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Gastroesophageal reflux disease in children, which affects approximately 18% of infants, results from gastric contents flowing into the esophagus due to factors such as immature sphincter function. Regurgitation is normal and often resolves by age two; however, persistent gastroesophageal reflux disease can lead to otolaryngological lesions such as subglottic stenosis. PubMed, AIRE, Scholar, MEDLINE, Springer Nature Journal, and Scopus were searched from their inception to February 2024. Pediatric patients < 18 years diagnosed with subglottic stenosis and investigated for gastroesophageal reflux disease or related endoscopic findings were included. Studies not reporting relevant outcomes, duplicates, and non-English studies were excluded. Six studies conducted between 1990 and 2001 explored gastroesophageal reflux-associated otolaryngological issues among pediatrics. Among the 149 participants with subglottic stenosis, 53.47% had laryngopharyngeal reflux disease. Some studies used the Cotton–Myer classification to identify intubation and gastroesophageal reflux disease as primary causes. Younger age and difficulty with intubation were associated with subglottic stenosis severity. Early management of gastroesophageal reflux disease before surgical intervention improved outcomes and reduced endoscopic repair failure rates. A meta-analysis of risk ratios from three studies underscored the relationship's statistical significance, with an overall effect size of 0.03 (95%CI: 0.01 to 0.17) and a P-value <.0001. Heterogeneity analysis showed minimal variability across studies, supporting the observed association between gastroesophageal reflux disease and subglottic stenosis. The prevalence of gastroesophageal reflux disease in pediatric subglottic stenosis cases has been established, stressing the need for early diagnosis and treatment to minimize the necessity of surgery. [ABSTRACT FROM AUTHOR]

In recent decades, cranial and cervical vascular disorders have become major global health concerns, significantly impacting patients, families, and societies. Headache is a prevalent symptom of these vascular diseases and can often be the initial, primary, or sole manifestation. The intricate relationship between headaches and cranial/cervical vascular disorders poses a diagnostic and therapeutic challenge, with the underlying mechanisms remaining largely elusive. Understanding this association is crucial for the early diagnosis, prevention, and intervention of such conditions. This review aims to provide a comprehensive overview of the clinical features and potential pathogenesis of headaches attributed to cranial and cervical vascular disorders and provide a reference for disease management and a basis for potential pathological mechanisms. [ABSTRACT FROM AUTHOR]

Introduction: Adhesive capsulitis (AC), often referred to as frozen shoulder, presents a diagnostic challenge due to its insidious onset and progressive nature. The condition is characterized by pain and restricted motion in the shoulder, with a predilection for individuals between 40 and 60 years of age. A novel scoring system was developed to enhance the accuracy of diagnosing AC and distinguishing between its stages, aiming to streamline clinical decision-making and treatment planning. Methods: A cohort of patients with symptoms suggestive of AC was assessed using the new scoring system, which integrates clinical, radiological, and patient history factors. Parameters included comorbidities like diabetes mellitus, recent immobility, rotator cuff tears, and specific ultrasound findings. Patients were scored and categorized into definitive AC, uncertain diagnosis, or exclusion from AC, with scores > 7, 6–2, and < 2, respectively. Results: The scoring system effectively categorized patients, with those scoring > 7 demonstrating pronounced symptoms and ultrasound changes consistent with Phase 2 AC. Patients with scores between 6 and 2 were classified into uncertain Phase 1 or Phase 3, necessitating further observation. Scores < 2 effectively excluded AC, indicating a need to explore alternative diagnoses. Conclusion: The structured scoring system demonstrated potential as a comprehensive tool for diagnosing AC. By quantitatively assessing a range of contributory factors, it allowed for the stratification of the disease into distinct stages. This system is anticipated to improve early diagnosis and the precision of treatment interventions, although further validation in larger cohorts is warranted. Level of evidence: II-III. [ABSTRACT FROM AUTHOR]

Recognizing and analyzing medical images is crucial for disease early detection and treatment planning with appropriate treatment options based on the patient's individual needs and disease history. Deep learning technologies are widely used in the field of healthcare because they can analyze images rapidly and precisely. However, because each object on the image has the potential to hold illness information in medical images, it is critical to analyze the images with minimal information loss. In this context, Capsule Network (CapsNet) architecture is an important approach that aims to reduce information loss by storing the location and properties of objects in images as capsules. However, because CapsNet maintains information on each object in the image, the existence of several objects in complicated images can impair CapsNet's performance. This work proposes a new model called HMedCaps to improve the performance of CapsNet. In the proposed model, it is aimed to develop a deeper and hybrid structure by using Residual Block and FractalNet module together in the feature extraction layer. While it is aimed to obtain rich feature maps by increasing the number of features extracted by deepening the network, it is aimed to prevent the vanishing gradient problem that may occur in the network with increasing depth with these modules with skip connections. Furthermore, a new squash function is proposed to make distinctive capsules more prominent by customizing capsule activation. The CIFAR10 dataset of complex images, RFMiD dataset of retinal images, and Blood Cell Count Dataset dataset of blood cell images were used to evaluate the study. When the proposed model was compared with the basic CapsNet and studies in the literature, it was observed that the performance in complex images was improved and more accurate classification results were obtained in the field of medical image analysis. The proposed hybrid HMedCaps architecture has the potential to make more accurate diagnoses in the field of medical image analysis. [ABSTRACT FROM AUTHOR]

Living cells can rapidly adjust their metabolic activities in response to external stimuli, leading to fluctuations in intracellular temperature and reactive oxygen species (ROS) levels. Monitoring these parameters is essential for understanding cellular metabolism, particularly during dynamic biological processes. In this study, we present a bifunctional nanoprobe capable of simultaneous measurement of ROS levels and temperature within single cells. The nanoprobe features two individually addressable nanoelectrodes, with platinum (Pt) and nickel (Ni) coatings on both sides. At the tip, these two metal layers form a nano-thermocouple, enabling precise intracellular temperature measurements, while the Pt layer facilitates selective ROS detection. This dual functionality allows for real-time monitoring of cellular responses during synergistic chemo-photothermal therapy of cancer cells and zebrafish embryos subjected to mitochondrial toxic stress. Our results demonstrate that the nanoprobe effectively measures increases in temperature and ROS levels in HeLa cells undergoing chemo-photothermal therapy, as well as in chemically stimulated zebrafish embryos. By providing detailed analysis of submicrometer-scale temperature and ROS variations within living cells, this nanoprobe offers valuable insights into cellular processes and holds promise for early disease detection and drug development. [ABSTRACT FROM AUTHOR]

Skin lesion segmentation and classification from dermoscopic images have emerged as pivotal research topics, playing vibrant role in early detection and diagnosis of skin diseases, including melanoma. Previous studies have employed various deep learning models for skin lesion segmentation and classification, enabling the automatic learning of complex and discriminative features from dermoscopic images. However, inherent challenges arise due to the variance in skin lesion shape, size, and contrast, leading to intrinsic limitations of former models, such as Isolated Representation Learning, Uniform Attention, Limited Model Generalization, Reduced Model Interpretability, and Uncertainty. To address these limitations and propel the field forward, this paper introduces a novel frameworkcalled AMTLUS that leverages Multi-Task Learning (MTL) in conjunction with deep Attention Mechanisms and Uncertainty Estimation. The integration of MTL facilitates joint training of segmentation and classification tasks, enabling shared representation learning and efficient utilization of data. Incorporating attention mechanisms dynamically focuses on informative regions within dermoscopic images, improving segmentation accuracy and feature extraction for classification. Uncertainty estimation techniques quantify model confidence, offering probabilistic interpretations for improved reliability and interpretability. Our widespread experiments conducted on the ISIC-2016 dataset demonstrate superior accuracy and reliability, showcasing the proposed model's capability to identify challenging cases. This deep learning framework represents a significant advancement in automated skin lesion analysis, enhancing early detection and diagnosis of skin diseases, including melanoma. [ABSTRACT FROM AUTHOR]

