Differential Diagnosis of Parkinsonian Syndromes

Parkinsonian syndromes are considered clinicopathological conditions that are challenging to diagnose. Molecular imaging with [18F]-FDOPA and [18F]-FDG contributes to a more accurate clinical diagnosis by evaluating presynaptic dopaminergic pathways and glucose metabolism, respectively. The aim of this study was to correlate diagnoses made from dual PET/CT with the initial clinical diagnoses, as well as during follow-ups in patients with Parkinsonian syndromes. A secondary objective was to describe the imaging findings. Methods: A total of 150 patients with a clinical diagnosis of neurodegenerative Parkinsonism were evaluated using dual PET/CT. Clinically, 82% were diagnosed with PD, while the remaining 18% had an atypical Parkinsonism. Results: Using dual PET/CT, the most frequent diagnosis was PD in 67% of the patients, with the rest being diagnosed with an atypical Parkinsonism. In an agreement analysis between the initial clinical diagnosis and the imaging diagnosis by dual PET/CT, a concordance of 94.1% (n = 95) was observed for PD. In the remaining patients, the clinical diagnosis differed from that suggested by dual PET/CT, with atypical Parkinsonian syndromes being diagnosed as DLB in 40% (n = 4), PSP in 46.7% (n = 7), MSA-C in 75% (n = 6), MSA-P in 70% (n = 7), and CBD in 66.7% (n = 4). A total of 38.66% (n = 58) of patients were followed up (median follow-up of 27 months), with a Kappa coefficient of 0.591 (p < 0.001), suggesting substantial agreement. Conclusions: Dual FDOPA–FDG PET/CT demonstrated moderate agreement with the initial clinical diagnosis of Parkinsonism and moderate to substantial agreement during follow-up. This dual technique, therefore, stands out in differentiating between types of Parkinsonisms.

Research on the Design of Medical Imaging Assisted Diagnosis System Based on Machine Learning

The design and application of medical imaging diagnostic assistance systems have become an indispensable facet of modern medicine. Technologies based on machine learning can handle vast and intricate medical imaging data, offering precise diagnostic support and significantly enhancing the efficiency and accuracy of clinical diagnoses. This study focuses on the development and implementation of an efficient medical imaging diagnostic assistance system utilizing machine learning algorithms such as Principal Component Analysis (PCA), Support Vector Machines (SVM), and Multi-Layer Perceptrons (MLP). Through the architectural design of intelligent diagnostic assistance systems, data preprocessing, and feature vectorization, combined with the application of classification algorithms, a robust diagnostic support system capable of addressing diverse medical scenarios has been constructed. Performance evaluation experiments indicate that this system demonstrates high accuracy and robustness in processing medical imaging data, holding potential for clinical implementation. The findings of this study provide new insights for the advancement of intelligent diagnostic systems in medical imaging and lay a solid foundation for the development of future clinical diagnostic tools.

My experience of person-centered and personalized care in early-stage primary progressive aphasia.

Primary progressive aphasia (PPA) is a neurodegenerative brain disorder characterized by declining language ability. It is a rare, often young-onset dementia with a devastating impact on the work and personal activities of those affected. At present there is no cure or disease-modifying therapy for PPA nor any way to arrest or slow the underlying progressive brain degeneration. Throughout the course of the condition any treatment must therefore be palliative-designed to manage symptoms and improve the quality of life of the affected person. The majority of those affected receive little or no follow-up care after diagnosis, particularly in the early stage of the disease. There is very little information in the medical literature about person-centered care designed to improve the quality of life of people with PPA written from the perspective of those living with this condition. I received an early and accurate clinical diagnosis of the nonfluent/agrammatic variant of PPA, supported by imaging. I am fortunate to have benefited from exemplary individualized care from a multidisciplinary medical team from the onset of my difficulties with language. In this paper, I discuss my lived experience of all aspects of this personalized and person-centered care, describing how it was founded on shared decision-making and a holistic, dementia-inclusive approach encompassing the physical, mental, emotional, psychosocial and spiritual dimensions of living with an incurable neurodegenerative disease.

