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Objective: Oral cancer examinations seek to clinically identify early oral cancers and discover premalignancies prior to their progression to invasive cancer. Method and materials: A cross-sectional study was conducted between April and August 2017 to explore and quantify the diagnostic approach used by United States (US) general dental practitioners following discovery of an oral lesion suspicious for malignancy/premalignancy. US licensed general dental practitioners who were clinically active members of the US National Dental Practice-Based Research Network were eligible to participate. Data for analysis were obtained via two questionnaires. Results: The majority of participants were males, practiced full-time, had a suburban primary practice, were born during the 1950s, and graduated dental school in the 1980s or 2000s. After identifying an oral lesion suspicious for premalignancy/malignancy, the next action of most general dental practitioner respondents (65.4%) was to refer the patient for consultation/biopsy. Most general dental practitioners (87.2%) referred to an oral and maxillofacial surgeon; 22% of general dental practitioners reported personally biopsying suspicious lesions. There was a relatively weak association between completing an Advanced Education in General Dentistry or General Practice Residency and subsequently personally performing biopsies on patients with oral lesions suspicious for malignancy/premalignancy (OR 1.33, P = .06). Most written referrals took place electronically and often included information, including lesion location (87.0%), lesion sign/symptoms (85.3%), lesion history (83.9%), radiographs (59.3%), medical history (50.6%), dental history (48.8%), and photographs (42.4%). When a referral biopsy was performed, referring clinicians were most frequently informed of the findings via a written report (96.7%, when positive for malignancy/premalignancy, and 95.4% when negative). Conclusion: A snapshot is presented of current actions taken by US general dental practitioners following the discovery of oral abnormalities suspicious for premalignancy/malignancy. [ABSTRACT FROM AUTHOR]

Diagnosing Alzheimers disease (AD) poses significant challenges to health care, often resulting in delayed or inadequate patient care. The clinical integration of blood-based biomarkers (BBMs) for AD holds promise in enabling early detection of pathology and timely intervention. However, several critical considerations, such as the lack of consistent guidelines for assessing cognition, limited understanding of BBM test characteristics, insufficient evidence on BBM performance across diverse populations, and the ethical management of test results, must be addressed for widespread clinical implementation of BBMs in the United States. The Global CEO Initiative on Alzheimers Disease BBM Workgroup convened to address these challenges and provide recommendations that underscore the importance of evidence-based guidelines, improved training for health-care professionals, patient empowerment through informed decision making, and the necessity of community-based studies to understand BBM performance in real-world populations. Multi-stakeholder engagement is essential to implement these recommendations and ensure credible guidance and education are accessible to all stakeholders.

INTRODUCTION: Speech-based testing shows promise for sensitive and scalable objective screening for Alzheimers disease (AD), but research to date offers limited evidence of generalizability. METHODS: Data were taken from the AMYPRED (Amyloid Prediction in Early Stage Alzheimers Disease from Acoustic and Linguistic Patterns of Speech) studies (N = 101, N = 46 mild cognitive impairment [MCI]) and Alzheimers Disease Neuroimaging Initiative 4 (ADNI4) remote digital (N = 426, N = 58 self-reported MCI, mild AD or dementia) and in-clinic (N = 57, N = 13 MCI) cohorts, in which participants provided audio-recorded responses to automated remote story recall tasks in the Storyteller test battery. Text similarity, lexical, temporal, and acoustic speech feature sets were extracted. Models predicting early AD were developed in AMYPRED and tested out of sample in the demographically more diverse cohorts in ADNI4 (> 33% from historically underrepresented populations). RESULTS: Speech models generalized well to unseen data in ADNI4 remote and in-clinic cohorts. The best-performing models evaluated text-based metrics (text similarity, lexical features: area under the curve 0.71-0.84 across cohorts). DISCUSSION: Speech-based predictions of early AD from Storyteller generalize across diverse samples. HIGHLIGHTS: The Storyteller speech-based test is an objective digital prescreener for Alzheimers Disease Neuroimaging Initiative 4 (ADNI4). Speech-based models predictive of Alzheimers disease (AD) were developed in the AMYPRED (Amyloid Prediction in Early Stage Alzheimers Disease from Acoustic and Linguistic Patterns of Speech) sample (N = 101). Models were tested out of sample in ADNI4 in-clinic (N = 57) and remote (N = 426) cohorts. Models showed good generalization out of sample. Models evaluating text matching and lexical features were most predictive of early AD.