Reversing cancer cachexia remains a challenge. Genetic biomarkers for the early detection of the disease have been explored in order to enable the implementation of preventive measures. We therefore genotyped candidate genes based on cachexia phenotype and quantified adiponectin and GDF-15 levels in cachectic patients with gastrointestinal cancer. Patients with a diagnosis of gastrointestinal cancer were divided into a cachectic and a non-cachectic group after the start of chemotherapy. A control group (no cancer) was also included. We genotyped the following single nucleotide polymorphisms (SNPs) by quantitative PCR: FOXO3 (rs1935949), FOXO3 (rs4946935), ACVR2B (rs2268757), and SELP (rs6136). In addition, we quantified adiponectin and GDF-15 levels by ELISA. The rs2268757 SNP in the ACVR2B gene was associated with the weight loss phenotype in cachectic patients with gastrointestinal cancer (non-cachectic, P = 0.004). Plasma adiponectin levels were higher in cachectic patients compared to controls (P = 0.01) and non-cachectic patients (P = 0.004). GDF-15 was also elevated in cachectic patients compared to controls (P < 0.0001) and non-cachectic patients (P = 0.001). Analysis by sex showed elevated adiponectin levels in men (control, P = 0.01) and cachectic women (control, P = 0.04; non-cachectic, P = 0.01), as well as elevated GDF-15 levels in men (control, P = 0.002) and cachectic women (control, P = 0.002; non-cachectic, P = 0.007). However, there was no significant difference in the levels of these cytokines between cachectic men and women. The results suggest the rs2268757 SNP in the ACVR2B gene, adiponectin, and GDF-15 as potential biomarkers of cachexia in gastrointestinal cancer. [ABSTRACT FROM AUTHOR]

Recent advancements in hyperspectral imaging (HSI) for early disease detection have shown promising results, yet there is a lack of validated high-resolution (spatial and spectral) HSI data representing the responses of plants at different stages of leaf disease progression. To address these gaps, we used bacterial leaf spot (Xanthomonas perforans) of tomato as a model system. Hyperspectral images of tomato leaves, validated against in planta pathogen populations for seven consecutive days, were analyzed to reveal differences between infected and healthy leaves. Machine learning models were trained using leaf-level full spectra data, leaf-level Vegetation index (VI) data, and pixel-level full spectra data at four disease progression stages. The results suggest that HSI can detect disease on tomato leaves at pre-symptomatic stages and differentiate bacterial disease spots from abiotic leaf spots. Using VI data as features for machine learning improved overall classification performance by 26–37% compared to the direct use of raw data. Critical wavelength bands and VIs varied across disease progression stages, suggesting that pre-symptomatic disease detection relied more on changes in leaf water content (1400 nm) and plant defense hormone‐mediated responses (750 nm) rather than changes in leaf pigments or internal structure (800–900 nm), which may become more crucial during symptomatic stages. In conclusion, this study provides valuable insights into the dynamics of bacterial spot disease, revealing the potential benefits of leaf structure segmentation and VI group pattern analysis in HSI studies for the early detection of leaf diseases. [ABSTRACT FROM AUTHOR]

Purpose: Childhood central nervous system (CNS) tumors tend to have a longer time interval until diagnosis than other pediatric malignancies. The aim is to describe the time to diagnosis among Brazilian pediatric patients treated at a tertiary center and explore associated factors. Methods: Cross-sectional study; application of questionnaires to parents of children with CNS tumors during outpatient visit or inpatient care. Results: One hundred parents participated between August and November 2023. The median age of the children at diagnosis was 7.2 years old. Low-grade glioma (LGG) was the most common tumor type (37%), followed by medulloblastoma (24%). The most frequent symptoms were morning and/or persistent vomiting and headache. The mean prediagnostic symptomatic interval (PSI) was 150 days. The mean parental interval was shorter than the medical (58.1 days vs 92.8 days). LGGs and tumors located in the central area had longer intervals to diagnosis than other tumors (296 vs 54 days) (p = 0.005) and (206 vs 155 days) (p = 0.007), respectively. Despite 81% of the patients undergoing pediatric routine follow-up, 87% of them had been diagnosed at an emergency department. Children attended by the same physician had a shorter mean interval (18.2 vs 88.3 days) than those assisted by different professionals (p = 0.015). The mean time for referral to our specialized center was 23 days. Conclusions: This study is a crucial step in recognizing barriers to early diagnosis of CNS tumors in a middle-income country as low awareness of signs/symptoms by parents and health professionals, aiming to provide opportunities for intervention strategies to reduce the time to diagnosis. [ABSTRACT FROM AUTHOR]

A growing number of treatments for genitourinary diseases can result in various iatrogenic complications. Multimodality imaging in the post-procedural setting is essential for early and accurate diagnosis to limit morbidity and mortality. We review common and uncommon treatment-induced pathologies affecting the genitourinary system via a case-based approach. We illustrate notable complications affecting the kidneys, ureters, bladder, and urethra induced by percutaneous procedures, external beam radiation, immunotherapy, laparoscopic/robotic pelvic surgery, and intravesicular BCG. Finally, we provide guidance on optimal imaging techniques for diagnosis and highlight the role of image-guided interventions for mitigation of complications. [ABSTRACT FROM AUTHOR]

Background: This study aimed to develop a simple self-administered screening tool for odor memory, which allowed users to self-test their olfactory function repeatedly even at home. Methods: One hundred and ten participants were recruited (30 men, age = 50.1 ± 9.9 years; 80 women, age = 47.1 ± 11.5 years); half of them were heathy volunteers, the other half were patients with olfactory dysfunction. Fifty-one healthy participants volunteered for a retest within an interval of a maximum of 14 days. Olfactory function was assessed using the extended Sniffin' Sticks test (SST) comprising tests for odor threshold, identification, and discrimination. All participants received the Novel Olfactory Sorting Task (NOST) which is based on the sorting of 12 matching pairs of odors involving olfactory and cognitive functions. After that, all participants rated questions related to their test performance and the practicability of the test. Results: Consistent with the previous literature, significant effects of age were found. Results showed an acceptable test-retest reliability and a satisfactory validity of the NOST. The NOST score not only had positive correlations with SST, but also was capable of differentiating severe hyposmia/anosmia from normosmia by the score of 5.5 (sensitivity of 76.2%, specificity of 77.6%). Conclusion: The present study showed the good reliability, validity, and possible clinical usefulness of the NOST. As a self-performed screening test, it can be comprehended and conducted easily, which may provide a quick and simple approach to obtaining a global estimation of olfactory and cognitive functions. [ABSTRACT FROM AUTHOR]

Acne fulminans (AF) is a severe form of inflammatory acne commonly associated with adolescents. It is characterized by an abrupt onset of painful nodules and plaques and can progress to suppurative, ulcerative, and hemorrhagic lesions. AF can be associated with systemic symptoms such as fever, arthralgia, and bone pain. The etiology of AF is unknown but it has been linked to the use of certain medications and has been rarely found in autoinflammatory syndromes. In previous years, there have been reports of <200 cases in the literature; however, AF may be more common in clinical practice than reported. The most common presentation of AF is seen in adolescents starting isotretinoin therapy. Diagnosis of AF is determined based on its clinical findings. The main purpose of this article is to provide clinicians with a practical approach to treating AF. Current evidence for its treatment is limited to case reports and case series. The mainstay treatment of AF is a combination of prednisone and isotretinoin. It is important to taper or discontinue any exacerbating or precipitating medications such as isotretinoin, antibiotics, or androgens when AF is identified. Along with treatment of AF, it is important to treat associated scarring. Early identification and treatment of AF in adolescents is crucial to minimize both acute symptoms and long-term scarring, and further research is needed to determine optimal management. [ABSTRACT FROM AUTHOR]

Summary: Designing appropriate diagnostic and treatment methods to reduce fall risk and improve quality of life, as well as reduce the cost of care in elderlies. Our findings have potential for early diagnosis of those with a high probability of falling based on fairly simple clinical measures of hyperkyphosis, forward head, and lordosis. Introduction: Poor balance is an underlying cause of falling in the elderly, for which a change in the natural curvature of the spine plays a major role. Little is known about the relationship between spinal curvatures and fall incidence in this population. We primarily aimed to investigate the relationship between sagittal plane spinal curvatures and fall incidence over 1 year among nursing facility residents. Secondarily, we aim to determine associations of sagittal plane spinal curvatures with participants' perception of fall risk and balance capability. Methods: Participants (100 residents mean age 70.17 ± 6.01 years) underwent standing measures of sagittal plane spinal curvatures (flexible ruler technique) and forward displacement of the head relative to the cervical spine. The Tinetti Performance Oriented Mobility Assessment (POMA) and Fall Efficacy Scale assessed participants' perception of balance and fear of falling. Incident falls were self-reported monthly and tracked across 1 year. Spearman's correlations and logistic regression evaluated associations between fall incidence and spinal curvature. Predictive performance of spinal curvature and fall risk was determined by the corresponding ROC for defining a cut-off for variables of spinal curvature and fall risk indicators. Results: Predictive performance of spinal curvature and fall risk factors indicated 84% and 77% of participants were correctly classified using models of kyphosis and head angle, respectively. Conclusions: Our study adds new data on spinal curvatures and incident falls among nursing facility residents. Efforts are needed to intervene to counter progression of spinal curvatures and improve fall prevention practices. [ABSTRACT FROM AUTHOR]