Domain-Adaptive Framework for ACL Injury Diagnosis Utilizing Contrastive Learning Techniques

In sports medicine, anterior cruciate ligament (ACL) injuries are common and have a major effect on knee joint stability. For the sake of prognosis evaluation and treatment planning, an accurate clinical auxiliary diagnosis of ACL injuries is essential. Although existing deep learning techniques for ACL diagnosis work well on single datasets, research on cross-domain data transfer is still lacking. Building strong domain-adaptive diagnostic models requires addressing domain disparities in ACL magnetic resonance imaging (MRI) from different hospitals and making efficient use of multiple ACL datasets. This work uses the publicly available KneeMRI dataset from Croatian hospitals coupled with the publicly available MRnet dataset from Stanford University to investigate domain adaptation and transfer learning models. First, an optimized model efficiently screens training data in the source domain to find unusually misclassified occurrences. Subsequently, before being integrated into the contrastive learning module, a target domain feature extraction module processes features of target domain samples to improve extraction efficiency. By using contrastive learning between positive and negative sample pairs from source and target domains, this method makes domain adaptation easier and improves the efficacy of ACL auxiliary diagnostic models. Utilizing a spatially augmented ResNet-18 backbone network, the suggested approach produces notable enhancements in experimentation. To be more precise, the AUC for transfer learning improved by 3.5% from MRnet to KneeMRI and by 2.5% from KneeMRI to MRnet (from 0.845 to 0.870). This method shows how domain transfer can be used to improve diagnostic accuracy on a variety of datasets and effectively progresses the training of a strong ACL auxiliary diagnostic model.

Electrochemical Detection of NT-Probnp Using High Performance Fully Inkjet-Printed Organic Electrochemical Transistors

Cardiovascular diseases are the number one cause of death worldwide, with Heart Failure (HF) accounting for a third of these fatalities. Therefore, there is a growing need for a point-of-care (POC) device capable of providing timely and accurate information on HF to minimize its impact on patients. N-terminal pro-B-type natriuretic peptide (NT-proBNP), released by myocardial cells in the blood in response to stress or strain, is regarded as the gold standard biomarker for HF management given its superior clinical relevance in diagnosis, prognosis, and therapy. Electrochemical potentiometric biosensors are promising alternatives to traditional analytical methods for biomarker detection. In particular, organic electrochemical transistors (OECTs) have garnered increased attention due to their ability to interface bodily fluids by simply operating inside an ionic environment. Additionally, they possess inherent transistor-specific amplification capabilities represented by the transconductance (gm), shown to scale with the thickness of the semiconducting channel, yielding values that are orders of magnitudes higher than those of field effect transistors (FETs). This feature enables OECTs to detect and amplify the presence of the target molecule without relying on complementary methodologies.HF patients lack self-operated and reliable POC devices that can provide immediate information about their health ensuring clinical accuracy for diagnosis and intervention. While several methods have been proposed, they fail to combine self-operation, speed, low-cost, and clinical accuracy. In this work, we aim to develop a POC HF sensor that can detect and quantify NT-proBNP levels in a finger prick blood volume, all while being cheap to fabricate, easy to operate, and rapid in providing results.OECTs were fabricated using inkjet-printing. First, the source, drain, and gate (2.5x2.5 mm) were patterned using a conductive gold nanoparticle ink. Subsequently the leads were insulated with polyimide, after which PEDOT:PSS was deposited to form the semi-conducting channel (15x400µm). Antibody immobilization for NT-proBNP detection was achieved by chemical functionalization of either the channel or the gate. To functionalize the channel, the PEDOT:PSS surface was treated with oxygen plasma to introduce hydroxyl groups. The following steps included silanization, then streptavidin grafting by immersion, and finally, antibody immobilization using biotinylated anti-NT-proBNP antibodies. To functionalize the gate, a quick three step protocol was adapted by employing thiolated-biotinylated-Polyethylene Glycol followed by streptavidin and then biotinylated antibodies. Surface functionalization, in both cases, was verified using electrochemical impedance spectroscopy (EIS). To detect the presence and quantify the levels of NT-proBNP using the realized OECT, several concentrations of NT-proBNP were prepared in PBS and tris-base buffer (pH=8.5) and introduced into the device sensing area. The different concentrations were distinguished through scanning of the transfer characteristic at drain-source voltages (VDS)=-0.05V or -0.5V and gate-source voltages (VGS)=0 to 0.8V.The developed biosensor has a miniaturized active recording area, enabling the use of minimal volumes to operate and detect NT-proBNP. The functionalization of either the in-plane gold gate or the PEDOT:PSS channel was validated by an increase in the electric impedance of the system at every step. Repetitive transfer characteristics scans revealed better stability at -0.05 VDS compared to -0.5 VDS, which is attributed to decreased channel current, and subsequently, preserved channel integrity in solution at the lower bias. The latter increases channel stability, but decreases gm, yet remains sufficiently high to successfully detect NT-proBNP at clinically relevant concentrations (0 to 1000 pg/ml). Functionalizing the gate led to a linearity of 95%, and a decrease in variability across trials compared to functionalizing the channel which can be attributed to the smaller detection area and better reactivity. The sensor also demonstrated selectivity by exhibiting a stronger response to NT-proBNP compared to BSA (a non-specific protein) proving the effectiveness of the chemical functionalization, which is particularly important when using the sensor with a complex solution like blood.In this work, we have developed an electrochemical potentiometric solution towards HF POC applications, capable of quantifying NT-proBNP in microvolumes at clinically relevant concentrations with high sensitivity, specificity, and repeatability. The adapted microfabrication methodology provides a cheap and quick approach for producing high performing sensors, rendered sensitive to HF biomarkers by chemical modification. The functionalization was found to be efficient and faster when applied at the gate instead of the channel given the versatile nature of the in-plane gate and the size-dependent performance limitations of the channel. The successful bedside integration of such technology holds promise in reducing HF mortality by enabling early diagnosis and immediate intervention, improving disease progression and management, and facilitating personalized strategies for prognosis, therapy, and prevention.