BackgroundThe Mnemonic Similarity Task (MST) is a popular memory task designed to assess hippocampal integrity. We assessed whether analyzing MST performance using a multinomial processing tree (MPT) cognitive model could detect individuals with elevated Alzheimer's disease (AD) biomarker status prior to cognitive decline.MethodWe analyzed MST data from >200 individuals (young, cognitively healthy older adults and individuals with mild cognitive impairment [MCI]), a subset of which also had existing cerebrospinal fluid (CSF) amyloid beta (Aβ) and phosphorylated tau (pTau) data using both traditional and model-derived approaches. We assessed how well each could predict age group, memory ability, MCI status, Aβ, and pTau status using receiver operating characteristic analyses.ResultsBoth approaches predicted age group membership equally, but MPT-derived metrics exceeded traditional metrics in all other comparisons.DiscussionA MPT model of the MST can detect individuals with AD prior to cognitive decline, making it a potentially useful tool for screening and monitoring older adults during the asymptomatic phase of AD.HighlightsThe MST, along with cognitive modeling, identifies individuals with memory deficits and cognitive impairment. Cognitive modeling of the MST identifies individuals with increased AD biomarkers prior to changes in cognitive function. The MST is a digital biomarker that identifies individuals at high risk of AD.

Objectives: Tooth wear, also referred to as tooth surface loss, occurs due to causes other than caries. This involves the loss of hard tissues, namely enamel, dentin, and/or cementum. Types of tooth wear/tooth surface loss are abrasion, abfraction, attrition, and erosion. These multifactorial conditions can be caused by factors such as lifestyle, diet, or even habits, and may not be symptomatic. The focus of this article is to provide clinicians with detailed examples and explanations of the causes of tooth loss to aid in early diagnosis. Through early identification, the preventive measures outlined can be implemented to avoid excessive tooth wear. The provision of a treatment flowchart and general treatment recommendations aims to help clinicians determine when to restore these lesions and the most appropriate treatment measures for the four types of wear. Method and materials: The PubMed (MEDLINE) search engine was used to gather information on teeth restricted to a 5-year period (26 August 2018 to 25 July 2022). Only English-language studies and reviews with the best balance of sensitivity and specificity were considered. A Boolean search of the PubMed dataset was implemented to combine a range of keywords: ("tooth wear" OR tooth attrition OR tooth erosion OR tooth abrasion OR tooth abfraction). Additional articles were selected through Google Scholar. Results: By this process, many articles and studies were obtained, and the 48 most relevant published studies were chosen and used in the current review. Conclusion: Tooth wear affects an increasing number of individuals and can have detrimental effects physically, mentally, and emotionally. It is important to stress early diagnosis and management of tooth wear through monitoring, prevention, and treatment where indicated. [ABSTRACT FROM AUTHOR]

Early-stage nonalcoholic fatty liver disease (NAFLD) is a silent condition, with most cases going undiagnosed, potentially progressing to liver cirrhosis and cancer. A non-invasive and cost-effective detection method for early-stage NAFLD detection is a public health priority but challenging. In this study, an adhesive, soft on-skin sensor with low electrode-skin contact impedance for early-stage NAFLD detection is fabricated. A method is developed to synthesize platinum nanoparticles and reduced graphene quantum dots onto the on-skin sensor to reduce electrode-skin contact impedance by increasing double-layer capacitance, thereby enhancing detection accuracy. Furthermore, an attention-based deep learning algorithm is introduced to differentiate impedance signals associated with early-stage NAFLD in high-fat-diet-fed low-density lipoprotein receptor knockout (Ldlr-/-) mice compared to healthy controls. The integration of an adhesive, soft on-skin sensor with low electrode-skin contact impedance and the attention-based deep learning algorithm significantly enhances the detection accuracy for early-stage NAFLD, achieving a rate above 97.5% with an area under the receiver operating characteristic curve (AUC) of 1.0. The findings present a non-invasive approach for early-stage NAFLD detection and display a strategy for improved early detection through on-skin electronics and deep learning.