Autism is increasingly viewed as an expression of neurodiversity deserving accommodation, rather than merely as a disorder in need of remediation or even prevention. This reconceptualization has inspired calls to broaden the ethical debate on early autism care beyond matters of efficient screenings and effective interventions. We conducted 14 in-depth interviews with 26 parents of infants at an increased likelihood for autism (siblings, preterms and children with persistent feeding difficulties) to understand which benefits and risks these parents see for the implementation of a systematic, early autism detection program in our region. With this study, we aim to contribute empirically to the ethical debate on good and just early autism care in the age of neurodiversity. Data were analyzed according to the QUAGOL-methodology. Three main themes emerged from our analysis. In their evaluation of early autism detection, parents discussed how a diagnosis helps gain a different perspective fostering understanding and recognition for both child and parent. Second, a diagnosis supports parents in adjusting their parenting practices, to justify this deviation from "normal" parenting and to strive for such adjusted environments beyond the nuclear family. Third, an autism diagnosis induces ambiguities parents need to navigate, involving questions on whether and when to mobilize the diagnostic label and which language to use to talk about autism. We discuss the complex position of parents of a (potentially) autistic child in terms of moving back and forth across the ab/normal binary and describe implications for the ethical debate on early autism detection. [ABSTRACT FROM AUTHOR]

Use of telehealth assessments for toddlers at increased likelihood of autism spectrum disorder (ASD) began prior to the global COVID-19 pandemic; however, the value of telehealth assessments as an alternative to in-person assessment (IPA) became clearer during the pandemic. The Naturalistic Observation Diagnosis Assessment (NODA™), previously demonstrated as a valid and reliable tool to evaluate asynchronous behaviors for early diagnosis, was enhanced to add synchronous collection of behaviors to assist clinicians in making a differential diagnosis of ASD. This study was conducted to validate the information gathered through NODA-Enhanced (NODA-E™) as compared to a gold standard IPA. Forty-nine toddlers aged 16.0–32.1 months of age, recruited through community pediatric offices and a tertiary ASD clinic, participated in both NODA-E and IPA assessments. There was high agreement between the two assessment protocols for overall diagnosis (46 of 49 cases; 93.6%; κ =.878), specific diagnostic criteria for social communication and social interaction (SCI; range 95.9–98%; κ =.918—.959), and for two of four criteria specified for restricted and repetitive behaviors (RRB; range 87.8–98%; κ =.755 and.959). There was lower agreement for two subcategories of RRBs (range 65.3–67.3%; κ =.306 and.347). NODA-E is a tool that can assist clinicians in making reliable and valid early ASD diagnoses using both asynchronous and synchronous information gathered via telehealth and offers an additional tool within a clinician's assessment toolbox. [ABSTRACT FROM AUTHOR]

Liver cirrhosis is one of the most prevalent chronic liver disorders with high mortality. We aimed to explore changed gut microbiome and urine metabolome in compensatory liver cirrhosis (CLC) patients, thus providing novel diagnostic biomarkers for CLC. Forty fecal samples from healthy volunteers (control: 19) and CLC patients (patient: 21) were undertaken 16S rDNA sequencing. Chromatography-mass spectrometry was performed on 40 urine samples (20 controls and 20 patients). Microbiome and metabolome data were separately analyzed using corresponding bioinformatics approaches. The diagnostic model was constructed using the least absolute shrinkage and selection operator regression. The optimal diagnostic model was determined by five-fold cross-validation. Pearson correlation analysis was applied to clarify the relations among the diagnostic markers. 16S rDNA sequencing analyses showed changed overall alpha diversity and beta diversity in patient samples compared with those of controls. Similarly, we identified 841 changed metabolites. Pathway analysis revealed that the differential metabolites were mainly associated with pathways, such as tryptophan metabolism, purine metabolism, and steroid hormone biosynthesis. A 9-maker diagnostic model for CLC was determined, including 7 microorganisms and 2 metabolites. In this model, there were multiple correlations between microorganisms and metabolites. Subdoligranulum, Agathobacter, norank_f_Eubacterium_coprostanoligenes_group, Butyricicoccus, Lachnospiraceae_UCG_004, and L-2,3-Dihydrodipicolinate were elevated in CLC patients, whereas Blautia, Monoglobus, and 5-Acetamidovalerate were reduced. A novel diagnostic model for CLC was constructed and verified to be reliable, which provides new strategies for the diagnosis and treatment of CLC. [ABSTRACT FROM AUTHOR]

Introduction: The subarachnoid space in the brain contains crucial blood vessels and cerebrospinal fluid. Aneurysms in these vessels can lead to subarachnoid hemorrhage (SAH), a serious stroke subtype with high morbidity and mortality rates. SAH treatment includes procedures like coiling and clipping, but these are available only at comprehensive stroke centers (CSCs), necessitating urgent diagnosis and transfer to specialized facilities. Methods: This IRB-approved study was conducted by Polk County Fire Rescue (PCFR) in Florida. PCFR, serving an 850,000-person population, implemented a three-step SAH protocol. The protocol uses both Ottawa SAH criteria and recurring symptoms, such as new-onset seizures and high systolic blood pressure, that were identified by EMS. Acute management included administering labetalol, levetiracetam, and ondansetron. Results: Of 2175 stroke patients, 80 screened positive for SAH and were eligible for transfer. Patients had a median age of 66, and 33% had an initial systolic BP over 220 mmHg. The interfacility transfer rate dropped from 12.9 to 3.6% after implementing the protocol. Conclusion: The PCFR protocol's effectiveness suggests its potential for nationwide implementation. Early SAH recognition and prompt transfer to CSCs reduce complications and improve outcomes. Accurate field diagnosis by EMTs can prevent unnecessary transfers and enhance patient care. Future improvements may include portable diagnostic tools and enhanced EMT training to further improve SAH patients' pre-hospital care. [ABSTRACT FROM AUTHOR]

Early diagnosis of inborn errors of immunity (IEIs) has been shown to reduce mortality, morbidity, and healthcare costs. The need for early diagnosis has led to the development of computational tools that trigger earlier clinical suspicion by physicians. Primary care professionals serve as the first line for improving early diagnosis. To this end, a computer-based tool (based on extended Jeffrey Modell Foundation (JMF) Warning Signs) was developed to assist physicians with diagnosis decisions for IEIs in the primary care setting. Two expert-guided scoring systems (one pediatric, one adult) were developed. IEI warning signs were identified and a panel of 36 experts reached a consensus on which signs to include and how they should be weighted. The resulting scoring system was tested against a retrospective registry of patients with confirmed IEI using primary care EHRs. A pilot study to assess the feasibility of implementation in primary care was conducted. The scoring system includes 27 warning signs for pediatric patients and 24 for adults, adding additional clinically relevant criteria established by expert consensus to the JMF Warning Signs. Cytopenias, ≥ 2 systemic infections, recurrent fever and bronchiectasis were the leading warning signs in children, as bronchiectasis, autoimmune diseases, cytopenias, and > 3 pneumonias were in adults. The PIDCAP (Primary Immune Deficiency "Centre d'Atenció Primària" that stands for Primary Care Center in Catalan) tool was implemented in the primary care workstation in a pilot area. The expert-based approach has the potential to lessen under-reporting and minimize diagnostic delays of IEIs. It can be seamlessly integrated into clinical primary care workstations. [ABSTRACT FROM AUTHOR]

