Accuracy Of Clinical Diagnosis Research Articles

Improving the diagnostic strategy for thyroid nodules: a combination of artificial intelligence-based computer-aided diagnosis system and shear wave elastography.

Thyroid nodules are highly prevalent in the general population, posing a clinical challenge in accurately distinguishing between benign and malignant cases. This study aimed to investigate the diagnostic performance of different strategies, utilizing a combination of a computer-aided diagnosis system (AmCAD) and shear wave elastography (SWE) imaging, to effectively differentiate benign and malignant thyroid nodules in ultrasonography. A total of 126 thyroid nodules with pathological confirmation were prospectively included in this study. The AmCAD was utilized to analyze the ultrasound imaging characteristics of the nodules, while the SWE was employed to measure their stiffness in both transverse and longitudinal thyroid scans. Twelve diagnostic patterns were formed by combining AmCAD diagnosis and SWE values, including isolation, series, parallel, and integration. The diagnostic performance was assessed using the receiver operating characteristic curve and area under the curve (AUC). Sensitivity, specificity, accuracy, missed malignancy rate, and unnecessary biopsy rate were also determined. Various diagnostic schemes have shown specific advantages in terms of diagnostic performance. Overall, integrating AmCAD with SWE imaging in the transverse scan yielded the most favorable diagnostic performance, achieving an AUC of 72.2% (95% confidence interval (CI): 63.0-81.5%), outperforming other diagnostic schemes. Furthermore, in the subgroup analysis of nodules measuring <2 cm or 2-4 cm, the integrated scheme consistently exhibited promising diagnostic performance, with AUCs of 74.2% (95% CI: 61.9-86.4%) and 77.4% (95% CI: 59.4-95.3%) respectively, surpassing other diagnostic schemes. The integrated scheme also effectively addressed thyroid nodule management by reducing the missed malignancy rate to 9.5% and unnecessary biopsy rate to 22.2%. The integration of AmCAD and SWE imaging in the transverse thyroid scan significantly enhances the diagnostic performance for distinguishing benign and malignant thyroid nodules. This strategy offers clinicians the advantage of obtaining more accurate clinical diagnoses and making well-informed decisions regarding patient management.

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.

Real-world outcomes of a dedicated fast-track polymyalgia rheumatica clinic.

To examine the clinical impact of a fast-track PMR clinic to enable early diagnosis and treatment, and to define both patient and disease characteristics in newly diagnosed PMR. Primary care physicians were invited to refer patients with new PMR to our fast-track clinic. Referral criteria included new onset shoulder or pelvic girdle pain and/or stiffness with elevated inflammatory markers in patients over 50 years. All patients were seen within 72 h of referral. Patients with a rheumatology diagnosis of PMR had an ultrasound (US) of their temporal and axillary arteries. 172 patients were referred from primary care over 12 months. 39% of patients referred with suspected PMR had an alternative diagnosis for which PMR regimen glucocorticoids was inappropriate. 55% of the non-PMR diagnoses were other inflammatory rheumatological conditions requiring follow-up. Only 20% of patients referred from primary care already on glucocorticoids were commenced on bone protection. PMR patients were co-morbid with a mean of 2.5 other conditions. 75% of PMR patients experienced a glucocorticoid-related adverse event in the first 12 months of treatment. 16% of patients with new PMR had ultrasound features of subclinical giant cell arteritis. The commencement of glucocorticoid therapy should be deferred until after specialist evaluation to enable an accurate clinical diagnosis. A delay in treatment can only realistically be avoided if GPs have access to a Fast-track PMR clinic. We believe that rheumatologists should consider establishing fast-track PMR clinics and this study provides a strong case for and a template to support this practice innovation.

Diagnostic Enigma in a rare Case of Primary Neuritic Leprosy

Leprosy is one of the most prevalent diseases affecting peripheral nerves. Pure neuritic leprosy accounts for 4-10% of all leprosy cases, with clinical symptoms restricted to the peripheral nerves and no skin abnormalities. Leprosy is diagnosed based on the skin and nerves' typical clinical and histological involvement.The absence of typical dermatological characteristics significantly reduces clinical diagnosis accuracy, necessitating histological confirmation. We presented the case of a 26-year-old male with an 8-year history of growing numbness in his right leg, recurrent ulcers in his right foot, and hand abnormalities. The patient was subsequently identified with pure neural leprosy (PNL), a kind of leprosy that affects peripheral nerves but lacks conventional skin lesions. As a result, in leprosy-endemic countries such as Bangladesh, this kind of leprosy should be extensively explored, especially in patients with no skin abnormalities. Bangladesh J Medicine 2024; 35: 214-217

Observation of dynamic position and morphological changes of temporomandibular joint discs under Angle's classification