Establishment of the contribution of AGBL5 in autosomal recessive retinitis pigmentosa: Experience from an Iranian family evaluation and literature review

BackgroundRetinitis Pigmentosa (RP) as inherited disease of the retina causes vision impairment due to progressive abnormalities of photoreceptors or the retinal pigment epithelial cells. RP as a clinical heterogenous disorder characterized by wide genetic heterogeneity with a broad range of causative genes involved in the genesis of the disease. Pathogenic variants in AGBL5 have been reported in few cases with RP. The aim of this study is to identify the accurate clinical diagnosis of RP in an Iranian family based on the combination of clinical and genetic investigations. Materials and methodsWe used whole exome sequencing (WES) to identify the pathogenic genetic defect responsible for RP in an Iranian family with consanguineous marriage. Using various filtering steps, we filtered out the exome data to narrow down the annotated variants. Variant prioritization was done based on a panel of RP genes. ResultsAll the annotated variants from WES analysis were passed through various filtering steps depending upon allelic frequency, genomic position, protein impact, pathogenic effect, previous relevance to the disease phenotype and quality of the variants. Finally, we found the nonsense homozygous change in AGBL5 gene (NM_021831.6; c.1729C>T; p.Arg577Ter). The RP phenotypes of the proband were similar to previous reports. ConclusionThis study can provide further evidence regarding the relationship of pathogenic variants in AGBL5 as a cause of RP and we believe that it can provide evidences for the relationship between clinical manifestations and pathogenic variants.