Introduction: Cardiac amyloidosis is a rarely reported and potentially fatal variant of the systemic disease. Its early diagnosis could potentially lead to significantly improved clinical outcomes. Case presentation: A 56-year-old female presented with dyspnea and palpitations. Her physical exam and non-invasive evaluation with cardiac magnetic resonance imaging (CMRI) revealed restrictive cardiomyopathy, and the bone marrow biopsy results showed systemic amyloidosis. Discussion: The diagnosis of cardiac amyloidosis is not always straightforward, and delay can cause the progression of the disease and an increased risk of morbidity and mortality. Electrocardiograms, echocardiograms, cardiac magnetic resonance imaging, and histopathologic evaluation are the main methods for diagnosing cardiac amyloidosis. The treatment consists of controlling heart failure symptoms and disease-modifying interventions, including medical and surgical therapeutic methods. Clinical learning point (conclusion): Cardiac involvement is the main cause of death in systemic amyloidosis. Early suspicion, diagnosis, and treatment are crucial in improving patients' survival. CMRI can play an essential role in the diagnosis of cardiac Amyloidosis. A graphical abstract is provided for visual summary. [ABSTRACT FROM AUTHOR]

Accurate and early disease prediction enables patients to plan and improve their quality of life in the future. Early detection of neurodegenerative diseases, such as Parkinson's disease, is a high priority and a significant challenge in which physicians must act quickly to diagnose and predict the risk of disease severity. Machine learning (ML) models combined with feature selection (FS) techniques can assist physicians in quickly diagnosing a disease. FS technique optimally subsets features to improve model performance and reduce the number of tests required for a patient, thereby speeding up diagnosis. This paper proposes an e-diagnosis approach based on ML-FS algorithms to detect Parkinson's disease using data obtained from Parkinson's Progression Markers Initiative (PPMI) Online study. Also, it can be considered patient-oriented research as it uses self-reported online collected data. The results of six FS techniques pre-applied to classification algorithms named logistic regression, random forest, support vector machine, CatBoost, extreme learning machine, and XGBoost are shown in this study. Chi-square, mutual information, and analysis of variance (ANOVA) filter-based FS methods, while sequential feature selection, Boruta, and recursive feature elimination are considered wrapper methods. The outcomes show that random forest when trained on features selected by the recursive feature elimination technique help to build an efficient and effective approach for detecting Parkinson's disease. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a global healthcare challenge lacking a simple and affordable detection method. We propose a novel deep learning framework, Eye-AD, to detect Early-onset Alzheimer's Disease (EOAD) and Mild Cognitive Impairment (MCI) using OCTA images of retinal microvasculature and choriocapillaris. Eye-AD employs a multilevel graph representation to analyze intra- and inter-instance relationships in retinal layers. Using 5751 OCTA images from 1671 participants in a multi-center study, our model demonstrated superior performance in EOAD (internal data: AUC = 0.9355, external data: AUC = 0.9007) and MCI detection (internal data: AUC = 0.8630, external data: AUC = 0.8037). Furthermore, we explored the associations between retinal structural biomarkers in OCTA images and EOAD/MCI, and the results align well with the conclusions drawn from our deep learning interpretability analysis. Our findings provide further evidence that retinal OCTA imaging, coupled with artificial intelligence, will serve as a rapid, noninvasive, and affordable dementia detection. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) for extracting structural information from structural magnetic resonance imaging (sMRI), combined with functional magnetic resonance imaging (fMRI) and neuropsychological features, has emerged as a pivotal tool for early diagnosis of Alzheimer's disease (AD). However, the fixed-size convolutional kernels in CNNs have limitations in capturing global features, reducing the effectiveness of AD diagnosis. We introduced a group self-calibrated coordinate attention network (GSCANet) designed for the precise diagnosis of AD using multimodal data, including encompassing Haralick texture features, functional connectivity, and neuropsychological scores. GSCANet utilizes a parallel group self-calibrated module to enhance original spatial features, expanding the field of view and embedding spatial data into channel information through a coordinate attention module, which ensures long-term contextual interaction. In a four-classification comparison (AD vs. early MCI (EMCI) vs. late MCI (LMCI) vs. normal control (NC)), GSCANet demonstrated an accuracy of 78.70%. For the three-classification comparison (AD vs. MCI vs. NC), it achieved an accuracy of 83.33%. Moreover, our method exhibited impressive accuracies in the AD vs. NC (92.81%) and EMCI vs. LMCI (84.67%) classifications. GSCANet improves classification performance at different stages of AD by employing group self-calibrated to expand features receptive field and integrating coordinated attention to facilitate significant interactions among channels and spaces. Providing insights into AD mechanisms and showcasing scalability for various disease predictions. [ABSTRACT FROM AUTHOR]

Abdominal aortic aneurysm (AAA) often remains undetected until rupture due to limited access to diagnostic ultrasound. This trial evaluated a deep learning (DL) algorithm to guide AAA screening by novice nurses with no prior ultrasonography experience. Ten nurses performed 15 scans each on patients over 65, assisted by a DL object detection algorithm, and compared against physician-performed scans. Ultrasound scan quality, assessed by three blinded expert physicians, was the primary outcome. Among 184 patients, DL-guided novices achieved adequate scan quality in 87.5% of cases, comparable to the 91.3% by physicians (p = 0.310). The DL model predicted AAA with an AUC of 0.975, 100% sensitivity, and 97.8% specificity, with a mean absolute error of 2.8 mm in predicting aortic width compared to physicians. This study demonstrates that DL-guided POCUS has the potential to democratize AAA screening, offering performance comparable to experienced physicians and improving early detection. [ABSTRACT FROM AUTHOR]

For any agricultural society, the well-being of the plants is crucial to achieve a greater yield. The health and vigor of plants play a pivotal role in shaping the ultimate outcome of crop production. There are, too many infections affecting the plants generate harm to diverse economies and communities. It can also result in significant environmental losses. To prevent such losses, it is easier to diagnose diseases correctly and promptly at an early stage of plant life. This research mainly focuses on Muskmelon leaf diseases. Muskmelon is a remunerative crop with a short life span of around 65 days. Any disease attack in this duration will affect the crop entirely which in turn leads to yield loss. Hence, there needs an early prediction system for predicting diseases. The primary goal of this research is to develop a prediction model based of Multi Class Capsule Network for early detection of disease and pest in plants. The performance indicators examined for classification of leaf diseases are Accuracy, Precision, Recall, F1 score and, Loss function. The performance of Multi – Class Capsule Network [MCCN] is compared with existing pre-trained models such as, AlexNet, ResNet, VGG16, VGG19, GoogleNet, and CapsuleNet. Experimental results indicated that the MCCN model performs with an accuracy of 97.30% which is better than the accuracy of other models under considerations. [ABSTRACT FROM AUTHOR]

Background: Acute flank pain is a common presentation in the Emergency Department (ED). Point-of-care ultrasound (PoCUS) has emerged as an invaluable tool in the evaluation of acute flank pain and provides real-time images resulting in the early identification of the underlying pathology. Case Report: Here we report a case of a young lady presented to the ED with a complaint of right flank pain followed by a syncopal episode. The diagnosis of ruptured angiomyolipoma was made using a PoCUS and an immediate consult to interventional radiology and urology was provided. Conclusion: This case highlights the importance of PoCUS in assessing patients presented to the ED with acute flank pain. Its incorporation into clinical practice promises to revolutionize the approach to renal emergencies, emphasizing its important role in delivering timely and effective care. [ABSTRACT FROM AUTHOR]

Heart sound auscultation plays a crucial role in the early diagnosis of cardiovascular diseases. In recent years, great achievements have been made in the automatic classification of heart sounds, but most methods are based on segmentation features and traditional classifiers and do not fully exploit existing deep networks. This paper proposes a cardiac audio classification method based on image expression of multidimensional features (CACIEMDF). First, a 102-dimensional feature vector is designed by combining the characteristics of heart sound data in the time domain, frequency domain and statistical domain. Based on the feature vector, a two-dimensional feature projection space is constructed by PCA dimensionality reduction and the convex hull algorithm, and 102 pairs of coordinate representations of the feature vector in the two-dimensional space are calculated. Each one-dimensional component of the feature vector corresponds to a pair of 2D coordinate representations. Finally, the one-dimensional feature component value and its divergence into categories are used to fill the three channels of a color image, and a Gaussian model is used to dye the image to enrich its content. The color image is sent to a deep network such as ResNet50 for classification. In this paper, three public heart sound datasets are fused, and experiments are conducted using the above methods. The results show that for the two-classification/five-classification task of heart sounds, the method in this paper can achieve a classification accuracy of 95.68%/94.53% when combined with the current deep network. [ABSTRACT FROM AUTHOR]