Objective: To observe the dynamic changes of the temporomandibular joint (TMJ) disc in joint movement under different Angle's classification, providing reference for understanding joint functional movement and providing a basis for more accurate clinical imaging diagnosis. Methods: A total of 30 patients (13 males and 17 females) with temporomandibular disorders who were admitted to Beijing Friendship Hospital, Capital Medical University and General Hospital of the People's Liberation Army from January 2022 to April 2024 were enrolled. Thirty adults (13 males and 17 females) with different Angle's classification, with an average age of (34.4±8.5) years, were subjected to dynamic imaging of their TMJ from the closed position to the maximum opening position, and then to the closed position using MRI. The position and morphological changes of the articular discs were observed. Results: The results showed that volunteers with no displacement of the articular disc in Class Ⅰ, Ⅱ, and Ⅲ relationships had different shapes of the articular disc during open and closed mouth movements. However, in the maximum opening position, the articular disc were all located directly below the maxillary nodules, and their shape is double concave. In terms of irreversible anterior displacement of the articular disc, in class Ⅰ Angle, the posterior zone of the disc contacts the anterior inclined plane of condyle from the maximum opening position back to the front of the closing position. In Class Ⅱ, the posterior zone of the disc contacts the anterior inclined plane of condyle from the beginning of opening position to maximum opening position. In Class Ⅲ, the posterior zone of the disc is always in contact with the anterior inclined plane of condyle throughout the entire movement process. And among them, the articular disc presents a forward displacement state at the closing position, its morphology undergoes folding phenomenon. When the openness is 2.5 cm, the articular disc moves up to a certain extent, and is closer to the anterior inclined plane of condyle, and its shape is also partially changed; When the openness is 4.3 cm, the shape of the articular disc, located between the anterior inclined plane of the joint node and the posterior inclined plane of the condyle, is typical double concave, which is sufficient to show that the articular disc is reversible when maximum opening position is reached. In terms of reversible anterior disc displacement, in class Ⅰ Angle, the posterior zone of the disc contact with the anterior inclined plane of condyle at the beginning of the opening position and the end of the closing position. In classⅡ Angle, the posterior zone of the articular disc is not in contact with the anterior inclined plane of condyle. In Class Ⅲ Angle, the posterior zone of the articular disc contact with the anterior inclined plane of condyle at the end of the closing position. Conclusions: Multi level dynamic MR imaging data of the temporomandibular joint can dynamically observe the movement of the temporomandibular joint, intuitively and accurately display the position and shape of the articular disc during movement, and can serve as a useful supplement to static conventional MR imaging of the TMJ. The patient's TMJ needs to reach the maximum opening position in order to determine whether the joint disc displacement can be reversible or not.

Deep learning-based binary classification of beta-amyloid plaques using 18F florapronol PET.

This study aimed to investigate a deep learning model to classify amyloid plaque deposition in the brain PET images of patients suspected of Alzheimer's disease. A retrospective study was conducted on patients who were suspected of having a mild cognitive impairment or dementia, and brain amyloid 18F florapronol PET/computed tomography images were obtained from 2019 to 2022. Brain PET images were visually assessed by two nuclear medicine specialists, who classified them as either positive or negative. Image rotation was applied for data augmentation. The dataset was split into training and testing sets at a ratio of 8 : 2. For the convolutional neural network (CNN) analysis, stratified k-fold (k = 5) cross-validation was applied using training set. Trained model was evaluated using testing set. A total of 175 patients were included in this study. The average age at the time of PET imaging was 70.4 ± 9.3 years and included 77 men and 98 women (44.0% and 56.0%, respectively). The visual assessment revealed positivity in 62 patients (35.4%) and negativity in 113 patients (64.6%). After stratified k-fold cross-validation, the CNN model showed an average accuracy of 0.917 ± 0.027. The model exhibited an accuracy of 0.914 and an area under the curve of 0.958 in the testing set. These findings affirm the model's high reliability in distinguishing between positive and negative cases. The study verifies the potential of the CNN model to classify amyloid positive and negative cases using brain PET images. This model may serve as a supplementary tool to enhance the accuracy of clinical diagnoses.

Identification of Bipolar Disorder and Schizophrenia Based on Brain CT and Deep Learning Methods.

With the increasing prevalence of mental illness, accurate clinical diagnosis of mental illness is crucial. Compared with MRI, CT has the advantages of wide application, low price, short scanning time, and high patient cooperation. This study aims to construct a deep learning (DL) model based on CT images to make identification of bipolar disorder (BD) and schizophrenia (SZ). A total of 506 patients (BD = 227, SZ = 279) and 179 healthy controls (HC) was collected from January 2022 to May 2023 at two hospitals, and divided into an internal training set and an internal validation set according to a ratio of 4:1. An additional 65 patients (BD = 35, SZ = 30) and 40 HC were recruited from different hospitals, and served as an external test set. All subjects accepted the conventional brain CT examination. The DenseMD model for identify BD and SZ using multiple instance learning was developed and compared with other classical DL models. The results showed that DenseMD performed excellently with an accuracy of 0.745 in the internal validation set, whereas the accuracy of the ResNet-18, ResNeXt-50, and DenseNet-121model was 0.672, 0.664, and 0.679, respectively. For the external test set, DenseMD again outperformed other models with an accuracy of 0.724; however, the accuracy of the ResNet-18, ResNeXt-50, and DenseNet-121model was 0.657, 0.638, and 0.676, respectively. Therefore, the potential of DL models for identification of BD and SZ based on brain CT images was established, and identification ability of the DenseMD model was better than other classical DL models.