Machine learning (ML)-driven diagnosis systems are particularly relevant in pediatrics given the well-documented impact of early-life health conditions on later-life outcomes. Yet, early identification of diseases and their subsequent impact on length of hospital stay for this age group has so far remained uncharacterized, likely because access to relevant health data is severely limited. Thanks to a confidential data use agreement with the California Department of Health Care Access and Information, we introduce Ped-BERT: a state-of-the-art deep learning model that accurately predicts the likelihood of 100+ conditions and the length of stay in a pediatric patients next medical visit. We link mother-specific pre- and postnatal period health information to pediatric patient hospital discharge and emergency room visits. Our data set comprises 513.9K mother-baby pairs and contains medical diagnosis codes, length of stay, as well as temporal and spatial pediatric patient characteristics, such as age and residency zip code at the time of visit. Following the popular bidirectional encoder representations from the transformers (BERT) approach, we pre-train Ped-BERT via the masked language modeling objective to learn embedding features for the diagnosis codes contained in our data. We then continue to fine-tune our model to accurately predict primary diagnosis outcomes and length of stay for a pediatric patients next visit, given the history of previous visits and, optionally, the mothers pre- and postnatal health information. We find that Ped-BERT generally outperforms contemporary and state-of-the-art classifiers when trained with minimum features. We also find that incorporating mother health attributes leads to significant improvements in model performance overall and across all patient subgroups in our data. Our most successful Ped-BERT model configuration achieves an area under the receiver operator curve (ROC AUC) of 0.927 and an average precision score (APS) of 0.408 for the diagnosis prediction task, and a ROC AUC of 0.855 and APS of 0.815 for the length of hospital stay task. Further, we examine Ped-BERTs fairness by determining whether prediction errors are evenly distributed across various subgroups of mother-baby demographics and health characteristics, or if certain subgroups exhibit a higher susceptibility to prediction errors.

BACKGROUND: A lack of high-quality provider education hinders the delivery of standard-of-care delirium detection and prevention practices in the intensive care unit (ICU). To fill this gap, we developed and validated an e-learning ICU Delirium Playbook consisting of eight videos and a 44-question knowledge assessment quiz. Given the increasing Spanish-speaking population worldwide, we translated and cross-culturally adapted the playbook from English into Spanish. OBJECTIVE: To translate and culturally adapt the ICU Delirium Playbook into Spanish, the second most common native language worldwide. METHODS: The translation and cross-cultural adaptation process included double forward and back translations and harmonization by a 14-person interdisciplinary team of ICU nurses and physicians, delirium experts, methodologists, medical interpreters, and bilingual professionals representing many Spanish-speaking global regions. After a preeducation quiz, a nurse focus group completed the playbook videos and posteducation quiz, followed by a semistructured interview. RESULTS: The ICU Delirium Playbook: Spanish Version maintained conceptual equivalence to the English version. Focus group participants posted mean (standard deviation) pre- and post-playbook scores of 63% (10%) and 78% (12%), with a 15% (11%) pre-post improvement (P = 0.01). Participants reported improved perceived competency in performing the Confusion Assessment Method for the ICU and provided positive feedback regarding the playbook. CONCLUSION: After translation and cultural adaptation, the ICU Delirium Playbook: Spanish Version yielded significant knowledge assessment improvements and positive feedback. The Spanish playbook is now available for public dissemination.

Breast cancer is the most common type of cancer that affect women and yet there is no substantive cure. On early diagnosis of breast lesions with this size using mammogram would help the affected tumor patients have a good survival rate. Detecting cancer cells is a challenging task and hence we propose a single valued neutrosophic approach for segmenting breast tumor lesions in mammogram images. In this neutrosophic approach, the intensity values are represented as truth, indeterminacy and falsity membership in where the indeterminacy are noise, and the breast regions are the true and falsity values. The computations of these memberships are then pre-processed using α-mean­ and ­β-enhancement to minimize the indeterminacy. Finally, gamma clustering techniques are used to localize and segment the tumor from the background breast tissue region. Experiments are conducted on publicly available datasets and the proposed method is evaluated and achieves a better segmentation performance. [ABSTRACT FROM AUTHOR]

Early diagnosis remains a major limitation of cancer outcomes with ethnicity and deprivation being determinants of inequalities that impact outcomes. Prostate cancer suffers from lower incidence rates and higher mortality rates in the most deprived versus the least deprived groups. We developed the 'Man Van' to enable high‐risk male patients' from deprived communities and ethnic minorities increased access to health care to address these health inequalities. Between December 2021 and December 2022 the Man Van project was piloted in eight different locations chosen using geospatial targeting based on ethnic minority populations and deprivation scores. The primary outcome measures were the prevalence of prostate cancer and other health conditions. 810 men were recruited to be seen at our Man Van clinics with 610 men attending. 48% of attendees were non‐White including 30% of men who were Black. 420 men had PSA tests performed with a median PSA of 1 μg/L. 15 prostate cancers were diagnosed (3.6%; 95% CI 2.0–5.9) with 10 of these being clinically significant disease. Black men were more likely to be diagnosed compared to white men: 7.1% versus 1.8% (p <.05). The Man Van project is a novel approach to tackling health inequalities combining awareness raising, improved access to healthcare as well as ease of follow‐up. Comparatively high levels of prostate cancers were diagnosed at early stages and high levels of other health conditions were found which could improve the economic value of the service. [ABSTRACT FROM AUTHOR]