Implementation of Long-Read Sequencing for Routine Molecular Diagnosis of Familial Mediterranean Fever

BackgroundLong-read sequencing technology, widely used in research, is proving useful in clinical diagnosis, especially for infectious diseases. Despite recent advances, it hasn't been routinely applied to constitutional human diseases. Long-read sequencing detects intronic variants and phases variants, crucial for identifying recessive diseases. MethodsWe integrated long-read sequencing into the clinical diagnostic workflow for the MEFV gene, responsible for familial Mediterranean fever (FMF), using a Nanopore-based workflow. This involved long-range PCR amplification, native barcoding kit library preparation, GridION sequencing, and in-house bioinformatics. We compared this new workflow against our validated method using 39 patient samples and 3 samples from an external quality assessment scheme to ensure compliance with ISO15189 standards. ResultsOur evaluation demonstrated excellent performance, meeting ISO15189 requirements for reproducibility, repeatability, sensitivity, and specificity. Since October 2022, 150 patient samples were successfully analyzed with no failures. Among these samples, we identified 13 heterozygous carriers of likely pathogenic (LP) or pathogenic (P) variants, 1 patient with a homozygous LP/P variant in MEFV, and 4 patients with compound heterozygous variants. ConclusionThis study represents the first integration of long-read sequencing for FMF clinical diagnosis, achieving 100% sensitivity and specificity. Our findings highlight its potential to identify pathogenic variants without parental segregation analysis, offering faster, cost-effective, and accurate clinical diagnosis. This successful implementation lays the groundwork for future applications in other constitutional human diseases, advancing precision medicine.

Revisiting X-linked congenital ichthyosis.

X-linked recessive ichthyosis (XLI) is a hereditary skin disease characterized by generalized dryness and scaling of the skin, with frequent extracutaneous manifestations. It is the second most common type of ichthyosis, with a prevalence of 1/6,000 to 1/2,000 in males and without any racial or geographical differences. The causative gene for XLI is the steroid sulfatase gene (STS), located on Xp22.3. STS deficiency causes an abnormal cholesterol sulfate (CS) accumulation in the stratum corneum (SC). Excess CS induces epidermal permeability barrier dysfunction and scaling abnormalities. This review summarizes XLI's genetic, clinical, and pathological features, pathogenesis, diagnosis and differential diagnoses, and therapeutic perspectives. Further understanding the role of the STS gene pathogenic variants in XLI may contribute to a more accurate and efficient clinical diagnosis of XLI and provide novel strategies for its treatment and prenatal diagnosis.

Deep Learning Algorithms for the Detection of Suspicious Pigmented Skin Lesions in Primary Care Settings: A Systematic Review and Meta-Analysis.

Early detection of suspicious pigmented skin lesions is crucial for improving the outcomes and survival rates of skin cancers. However, the accuracy of clinical diagnosis by primary care physicians (PCPs) is suboptimal, leading to unnecessary referrals and biopsies. In recent years, deep learning (DL)algorithms have shown promising results in the automated detection and classification of skin lesions. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of DL algorithms for the detection of suspicious pigmented skin lesions in primary care settings. A comprehensive literature search was conducted using electronic databases, including PubMed, Scopus, IEEE Xplore, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science. Data from eligible studies were extracted, including study characteristics, sample size, algorithm type, sensitivity, specificity, diagnostic odds ratio (DOR), positivelikelihood ratio (PLR), negative likelihood ratio(NLR), and receiver operating characteristic curve analysis. Three studies were included. The results showed that DL algorithms had a high sensitivity (90%, 95% CI: 90-91%) and specificity (85%, 95% CI: 84-86%) for detecting suspicious pigmented skin lesions in primary care settings. Significant heterogeneity was observed in both sensitivity (p = 0.0062, I2 = 80.3%) and specificity (p < 0.001, I2 = 98.8%). The analysis of DOR and PLR further demonstrated the strong diagnostic performance of DL algorithms. The DOR was 26.39, indicating a strong overall diagnostic performance of DL algorithms. The PLR was 4.30, highlighting the ability of these algorithms to influence diagnostic outcomes positively. The NLR was 0.16, indicating that a negative test result decreased the odds of misdiagnosis. The area under the curve of DL algorithms was 0.95, indicating excellent discriminative ability in distinguishing between benign and malignant pigmented skin lesions. DL algorithms have the potential to significantly improve the detection of suspicious pigmented skin lesions in primary care settings. Our analysis showed that DL exhibited promising performance in the early detection of suspicious pigmented skin lesions. However, further studies are needed.