Background: Pulmonary pseudoaneurysm (PAP) is a fatal complication of severe acute respiratory syndrome caused by coronavirus 2 infections in the lungs. Other causative agent such as fungal and tubercular also responsible for this entity. Early detection and treatment can save a patient's life. Case presentation: Here we describe a case of a left pulmonary artery pseudo aneurysm in a 54-year-old patient with COVID-19 pneumonia who presented with massive hemoptysis and which was treated with endovascular embolization. Conclusion: It is caused by vasculopathy due to immunosuppression /immune dysregulation in COVID-19. Minimally invasive endovascular embolization of pseudoaneurysm carries less mortality and morbidity. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis (RA) is an autoimmune disease featured by chronic synovitis and progressive joint damage. Early treatment before the onset of clinical symptoms (also known as the pre-RA stage) may slow or stop the progression of the disease. We sought to discover the dynamic metabolic changes during the evolution of collagen-induced arthritis (CIA) to better characterize the disease stages. Untargeted metabolomics analysis using gas chromatography–mass spectrometry revealed that the metabolic profiles of CIA mice gradually differed from that of the control group with the progression of the disease. During the induction phase, the CIA group showed some metabolic alterations in galactose metabolism, arginine biosynthesis, tricarboxylic acid cycle (TCA cycle), pyruvate metabolism, and starch/sucrose metabolism. During the early inflammatory phase, no joint swelling was observed in CIA mice, and metabolites changed mainly involving amino acid metabolism (arginine biosynthesis, arginine/proline metabolism, phenylalanine/tyrosine/tryptophan biosynthesis), and glutathione metabolism. During the peak inflammatory phase, severe arthritis symptoms were observed in CIA mice, and there were more extensive metabolic alterations in valine/leucine/isoleucine biosynthesis, phenylalanine/tyrosine/tryptophan biosynthesis, TCA cycle, galactose metabolism, and arginine biosynthesis. Moreover, the reduction of specific amino acids, such as glycine, serine, and proline, during the early stages may result in an imbalance in macrophage polarization and enhance the inflammatory response in CIA mice. Our study confirmed that specific perturbations in amino acid metabolism have occurred in CIA mice prior to the onset of joint symptoms, which may be related to autoimmune disorders. The findings could provide insights into the metabolic mechanism and the diagnosis of pre-RA. [ABSTRACT FROM AUTHOR]

There is a renewed interest on eye movements analysis and retinal alterations in Parkinson's disease. This may identify markers for at-risk subpopulation, early diagnosis and evolutive profiles for research or personalized medicine. [ABSTRACT FROM AUTHOR]

Purpose: Early-stage spondylolysis (ESS) is a common cause of acute low back pain (LBP) in adolescents. When treating patients with ESS, early diagnosis is essential, yet difficult without magnetic resonance imaging. This study evaluates a self-reported questionnaire for detecting ESS. Methods: We evaluated a total of 69 adolescents (≤ 18 years old) with acute LBP (≤ 1 month) with plain radiography, magnetic resonance imaging and a questionnaire including the following parameters: exercise frequency per week, daily training time, the necessity for stopping sports activity, pain-producing situations, pain quality, pain response to hyperextension or hyperflexion, pain location, and pain extent. Patients with obvious pathological findings other than ESS (e.g., disk herniation or infection) were excluded. The correlation of each question and gender, regarding ESS was determined, to evaluate the value of the originally developed questionnaire. Results: 24 out of 69 patients were diagnosed with ESS (ESS group), with a mean age of 13.9 years old and 21 males and 3 females in the group. 45 patients had NS-LBP, with a mean age of 14.6 years old, and 28 males and 17 females in the group. Correlation analyses showed weak associations between ESS and higher frequency of exercise per week, longer training time per day, and pain-producing situations. There was also a moderate association between ESS and male gender. Conclusion: Exercise frequency, training time, pain-provoking situations, and gender could be important factors to detect ESS within this questionnaire. [ABSTRACT FROM AUTHOR]

Introduction: Inflammatory bowel disease (IBD)-related arthritis is recognized as the most prevalent extraintestinal manifestation (EIM) of IBD. The objective of this study was to determine the prevalence and characteristics of undiagnosed IBD-related arthritis and to compare two screening questionnaires, DETection of Arthritis in Inflammatory boweL diseases (DETAIL) and IBd Identification of Spondyloarthritis Questionnaire (IBIS-q), for early disease detection. Methods: Between April and October 2023, both the DETAIL and IBIS-q questionnaires were administered to consecutive IBD outpatients visiting the University Hospital of Udine, Italy. During routine gastroenterology evaluations, patients aged > 18 years with Crohn's disease (CD) or ulcerative colitis (UC) were requested to complete both questionnaires. Subsequently, all patients who completed the questionnaires underwent a blinded rheumatological evaluation within 2 weeks. Patients with a previous diagnosis of IBD-related SpA were then excluded. Results: Overall, 203 patients were enrolled, of whom 26 were excluded because of a prior diagnosis of inflammatory arthritis. Among the remaining 177 patients, 10/177 (5.6%) received a new diagnosis of IBD-related arthritis. The median duration of symptoms before diagnosis was 4 (IQR 1.8–10.5) months. Imaging-confirmed enthesitis was the predominant pattern in 8 out 10 cases (80%, with 8 out 8 lacking concomitant peripheral arthritis), axial involvement in 1 out 10 cases (10%), and peripheral arthritis in 1 out 10 cases (10%). The DETAIL questionnaire exhibited higher specificity, but lower sensitivity compared to the IBIS-q, with a sensitivity of 40.0% (12.2–73.8) and specificity of 84.4% (78.0–89.6) versus a sensitivity of 70.0% (34.8–93.3) and specificity of 74.3% (66.9–80.7). Both questionnaires performed less effectively than in other studies. Conclusion: This study highlights a significant proportion of undiagnosed IBD-related arthritis (5.6%). Enthesitis emerged as the predominant pattern of newly diagnosed arthritis in our cohort, likely due to the recent onset of symptoms. Our study underscores the importance of entheseal involvement in early IBD-related arthritis and the importance of incorporating entheseal involvement into screening questionnaires. [ABSTRACT FROM AUTHOR]

Introduction: Screening for islet-specific autoantibodies can identify individuals at risk for type 1 diabetes (T1D). Despite calls for increased nationwide autoantibody screening efforts, it is unclear how many individuals have participated in screening among people who may benefit from it. Moreover, knowledge and perceptions of autoantibody screening in real-world samples are not well understood. Methods: We surveyed a sample of individuals (aged 18+ years old) from T1D Exchange Registry with a personal or family history of T1D to assess their self-reported T1D autoantibody knowledge, experiences, and attitudes. Participants belonged to one of three groups: adults with T1D who had a biological child without T1D or future plans for a child (PWD); parents without T1D who had a biological child with T1D and one or more biological children without T1D (Caregivers); and first-degree adult children or siblings to a person with T1D (Relatives). Descriptive analyses (means, standard deviations, frequencies) are presented by participant groups. Results: A total of 510 participants enrolled in the study. Across groups, participants reported feeling a little to somewhat knowledgeable about autoantibody screening and positive perceptions of autoantibody screening in general. However, few participants had screened their child without T1D (PWDs, 21.94%; Caregivers, 46.30%) or themselves (Relatives, 19.23%). Among those who had screened, participants reported generally positive experiences. Among those who had not screened, many participants were "undecided" about autoantibody screening (PWD, 38.46%; Caregivers, 40.52%; Relatives, 44.44%). Influences reported for participants' decisions to screen, not screen, or their current indecision differed by group: PWDs (21.70%) and Caregivers (26.87%) most often reported self-initiated research as an influence and Relatives reported they had not previously considered screening (48.28%). Conclusion: Results highlight the need for more accessible information about screening, including real experiences from those who have screened. [ABSTRACT FROM AUTHOR]