A virus known as the Coronavirus 2019 (COVID-19) can have serious effects on a variety of body parts. Preventing this spread of the virus requires early detection and proper treatment. Many methods are followed for early detection of this disease based on symptoms and classification for proper treatment. In this paper, classification of COVID-19 using Computed Tomography (CT) scan images with hybrid optimisation-enabled deep learning is researched. Here, a hybrid optimisation algorithm called War Snake Optimisation Algorithm (WSOA) is proposed to train Deep Q-Network (Deep Q-Net). Moreover, the proposed WSOA is formed by integrating War strategy optimisation (WSO) and Snake Optimiser (SO). In this paper, pre-processing is carried out using a median filter, followed by lung lobe segmentation that is done by Generative Adversarial Network (GAN). Furthermore, image augmentation is done by translation, rotation, flipping and scaling. Then, feature extraction is followed after image augmentation, where needed features get extracted to end up with the classification of COVID-19. This classification brings out the patient's health condition of normal and abnormal situations. Moreover, this research work is analysed with three performance metrics, such as accuracy, sensitivity and specificity with values of 93.7%, 94.8% and 89.5%. [ABSTRACT FROM AUTHOR]

Bacterial canker of kiwifruit, caused by Pseudomonas syringae pv. actinidiae (Psa), is a highly destructive disease that results in significant economic losses. Timely detection of Psa is essential for implementing effective disease control measures. In this study, a recombinase‐aided amplification with lateral flow dipstick (RAA‐LFD) assay targeting the type III effector avirulence D1 (AvrD1) gene was developed. The assay was optimized for reaction conditions to ensure a swift and accurate detection protocol suitable for field application. In tandem, conventional PCR was also selected for comparison with the RAA‐LFD assay. The RAA‐LFD assay demonstrated a detection limit of 1 pg/μL for genomic DNA and 104 cfu/mL cell suspension. Specificity testing confirmed that the assay exclusively targeted Psa without cross‐reactivity with other endophytic bacteria present in kiwifruit. To validate the RAA‐LFD assay, it was compared with the conventional PCR method using 56 field samples. The results from both methods were found to be in complete agreement, validating the RAA‐LFD as a reliable diagnostic tool. Therefore, the established Psa RAA‐LFD assay provided technical support for the early and accurate diagnosis of bacterial canker in kiwifruit, which is crucial for disease management and prevention strategies. [ABSTRACT FROM AUTHOR]

Hepatocellular Carcinoma (HCC) is a malignant tumor with high morbidity and mortality worldwide, which represents a serious threat to human life, health and quality of life. Blood-based detection is essential for HCC screening, early diagnosis, prognosis evaluation, and surveillance. Current non-invasive detection strategy including serum alpha-fetoprotein (AFP), ultrasound, computerized tomography, and magnetic resonance imaging. The limited specificity of an AFP and the dependence on operator experience and diagnostic personnel for ultrasound have constrained their utility in early HCC diagnosis. In recent years, with the development of various detection technologies, there has been an increasing focus on exploring blood-based detection markers for HCC. The types of markers include protein markers, DNA mutation, DNA epigenetic modification, mRNA, miRNA, and so on. However, numerous methodological and biological factors limit the clinical sensitivity and generalization performance of these new biomarkers. In this review, we describe the state-of-the-art technologies for cfDNA analysis, and discuss outstanding biological and technical challenges that, if addressed, would substantially improve HCC diagnostics and patient care. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer‐related death worldwide and lacks biomarkers for personalized therapy. Herein, it is reported that MCB1 could be a novel oncofetal protein that is upregulated in the preneoplastic lesions and serum of early HCC patients. Functional studies reveal that MCB1 modulated p53 protein degradation to promote T‐IC generation and drive HCC initiation. Furthermore, the MCB1/p53 axis is shown to determine the responses of hepatoma cells to conventional chemotherapeutics and predict transcatheter arterial chemoembolization (TACE) benefits in patients. Importantly, MCB1 can mediate sorafenib/lenvatinib resistance by downregulating two essential drug targets fibroblast growth factor receptor 1 (FGFR1) and vascular endothelial growth factor receptor 3 (VEGFR3) expression in a proteasome‐dependent manner. Patient‐derived tumor organoids (PDOs), patient‐derived xenografts (PDXs), and patient cohorts analysis suggested that MCB1 levels in HCCs may determine the distinct responses to conventional therapeutics and targeted drugs. Furthermore, treatment of targeted drugs‐resistant HCC with adeno‐associated virus (AAV) targeting MCB1 or a proteasome inhibitor restores targeted drug response, suggesting their clinical significance in HCC combinational therapy. In conclusion, these findings demonstrate that MCB1 could act as a driver for HCC initiation, a contributor to drug resistance, and a biomarker for individualized HCC therapy. [ABSTRACT FROM AUTHOR]