Diagnostic ability of Peptidase S8 gene in the Arthrodermataceae causing dermatophytoses: A metadata analysis.

An unambiguous identification of dermatophytes causing dermatophytoses is necessary for accurate clinical diagnosis and epidemiological implications. In the current taxonomy of the Arthrodermataceae, the etiological agents of dermatophytoses consist of seven genera and members of the genera Trichophyton are the most prevalent etiological agents at present. The genera Trichophyton consists of 16 species that are grouped as clades, but the species borderlines are not clearly delimited. The aim of the present study was to determine the discriminative power of subtilisin gene variants (SUB1-SUB12) in family Arthrodermataceae, particularly in Trichophyton. Partial and complete reads from 288 subtilisin gene sequences of 12 species were retrieved and a stringent filtering following two different approaches for analysis (probability of correct identification (PCI) and gene gap analysis) conducted to determine the uniqueness of the subtilisin gene subtypes. SUB1 with mean PCI value of 60% was the most suitable subtilisin subtype for specific detection of T.rubrum complex, however this subtype is not reported in members of T. mentagrophytes complex which is one of the most prevalent etiological agent at present. Hence, SUB7 with 40% PCI value was selected for testing its discriminative power in Trichophyton species. SUB7 specific PCR based detection of dermatophytes was tested for sensitivity and specificity. Sequences of SUB7 from 42 isolates and comparison with the ITS region showed that differences within the subtilisin gene can further be used to differentiate members of the T. mentagrophytes complex. Further, subtilisin cannot be used for the differentiation of T. benhamiae complex since all SUB subtypes show low PCI scores. Studies on the efficiency and limitations of the subtilisin gene as a diagnostic tool are currently limited. Our study provides information that will guide researchers in considering this gene for identifying dermatophytes causing dermatophytoses in human and animals.

Qualitative evaluation of a molecular point-of-care testing study for influenza in UK primary care.

Influenza contributes to the surge in winter infections and the consequent winter pressures on the health service. Molecular point-of-care testing(POCT) for influenza might improve patient management by providing rapid and accurate clinical diagnosis to inform the timely initiation of antiviral therapy and reduce unnecessary admissions and antibiotics use. To explore factors that influence the adoption or non-adoption of POCT in English general practices and provide insights to enable its integration into routine practice workflows. A qualitative implementation evaluation was conducted in ten general practices within the English national sentinel network (Oxford-RCGP Research and Surveillance Centre), from April to July 2023. Using the nonadoption, abandonment, scale-up, spread, and sustainability framework, data collection and analysis were conducted across ten practices. We made ethnographic observations of the POCT workflow and surveyed the practice staff for their perspectives on POCT implementation. Data were analysed using a mix of descriptive statistics, graphical modelling techniques and framework approach. Ethnographic observations identified two modes of POCT integration into practice workflow: 1) clinician POCT workflow - typically involving batch testing due to time constraints, 2) research nurse/healthcare assistant POCT workflow - characterised by immediate testing of individual patients. Survey indicated that most primary care staff considered the POCT training offered was sufficient, and these practices were ready for change and had the capacity and resources to integrate POCT in workflows. General practices should demonstrate flexibility in the workflow and workforce they deploy to integrate POCT into routine clinical workflow.

P-528 Establishing sequencing coverage depth for preimplantation genetic testing for monogenic disorders (PGT-M) using a specific targeted next generation sequencing (tNGS)