Awareness of transthyretin amyloid cardiomyopathy (ATTR-CM) has increased over the years due to diagnostic and therapeutic developments. Timely initiation of novel disease-modifying treatments improves both morbidity and mortality, which underlines the necessity for a prompt diagnosis. Nevertheless, early diagnosis of ATTR-CM remains challenging. This is a retrospective observational cohort study of patients diagnosed with ATTR-CM. Between 2016 and 2023, 87 patients were diagnosed with cardiac amyloidosis of which 65 (75%) patients with ATTR-CM and 22 (25%) patients with light chain amyloidosis. This study included 65 ATTR-CM patients (mean age 77 ± 7 years; 86% male) of whom 59 (91%) with wild-type ATTR-CM (ATTRwt) and six (9%) with variant ATTR-CM. We observed a surge in ATTR-CM diagnoses from 3 patients/year (2016–2020) to 16 patients/year (2021–2023), driven by ATTRwt. Nevertheless, the interval between the onset of heart failure symptoms and ATTR-CM diagnosis has not changed significantly (2016–2020 27.3 months [18.6–62.4]; 2021–2023 30.0 months [8.6–57.2]; p = 0.546), driven by time to referral. Red flags for ATTR-CM preceded diagnosis by several years: left ventricular hypertrophy (79%, 5.8 years [3.3–7.0]) and carpal tunnel syndrome (49%, 6.8 years [2.3–12.1]). Despite the presence of typical red flags, symptom-to-diagnosis duration has remained similar driven by time to referral. Improved recognition of red flags for ATTR-CM could reduce the time to diagnosis and improve overall recognition. [ABSTRACT FROM AUTHOR]

Purpose: The current diagnostic methods for leptospirosis diagnosis are technically complex and expensive, with limited applicability to specialized laboratories. Furthermore, they lack diagnostic accuracy in the acute stage of the disease, which coincides with a period when antibiotics are highly effective. New simple and accurate tests are mandatory to decentralize and improve diagnosis. Here, we introduced a new lateral flow immunoassay (Lepto-LF) for human leptospirosis. Methods: We conducted a double-blinded assay using 104 serum samples from patients with confirmed or discarded diagnosis for leptospirosis. The diagnostic performance of Lepto-LF was estimated across different ranges of days from onset of symptoms (dpo), considering the diagnostic algorithm as reference standard. Additionally, it was compared with the screening methods enzyme-linked immunosorbent assay (IgM-ELISA) and the slide agglutination test using temperature-resistant antigen (SATR). Results: Lepto-LF exhibited perfect diagnostic performance with a Youden´s index J = 1 from 6 dpo in the acute phase. IgM-ELISA gave slightly lower accuracy with J = 0.91 and 95.5% of both sensitivity and specificity; while SATR showed a markedly inferior yield (J = 0.41, sensitivity = 95.5%, specificity = 45.5%). The performances remained consistent in the convalescence phase of the disease (> 10 dpo). Conclusion: Lepto-LF was found to be a reliable test for simple, rapid and early diagnosis of leptospirosis, resulting a promising tool for decentralizing leptospirosis diagnosis and enabling timely treatment of patients. In addition, Lepto-LF may be employed as confirmatory test, especially in remote areas and vulnerable contexts where the standard MAT is not available. [ABSTRACT FROM AUTHOR]

Women with a personal history of breast cancer (PHBC) are at an increased risk of either a local recurrence or a new primary breast cancer. Thus, surveillance is essential for the detection of recurrent disease at the earliest possible stage, allowing for prompt treatment, and potentially improving overall survival. Nowadays, mammography follow-up is the only surveillance imaging technique recommended by international guidelines. Nevertheless, sensitivity of mammography is lower after breast cancer treatment, particularly during the first 5 years, due to increased density or post-treatment changes. Contrast-enhanced breast imaging techniques, such as MRI or contrast-enhanced mammography (CEM), are very sensitive to detect malignant enhancement, especially in dense breasts. This Special Report will provide arguments in favor of and against breast cancer follow-up with MRI or CEM, in a debate style between experts in Breast Imaging. Finally, the scientific points of pros and cons arguments will be summarized to help objectively decide the best follow-up strategy for women with a personal history of breast cancer. Clinical relevance statement: A personalized approach to follow-up imaging after conservative breast cancer treatment could optimize patient outcomes, using mammography as a baseline for most patients, and MRI or CEM selectively in patients with higher risks for a recurrence. Key Points: • Women with a personal history of breast cancer are at an increased risk of either a local recurrence or a new primary breast cancer. • Breast cancer survivors may benefit from additional imaging with MRI/CEM, in case of increased risk of a second breast cancer, with dense breasts or a cancer diagnosis before age 50 years. • As survival after local recurrence seems to depend on the initial stage at diagnosis, imaging should be more focused on detecting tumors in the earliest stages. [ABSTRACT FROM AUTHOR]

Objective: To assess the value of material density (MD) images generated from a rapid kilovoltage-switching dual-energy CT (rsDECT) in early detection of peritoneal carcinomatosis (PC). Materials and methods: Thirty patients (60 ± 13 years; 24 women) with PC detected on multiple abdominal DECT scans were included. Four separate DECTs with varying findings of PC from each patient were used for qualitative/quantitative analysis, resulting in a total of 120 DECT scans (n = 30 × 4). Three radiologists independently reviewed DECT images (65 keV alone and 65 keV + MD) for diagnosis of PC (diagnostic confidence, lesion conspicuity, sharpness/delineation and image quality) using a 5-point Likert scale. Quantitative estimation of contrast-to-noise ratio (CNR) was done. Wilcoxon signed-rank test and Odds ratio calculation were used to compare between the two protocols. Inter-observer agreement was evaluated using Kappa coefficient analysis. P values < 0.05 were considered statistically significant. Results: 65 keV + MD images showed a slightly higher sensitivity (89%[95%CI:84,92]) for PC detection compared with 65 keV images alone without statistical significance (84%[95%CI:78,88], p = 0.11) with the experienced reader showing significant improvement (98%[95%CI:93,100] vs. 90%[95%CI:83,94], p = 0.02). On a per-patient basis, use of MD images allowed earlier diagnosis for PC in an additional 13–23% of patients. On sub-group analysis, earlier diagnosis of PC was particularly beneficial in patients with BMI ≤ 29.9 kg/m2. 65 keV + MD images showed higher diagnostic confidence, lesion conspicuity, and lesion sharpness for the experienced reader (p < 0.001). CNR was higher in MD images (1.7 ± 0.5) than 65 keV images (0.1 ± 0.02, p < 0.001). All readers showed moderate interobserver agreement for determining PC by both protocols (κ = 0.58 and κ = 0.47). Conclusion: MD images allow earlier and improved detection of PC with the degree of benefit varying based on reader experience and patient body habitus. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) has poor prognosis mostly due to the advanced stage at which disease is diagnosed. Early detection of disease at a resectable stage is, therefore, critical for improving outcomes of patients. Prior studies have demonstrated that pancreatic abnormalities may be detected on CT in up to 38% of CT studies 5 years before clinical diagnosis of PDAC. In this review, we highlight commonly missed signs of early PDAC on CT. Broadly, these commonly missed signs consist of small isoattenuating PDAC without contour deformity, isolated pancreatic duct dilatation and cutoff, focal pancreatic enhancement and focal parenchymal atrophy, pancreatitis with underlying PDAC, and vascular encasement. Through providing commentary on demonstrative examples of these signs, we demonstrate how to reduce the risk of missing or misinterpreting radiological features of early PDAC. [ABSTRACT FROM AUTHOR]

Background: Detection of precachexia is important for the prevention and treatment of cachexia. However, how to identify precachexia is still a challenge. Objective: This study aimed to detect cancer precachexia using a simple method and distinguish the different characteristics of precachexia and cachexia. Methods: We included 3896 participants in this study. We used all baseline characteristics as input variables and trained machine learning (ML) models to calculate the importance of the variables. After filtering the variables based on their importance, the models were retrained. The best model was selected based on the receiver operating characteristic value. Subsequently, we used the same method and process to identify patients with precachexia in a noncachexia population using the same method and process. Results: Participants in this study included 2228 men (57.2%) and 1668 women (42.8%), of whom 471 were diagnosed with precachexia, 1178 with cachexia, and the remainder with noncachexia. The most important characteristics of cachexia were eating changes, arm circumference, high-density lipoprotein (HDL) level, and C-reactive protein albumin ratio (CAR). The most important features distinguishing precachexia were eating changes, serum creatinine, HDL, handgrip strength, and CAR. The two logistic regression models for screening for cachexia and diagnosing precachexia had the highest area under the curve values of 0.830 and 0.701, respectively. Calibration and decision curves showed that the models had good accuracy. Conclusion: We developed two models for identifying precachexia and cachexia, which will help clinicians detect and diagnose precachexia. [ABSTRACT FROM AUTHOR]