In the United States, the burden of hepatitis C virus (HCV) infection is disproportionately high among young adults including pregnant persons, resulting in increased infections among children as perinatal transmission remains the main route of HCV infection in children. Hence, in 2020, the Centers for Disease Control and Prevention (CDC) recommended universal HCV screening during each pregnancy. HCV infection in infancy is usually asymptomatic, so the diagnosis entirely relies on testing of perinatally exposed infants which, historically, included anti-HCV antibody testing at ≥18 months of age. However, nation-wide perinatal HCV testing rates have been suboptimal with significant loss to follow-up. To address this problem, in 2023, the CDC introduced early single HCV RNA testing at 2–6 months of age with an alternative for HCV RNA testing up to 17 months of age if not previously tested. The high sensitivity and specificity of the HCV real-time PCR laid the grounds for this policy shift. In this review, we highlight how these new CDC recommendations will enhance testing of infants and children and ultimately contribute to overall HCV elimination efforts. We also emphasize the role of all pediatric providers and obstetricians in implementing these new guidelines. Additionally, we offer our perspective and practical advice for testing of perinatally exposed infants and children. Currently, curative oral antivirals for HCV-infection treatment are approved for children ≥3 years of age. As pediatricians, advocating for children's wellness, it is our utmost duty to ensure that every child exposed to perinatal hepatitis C has been tested, diagnosed, linked to care, treated, and achieved cure. [ABSTRACT FROM AUTHOR]

Adverse childhood experiences (ACEs) are common and can impact health across the life course. Thus, it is essential for professionals in child- and family-serving roles, including pediatric and adult primary care clinicians, to understand the health implications of childhood adversity and trauma and respond appropriately. Screening for ACEs in health care settings has received attention as a potential approach to ACEs identification and response. Careful examination of the existing evidence on ACEs screening and consideration, from a clinical and ethical perspective, of the potential benefits, challenges, and harms is critical to ensuring evidence-informed practice. In this critical appraisal, we synthesize existing systematic and scoping reviews on ACEs screening, summarize recent studies on the ability of ACEs to predict health outcomes at the individual level, and provide a comprehensive overview of potential benefits, challenges, and harms of ACEs screening. We identify gaps in the existing evidence base and specify directions for future research. We also describe trauma-informed, relational care as an orientation and perspective that can help pediatric and primary care clinicians to sensitively assess for and respond to ACEs and other potentially traumatic experiences. Overall, we do not yet have sufficient evidence regarding the potential benefits, challenges, and harms of ACEs screening in health care and other settings. In the absence of this evidence, we cannot assume that screening will not cause harm and that potential benefits outweigh potential harms. [ABSTRACT FROM AUTHOR]