Abstract Study question What is the sufficient sequencing coverage read depth (RD) for direct variant analysis for PGT-M using a specific tNGS assay? Summary answer Downsampling modelling experiments identified a minimum sequencing depth of 50X that is recommended for PGT-M with this specific tNGS assay. What is known already Properly developed and validated multiplex PCR amplification and NGS-based analysis reliably produces PGT-M diagnoses with low allele and amplification failure at RD of hundreds of reads when analyzing a 3-10 cell trophectoderm (TE) biopsy. However, the RD can be affected by amplification efficiency in a specific genomic region and therefore affect the reproducibility of variant detection: the higher the number of aligned reads, the higher the confidence of base calling at a particular position. Downsampling is a powerful bioinformatics tool for modelling comparisons between samples or between replicates, identifying analytical sequencing depth limits for accurate clinical diagnosis. Study design, size, duration This is a retrospective comparative diagnostic test accuracy (DTA) and verification study. Twenty-three embryos from 8 patients that had undergone PGT-M with clinically established Taqman qPCR genotyping were used as positive controls. The primary outcome was to establish the lowest RD point of down sampling where allelic frequency is reliably detected at heterozygosity between 20-80% in known positive controls. A further 30 PGT-M clinical cases were used to verify analytical performances under clinical conditions. Participants/materials, setting, methods Embryo biopsies were pre-amplified with a multiplex primer pool for PGT-M and PGT-A, using a two-step PCR strategy. Sequencing was performed on Illumina NextSeq550. Reads were aligned to a human reference genome (GRCh37/hg19) and processed with an in house bioinformatic pipeline. The RD and alternative allele frequencies (AAF) for PGT-M was calculated. The reads for the heterozygous variants were downsampled to 10X, 20X, 30X, 50X, 100X, 200X, 300X, 500X, and 1000X to establish the cutoff. Main results and the role of chance One or two TE biopsies (N = 44) of 23 embryos that were 100% concordant with previous PGT-M results were tested for 1 to 3 of the 12 variants (MLH1 c.199G&amp;gt;A, RB1 c.1589A&amp;gt;G, SLC16A2 c.604G&amp;gt;A, GNPTAB c.1123C&amp;gt;T, GNPTAB c.3314A&amp;gt;G, WFS1 c.1391C&amp;gt;G, USH2A c.10073G&amp;gt;A, USH2A del exons 5-11, F5 c.1601G&amp;gt;A, GAA c.693-1G&amp;gt;C, GAA c.875A&amp;gt;G, and HEXA c.1274_1277dupTATC). PGT-A analysis of multiple biopsies from the same embryo demonstrated consistent karyotypes (N = 44). The 19 heterozygous variants from 17 biopsies exhibited average RD of 763X (238X -1980X) with average AAF of 50% (44%- 56%). The reads for the heterozygous variants were downsampled to different levels (100,000 replicates). The distribution of the AAF were compared among the different depth levels. At a RD of 50X, the AAF displayed an interquartile range (IQR) of 8% (46%-54%) between the maximum and minimum values from 20% to 78%. Compared to 30X (IQR: 43%-57%, with minimum 13% and maximum 90%), and 100X (IQR: 47%-54%, with minimum 30% and maximum 71%), the RD at 50X generated reliable heterozygous calls with minimum depth requirement. 245 biopsies from 30 clinic cases were analyzed by tNGS-based PGT-M. 90 samples with heterozygous variant calls showed average RD of 979X (57X-5803X) with average AAF of 47% (29%-585). Limitations, reasons for caution The 50X cutoff is based on the heterozygous variants with theoretical 50% AFF and is specific for this PGT-A assay. This evaluation should be corroborated for complicated variants with homologous genes, which might require different thresholds. The aneuploidy at specific chromosome can cause the deviation of AFF from 50%. Wider implications of the findings This specific tNGS-based PGT-M can provide direct variant analysis at a 50X read depth with a high level of consistency compared to established methodologies while allowing for simultaneous aneuploidy screening using the same biopsy and procedural optimization. Trial registration number not applicable

Enhancing Clinical Diagnosis With Convolutional Neural Networks: Developing High-Accuracy Deep Learning Models for Differentiating Thoracic Pathologies.