Recognition of leaf diseases in agriculture is considered a significant aspect of ensuring food quantity, quality, and production. In general, crop leaves are susceptible and fragile to various diseases such as leaf mold, target spot, late blight, bacterial spot or early blight of tomato plants. However, these tomato plant diseases are challenging to recognize, and early diagnosis is vital. At the same time, the continuous growth of convolutional neural network (CNN) approaches has significantly assisted plant disease diagnosis, providing a robust mechanism with highly accurate results. On the other hand, the number of unhealthy leaf images collected is often unbalanced, and diagnosing diseases with such an unbalanced data set is complicated. So, numerous models for tomato disease diagnosis based on CNN models have been proposed. However, none overcomes the class imbalance problem and, as a result, does not generate findings with impartial accuracy. This article presents an efficient and robust solution for the heterogeneous PYNQ-Z1 board. Optimization techniques-including loop unrolling, pipelining, array partitioning, and loop flattening-enhance the computation speed across the network's convolutional, fully connected, and max-pooling layers. The presented CNN approach comprises an 8-layer network termed MiniTomatoNet. This network is characterized by its streamlined structure, possessing only under 23 K parameters with all weights and biases and occupying a memory of 89.51 KB. In addition, the model trains with a re-weighted focal loss function and achieves 97.63% accuracy and 98.51% AUC score; the inference rate speed is 0.068 s per frame, and the power consumption is 2.35 W. Finally, the model is efficient, low power, robust, high accuracy and fast speed, making it a promising solution for diagnosing tomato diseases. [ABSTRACT FROM AUTHOR]

A prompt diagnosis of urinary tract infection (UTI) is necessary to minimize its symptoms and limit sequelae. The current UTI screening by urine test strip analysis and microscopic examination has suboptimal diagnostic accuracy. A definitive diagnosis of UTI by urine culture takes two to three days for the results. These limitations necessitate a need for better biomarkers for the diagnosis and subsequent management of UTI in children. Here, we review the value of currently available UTI biomarkers and highlight the potential of emerging biomarkers that can facilitate a more rapid and accurate UTI diagnosis. Of the newer UTI biomarkers, the most promising are blood procalcitonin (PCT) and urinary neutrophil gelatinase-associated lipocalin (NGAL). PCT can provide diagnostic benefits and should be considered in patients who have a blood test for other reasons. NGAL, which is on the threshold of clinical care, needs more research to address its scope and utilization, including point-of-care application. Employment of these and other biomarkers may ultimately improve UTI diagnosis, guide UTI therapy, reduce antibiotic use, and mitigate UTI complications. [ABSTRACT FROM AUTHOR]

Parkinson's disease is a chronic neurodegenerative condition accompanied by a variety of motor and non-motor clinical symptoms. Diagnosing Parkinson's disease presents many challenges, such as excessive reliance on subjective scale scores and a lack of objective indicators in the diagnostic process. Developing efficient and convenient methods to assist doctors in diagnosing Parkinson's disease is necessary. In this paper, we study the skeleton sequences obtained from gait videos of Parkinsonian patients for early detection of the disease. We designed a Transformer network based on feature tensor fusion to capture the subtle manifestations of Parkinson's disease. Initially, we fully utilized the distance information between joints, converting it into a multivariate time series classification task. We then built twin towers to discover dependencies within and across sequence channels. Finally, a tensor fusion layer was employed to integrate the features from both towers. In our experiments, our model demonstrated superior performance over the current state-of-the-art algorithm, achieving an 86.8% accuracy in distinguishing Parkinsonian patients from healthy individuals using the PD-Walk dataset. [ABSTRACT FROM AUTHOR]

Purpose: Sepsis has a high incidence and a poor prognosis. Early recognition is important to facilitate timely initiation of adequate care. Sepsis screening tools, such as the (quick) Sequential Organ Failure Assessment ((q)SOFA) and National Early Warning Score (NEWS), could help recognize sepsis. These tools have been validated in a general immunocompetent population, while their performance in immunocompromised patients, who are particularly at risk of sepsis development, remains unknown. Methods: This study is a post hoc analysis of a prospective observational study performed at the emergency department. Inclusion criteria were age ≥ 18 years with a suspected infection, while ≥ two qSOFA and/or SOFA criteria were used to classify patients as having suspected sepsis. The primary outcome was in-hospital mortality. Results: 1516 patients, of which 40.5% used one or more immunosuppressives, were included. NEWS had a higher prognostic accuracy as compared to qSOFA for predicting poor outcome among immunocompromised sepsis patients. Of all tested immunosuppressives, high-dose glucocorticoid therapy was associated with a threefold increased risk of both in-hospital and 28-day mortality. Conclusion: In contrast to NEWS, qSOFA underestimates the risk of adverse outcome in patients using high-dose glucocorticoids. As a clinical consequence, to adequately assess the severity of illness among immunocompromised patients, health care professionals should best use the NEWS. [ABSTRACT FROM AUTHOR]

Background: Within endemic regions in southern and eastern Germany, Borna disease virus 1 (BoDV-1) causes rare zoonotic spill-over infections in humans, leading to encephalitis with a high case-fatality risk. So far, intra-vitam diagnosis has mainly been based on RT-qPCR from cerebrospinal fluid (CSF) and serology, both being associated with diagnostic challenges. Whilst low RNA copy numbers in CSF limit the sensitivity of RT-qPCR from this material, seroconversion often occurs late during the course of the disease. Case presentation: Here, we report the new case of a 40 − 50 year-old patient in whom the detection of virus-specific T cells via ELISpot corroborated the diagnosis of BoDV-1 infection. The patient showed a typical course of the disease with prodromal symptoms like fever and headaches 2.5 weeks prior to hospital admission, required mechanical ventilation from day three after hospitalisation and remained in deep coma until death ten days after admission. Results: Infection was first detected by positive RT-qPCR from a CSF sample drawn four days after admission (viral load 890 copies/mL). A positive ELISpot result was obtained from peripheral blood collected on day seven, when virus-specific IgG antibodies were not detectable in serum, possibly due to previous immune adsorption for suspected autoimmune-mediated encephalitis. Conclusion: This case demonstrates that BoDV-1 ELISpot serves as additional diagnostic tool even in the first week after hospitalisation of patients with BoDV-1 encephalitis. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study is to determine the age-based prevalence of dens invaginatus cases and to learn the radiologic findings. In addition, fractal analysis of the periapical regions of unilateral dens invaginatus cases and contralateral teeth was performed to determine the effect of possible microleakage on fractal dimension. Methods: A total of 136 patients (170 teeth) identified in panoramic radiographs taken for diagnostic purposes between January 2018 and December 2023 at our Department of Oral and Maxillofacial Radiology were included in the study. Data were analysed using IBM SPSS V23. The Chi-square test was used for comparing categorical variables between groups. The relationship between the FD values calculated around the apexes of the teeth with unilateral dens invaginatus and the contralateral teeth was analyzed by paired t test. A significance level of p < 0.050 was adopted. Results: The mean age of the cases was calculated as 28.1 (8–63) years. 66 (48.5%) of the cases were female, and 70 (51.1%) were male. The lateral incisor was the most affected tooth among these cases. Bilateral cases were observed in 34 patients. Type 2 was detected in 93 patients, Type 1 in 35 patients, and Type 3 dens invagination in 8 patients. Dens invaginatus cases were observed in 134 maxillary and 2 mandibular teeth. Periapical lesions were seen in 22 of these cases. Conclusions: Dens invaginatus cases are a congenital anomaly encountered at any age. Early diagnosis is crucial to prevent the loss of pulp vitality due to these invaginations. It should be noted that these invaginations can affect both jaws. However, due to the prevalence of occurrence in the maxillary anterior teeth and asymptomatic nonvitality, particular attention should be paid to evaluating this region. [ABSTRACT FROM AUTHOR]