Enhancing bone scan image quality: an improved self-supervised denoising approach.

Bone scans play an important role in skeletal lesion assessment, but gamma cameras exhibit challenges with low sensitivity and high noise levels. Deep learning (DL) has emerged as a promising solution to enhance image quality without increasing radiation exposure or scan time. However, existing self-supervised denoising methods, such as Noise2Noise (N2N), may introduce deviations from the clinical standard in bone scans. This study proposes an improved self-supervised denoising technique to minimize discrepancies between DL-based denoising and full scan images. &#xD;Retrospective analysis of 351 whole-body bone scan data sets was conducted. In this study, we used N2N and Noise2FullCount (N2F) denoising models, along with an interpolated version of N2N (iN2N). Denoising networks were separately trained for each reduced scan time from 5 to 50%, and also trained for mixed training datasets, which include all shortened scans. We performed quantitative analysis and clinical evaluation by nuclear medicine experts.&#xD;The denoising networks effectively generated images resembling full scans, with N2F revealing distinctive patterns for different scan times, N2N producing smooth textures with slight blurring, and iN2N closely mirroring full scan patterns. Quantitative analysis showed that denoising improved with longer input times and mixed count training outperformed fixed count training. Traditional denoising methods lagged behind DL-based denoising. N2N demonstrated limitations in long-scan images. Clinical evaluation favored N2N and iN2N in resolution, noise, blurriness, and findings, showcasing their potential for enhanced diagnostic performance in quarter-time scans.&#xD;The improved self-supervised denoising technique presented in this study offers a viable solution to enhance bone scan image quality, minimizing deviations from clinical standards. The method's effectiveness was demonstrated quantitatively and clinically, showing promise for quarter-time scans without compromising diagnostic performance. This approach holds potential for improving bone scan interpretations, aiding in more accurate clinical diagnoses.

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.

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.

Information sparsity guided transformer for multi-modal medical image super-resolution

Multi-modal medical image super-resolution (SR) plays a vital role in enhancing the resolution of medical images, providing more detailed visuals that aid in accurate clinical diagnosis. Recently, Transformer-based super-resolution methods have significantly promoted performance improvement in this field due to their capacity to capture global dependencies. They usually process all non-overlapping patches as tokens and densely sample these tokens without screening for calculating attention mechanisms. However, this strategy ignores the spatial sparsity of medical images, resulting in redundant or even detrimental computations on less informative regions. Hence, this paper proposes a novel sparsity-guided medical image SR network, namely SG-SRNet, by exploiting the spatial sparsity characteristics of the medical images. SG-SRNet mainly consists of two components: a sparsity mask (SM) generator for image sparsity estimation, and a sparsity-guided Transformer (SGTrans) for high-resolution image reconstruction. Specially, the SM generator generates a sparsity mask by minimizing our cross-sparsity loss which can respond to the informative positions. SGTrans first screens out the informative patches according to the sparsity mask. Then, SGTrans utilizes the designed cluster-based attention to only calculate attention between information-related tokens. We perform comprehensive experiments on three datasets to show that SG-SRNet brings significant performance enhancements with low computational complexity.

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.

Beyond Nyquist: A Comparative Analysis of 3D Deep Learning Models Enhancing MRI Resolution.

High-spatial resolution MRI produces abundant structural information, enabling highly accurate clinical diagnosis and image-guided therapeutics. However, the acquisition of high-spatial resolution MRI data typically can come at the expense of less spatial coverage, lower signal-to-noise ratio (SNR), and longer scan time due to physical, physiological and hardware limitations. In order to overcome these limitations, super-resolution MRI deep-learning-based techniques can be utilised. In this work, different state-of-the-art 3D convolution neural network models for super resolution (RRDB, SPSR, UNet, UNet-MSS and ShuffleUNet) were compared for the super-resolution task with the goal of finding the best model in terms of performance and robustness. The public IXI dataset (only structural images) was used. Data were artificially downsampled to obtain lower-resolution spatial MRIs (downsampling factor varying from 8 to 64). When assessing performance using the SSIM metric in the test set, all models performed well. In particular, regardless of the downsampling factor, the UNet consistently obtained the top results. On the other hand, the SPSR model consistently performed worse. In conclusion, UNet and UNet-MSS achieved overall top performances while RRDB performed relatively poorly compared to the other models.

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.

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.