Background The use of computational technology in medicine has allowed for an increase in the accuracy of clinical diagnosis, reducing errors through additional layers of oversight. Artificial intelligence technologies present the potential to further augment and expedite the accuracy, quality, and efficiency at which diagnosis can be made when used as an adjunctive tool. Such techniques, if found to be accurate and reliable in their diagnostic acuity, can be implemented to foster better clinical decision-making, improving patient quality of care while reducing healthcare costs. Methodology This study implemented convolution neural networks to develop a deep learning model capable of differentiating normal chest X-rays from those indicating pneumonia, tuberculosis, cardiomegaly, and COVID-19. There were 3,063 normal chest X-rays, 3,098 pneumonia chest X-rays, 2,920 COVID-19 chest X-rays, 2,214 chest X-rays, and 554 tuberculosis chest X-rays from Kaggle that were used for training and validation.The model was trained to recognize patterns within the chest X-rays to efficiently recognize these diseases within patients to be treated on time. Results The results indicated a success rate of 98.34% incorrect detections, exemplifying a high degree of accuracy. There are limitations to this study. Training models require hundreds to thousands of samples, and due to potential variability in image scanning equipment and techniques from which the images are sourced, the model could have learned to interpret external noise and unintended details which can adversely impact accuracy. Conclusions Further studies that implement more universal database-sourced images with similar image scanning techniques, assess diverse but related medical conditions, and the utilization of repeat trials can help assess the reliability of the model.These results highlight the potential of machine learning algorithms for disease detection with chest X-rays.

Accuracy of clinical diagnosis of behavioural variant frontotemporal dementia: a protocol for a systematic review and meta-analysis

IntroductionBehavioural variant frontotemporal dementia (bvFTD) characterisation has evolved, but diagnosis remains challenging, relying on clinical diagnostic criteria that have undergone revisions over time. In this systematic review, our aims are...

Oncotree2vec - a method for embedding and clustering of tumor mutation trees.

Understanding the genomic heterogeneity of tumors is an important task in computational oncology, especially in the context of finding personalized treatments based on the genetic profile of each patient's tumor. Tumor clustering that takes into account the temporal order of genetic events, as represented by tumor mutation trees, is a powerful approach for grouping together patients with genetically and evolutionarily similar tumors and can provide insights into discovering tumor subtypes, for more accurate clinical diagnosis and prognosis. Here, we propose oncotree2vec, a method for clustering tumor mutation trees by learning vector representations of mutation trees that capture the different relationships between subclones in an unsupervised manner. Learning low-dimensional tree embeddings facilitates the visualization of relations between trees in large cohorts and can be used for downstream analyses, such as deep learning approaches for single-cell multi-omics data integration. We assessed the performance and the usefulness of our method in three simulation studies and on two real datasets: a cohort of 43 trees from six cancer types with different branching patterns corresponding to different modes of spatial tumor evolution and a cohort of 123 AML mutation trees. https://github.com/cbg-ethz/oncotree2vec.

An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer's disease.

Are invasive postmortem examinations still the &amp;lsquo;gold standard&amp;rsquo; in diagnosing the cause of death?

For many years, the foundations of both its art and science in medicine grew from the observations made at the autopsy table. The observations of the pathologists formed the pathologic basis and manifestations of disease, which further advanced the development of therapeutics. For most of the 18th and 19th centuries, morbid anatomy (autopsy pathology) was considered the science of medicine. Today, the autopsy and the knowledge that derives from it do not hold the position they once did in the profession’s history. There is a belief among the medical community a complete autopsy is necessary to find out the answers when someone dies. In some jurisdictions, the forensic pathology community is reluctant to follow the targeted or minimally invasive approach in postmortem examinations due to a lack of understanding of the process. With a complete external examination, trace evidence collection, total body computed tomography (CT) scan, and minimally invasive/targeted dissection with a collection of samples for further analysis, almost all answers can be provided in hospital and medicolegal autopsy settings. We must collaborate with sister disciplines, such as experts in radiology and the legal community, to educate them on the importance of this new approach using advanced technology.Continuously evolving modern radiological imaging has remarkably increased the accuracy of clinical diagnosis. Conventionally, when you hear the word postmortem examination, people think it includes an external examination and dissection of all body cavities. In the 21st century, most questions raised by the family, clinicians, the coroner (medical examiner), courts, and law enforcement agencies can be answered with a complete external examination of the body, total body CT scan +/- magnetic resonance imaging, and targeted dissection or minimally invasive approach incorporated with sample collection for further testing. This is cost-effective and can produce reviewable data.