Purpose: Early diagnosis and immediate treatment of Cushing's syndrome (CS) are critical for a better prognosis but remain a challenge. However, few comprehensive reports have focused on this issue or investigated whether patient-reported manifestations are consistent with physician-assessed symptoms of CS. This study aimed to clarify the differences in patient-reported and physician-assessed manifestations of signs and symptoms of CS that prevent early diagnosis. Methods: This single-center retrospective study included 52 patients with CS (16 with Cushing's disease and 36 with adrenal CS). Upon clinical diagnosis, medical records were used to independently review the patient-reported and physician-assessed manifestations of typical (such as purple striae and proximal myopathy) and nonspecific features (such as hirsutism and hypertension). The correlations and differences between the patient-reported and physician-assessed manifestations were then analyzed. Results: We observed a positive correlation between the total number of manifestations of nonspecific features reported by patients and those assessed by physicians, but not for typical features. Moreover, manifestations reported by the patients were less frequent than those assessed by physicians for typical features, leading to discrepancies between the two groups. In contrast, there were no differences in most nonspecific features between the patient-reported and physician-assessed manifestations. Notably, the concordance between patient-reported and physician-assessed manifestations of typical features was not associated with urinary free cortisol levels. Conclusion: Regardless of disease severity, patients often do not complain of the typical features of CS that are crucial for formulating a diagnosis. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) encompasses diverse conditions such as congenital anomalies, glomerulonephritis, and hereditary nephropathies, necessitating individualized nutritional interventions. Early detection is pivotal due to the heightened risk of adverse outcomes, including compromised growth and increased healthcare costs. The nutritional assessment in pediatric CKD employs a comprehensive, multidisciplinary approach, considering disease-specific factors, growth metrics, and dietary habits. The prevalence of malnutrition, as identified through diverse tools and guidelines, underscores the necessity for regular and vigilant monitoring. Nutritional management strategies seek equilibrium in calorie intake, protein requirements, and electrolyte considerations. Maintaining a well-balanced nutritional intake is crucial for preventing systemic complications and preserving the remaining kidney function. The nuanced landscape of enteral nutrition, inclusive of gastrostomy placement, warrants consideration in scenarios requiring prolonged support, with an emphasis on minimizing risks for optimized outcomes. In conclusion, the ongoing challenge of managing nutrition in pediatric CKD necessitates continuous assessment and adaptation. This review underscores the significance of tailored dietary approaches, not only to foster growth and prevent complications but also to enhance the overall quality of life for children grappling with CKD. [ABSTRACT FROM AUTHOR]

Background: Attention deficit hyperactivity disorder (ADHD) is the most prevalent psychiatric disorder in children who experience delayed language development. This work aimed to study electroencephalographic (EEG) changes in children with ADHD and establish a strategy for prompt identification and suitable management in the future. Methods: This descriptive cross-sectional study was conducted on 100 children aged between 5 and 12 years old, both sexes, diagnosed with ADHD according to DSM-V Criteria, diagnosed as ADHD children with the Arabic version of Conner's Parent Rating Scale. Psychometric evaluation by intelligence quotient (IQ) using Stanford Binet Intelligence test. The Receptive Expressive Arabic Language Scale was used to assess language profile. Results: There was a significant negative correlation between the severity of ADHD and IQ scores. There was an insignificant negative correlation between ADHD and scaled receptive language score and scaled expressive language score. There was an insignificant positive correlation between ADHD and scaled expressive language score. There was a significant difference between EEG changes and combined subtype of ADHD and scaled score of receptive language (P < 0.05). There was an insignificant difference between (EEG changes and language development), the scaled score of expressive, total language, and the scaled score of language. Conclusions: EEG has clinical utility in the diagnosis of ADHD. The results of EEG analysis in children with ADHD have been quite important, and this novel methodology could offer supplementary factors to strengthen its diagnostic impact. [ABSTRACT FROM AUTHOR]

Circulating genetically abnormal cells (CACs) have emerged as a promising biomarker for the early diagnosis of lung cancer, particularly in patients with pulmonary nodules. However, their performance may be suboptimal in certain patient populations. This study aimed to refine patient selection to improve the detection of CACs in pulmonary nodules. A retrospective analysis was conducted on 241 patients with pulmonary nodules who had undergone pathological diagnosis through surgical tissue specimens. Utilizing consensus clustering analysis, the patients were categorized into three distinct clusters. Cluster 1 was characterized by older age, larger nodule size, and a higher prevalence of hypertension and diabetes. Notably, the diagnostic efficacy of CACs in Cluster 1 surpassed that of the overall patient population (AUC: 0.855 vs. 0.689, P = 0.044). Moreover, for Cluster 1, an integrated diagnostic model was developed, incorporating CACs, sex, maximum nodule type, and maximum nodule size, resulting in a further improved AUC of 0.925 (95% CI 0.846–1.000). In conclusion, our study demonstrates that CACs detection shows better diagnostic performance in aiding the differentiation between benign and malignant nodules in older patients with larger pulmonary nodules and comorbidities such as diabetes and hypertension. Further research and validation are needed to explore how to better integrate CACs detection into clinical practice. [ABSTRACT FROM AUTHOR]

Objective: This study aims to identify clinical laboratory parameters for the diagnosis of newly diagnosed multiple myeloma (NDMM), establish optimal cutoffs for early screening, and develop a diagnostic model for precise diagnosis. Methods: The study conducted a retrospective analysis of 279 NDMM patients and 553 healthy subjects at Zhejiang Province People's Hospital between January 2008 and June 2023. Multifactor LR was employed to explore clinical laboratory indicators with diagnostic value for NDMM, determine optimal cutoff values and contract a diagnostic model. The diagnostic efficacy and clinical utility were evaluated using receiver operating characteristic curves (ROC), sensitivity, specificity, and other indicators. Results: Multifactor analysis revealed that hemoglobin (Hb), albumin (Alb), and platelet distribution width (PDW) were significant diagnostic factors for NDMM. Optimal cutoff values for Hb, Alb, and PDW in MM diagnosis were determined, and the results showed a significant increase in the probability of NDMM diagnosis when Alb was below 39.3 g/L, Hb was below 11.6 g/dL, and PDW was below 14.1 fL. The diagnostic model constructed from the development cohort demonstrated a high area under the ROC curve of 0.960 (95% CI 0.942–0.978) and exhibited good sensitivity (0.860), specificity (0.957). The area under the curve (AUC) value of the diagnostic model in the external validation cohort was 0.979, confirming its good diagnostic efficacy and generalization. Conclusions: The optimal cutoff values for Hb, Alb, and PDW and the diagnostic model designed in the study provided good accuracy and sensitivity for the initial screening and diagnosis of NDMM. [ABSTRACT FROM AUTHOR]

One of the most severe cancers worldwide is Colorectal Cancer (CRC), which has the third-highest incidence of cancer cases and the second-highest rate of cancer mortality. Early diagnosis and treatment are receiving much attention globally due to the increasing incidence and death rates. Colonoscopy is acknowledged as the gold standard for screening CRC. Despite early screening, doctors miss approximately 25% of polyps during a colonoscopy examination because the diagnosis varies from expert to expert. After a few years, this missing polyp may develop into cancer. This study is focused on addressing such diagnostic challenges, aiming to minimize the risk of misdiagnosis and enhance the overall accuracy of diagnostic procedures. Thus, we propose an efficient deep learning method, DeepCPD, combining transformer architecture and Linear Multihead Self-Attention (LMSA) mechanism with data augmentation to classify colonoscopy images into two categories: polyp versus non-polyp and hyperplastic versus adenoma based on the dataset. The experiments are conducted on four benchmark datasets: PolypsSet, CP-CHILD-A, CP-CHILD-B, and Kvasir V2. The proposed model demonstrated superior performance compared to the existing state-of-the-art methods with an accuracy above 98.05%, precision above 97.71%, and recall above 98.10%. Notably, the model exhibited a training time improvement of over 1.2x across all datasets. The strong performance of the recall metric shows the ability of DeepCPD to detect polyps by minimizing the false negative rate. These results indicate that this model can be used effectively to create a diagnostic tool with computer assistance that can be highly helpful to clinicians during the diagnosing process. [ABSTRACT FROM AUTHOR]