BACKGROUND: Multisystem inflammatory syndrome (MIS-C) represents a diagnostic challenge because of its overlap with Kawasaki disease, Kawasaki disease shock syndrome, and toxic shock syndrome. Macrophage activation syndrome (MAS) is a frequently fatal complication of various pediatric inflammatory disorders and has been reported in MIS-C. Early diagnosis and prompt initiation by immune modulating therapies are essential for effectively managing MAS. METHODS: We conducted a retrospective study to determine the frequency, natural history, diagnostic metrics, treatment, and outcome of MAS in MIS-C within a large cohort of patients across 84 Latin American centers in 16 countries. We compared the clinical and laboratory characteristics between patients with and without MAS. RESULTS: Among 1238 patients with MIS-C, 212 (17.1%) fulfilled MAS criteria. Gastrointestinal and neurologic manifestations were more frequent in cases where MIS-C was complicated by MAS. Patients presenting with MIS-C complicated by MAS had a mortality rate of 12%, which was higher than those without it. Mortality was associated with MAS, seizures, arthritis, and shock. A ferritin or erythrocyte sedimentation rate ratio of >18.7 exhibited a sensitivity of 88.2% and a specificity of 75% in diagnosing MAS in MIS-C. CONCLUSIONS: MAS in MIS-C patients is associated with increased morbidity and mortality rates in the largest MIS-C Latin American cohort. Early recognition and appropriate management are crucial in improving patient outcomes and reducing mortality rates. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) mouse models are indispensable tools to understand the regulatory mechanisms of AD pathogenesis and to evaluate the potential therapeutic strategies in preclinical studies, although they rarely recapitulate the entire spectrum of AD pathological features. Most of the commonly used AD mouse models are developed by overexpressing genetic mutations implicated in familial AD, which accounts for less than 1% of all AD cases, and these models represent an extreme condition that would not occur in human patients with AD. On the basis of multiple humanized sporadic AD genetic risk factors, novel mouse models have increasingly been developed in an attempt to capture the trajectory and progression of AD more accurately. Despite numerous advantages, AD mouse models have their intrinsic limitations in translating preclinical findings to human clinical trials. Alzheimer's disease (AD) poses a significant health challenge worldwide, and the development of effective treatments necessitates a comprehensive understanding of its pathophysiology. Mouse models have been instrumental in offering insights into the crucial pathogenesis of AD. However, current models rarely recapitulate all aspects of AD pathology in patients; thus, translating the findings from mouse to human clinical trials has proved to be complex. In this review, we outline the development of some prevalently used AD mice, with a particular emphasis on the latest advances in newly generated models. In addition, we discuss the advantages and limitations in mouse models of AD and their applications in blood-based biomarkers. Finally, we speculate on potential future research directions. [ABSTRACT FROM AUTHOR]

Background: Patients with cardiogenic shock have a 25% to 50% mortality rate despite the introduction of mechanical circulatory devices and coordinated medical treatment. The use of shock teams has improved outcomes for these patients. Local Problem: A cardiovascular hospital with a multidisciplinary shock team had inconsistency and delays in recognition and diagnosis of patients with cardiogenic shock. Methods: A nurse-led, preintervention-postintervention quality improvement project was performed in April and May 2021 and in April and May 2022 within a cardiovascular hospital in north Texas. The 2 nursing staff interventions regarding shock team activation were education and shock alert implementation. Time from first signs to diagnosis (for inpatients) and time from initial transfer request to acceptance (for transferring patients) were measured. Descriptive and statistical analyses were conducted using R, version 4.0.0 (R Foundation for Statistical Computing). Results: The mean (SD) time to diagnosis of cardiogenic shock decreased significantly from 17.98 (28.39) hours in the preintervention group (n = 25) to 8.15 (12.26) hours in the postintervention group (n = 45; P =.045). For patients with cardiogenic shock transferring from referring hospitals, the median (IQR) time to acceptance was 1.55 (0.08-3.18) hours in the preintervention group and 0.35 (0.00-0.72) hours in the postintervention group (P <.001). Conclusions: Nursing staff interventions regarding shock team activation significantly improved the time of diagnosis and acceptance of patients with cardiogenic shock in a cardiovascular hospital, enhancing the overall quality of care provided to these patients. [ABSTRACT FROM AUTHOR]

Several major challenges, including an ageing population and declining workforce and the implementation of recent breakthrough therapies for Alzheimer disease, are prompting a necessary rethink of how people with neurodegenerative dementias are diagnosed and medically managed. Digital health technologies could play a pivotal part in this transformation, with new advances enabling the collection of millions of data points from a single individual. Possible applications include unobtrusive monitoring that aids early detection of disease and artificial intelligence-based health advice. To translate these advances to meaningful benefits for people living with a disease, technologies must be implemented within a system that retains the physician expert as a central figure in decision-making. This Perspective presents a new framework, termed the Digitized Memory Clinic, for the diagnostic pathway of neurodegenerative dementias that incorporates digital health technologies with currently available assessment tools, such as fluid and imaging biomarkers, in an interplay with the physician. The Digitized Memory Clinic will manage people across the entire disease spectrum, from the detection of risk factors for cognitive decline and the earliest symptoms to dementia, and will replace the present paradigm of a pure 'brick-and-mortar' memory clinic. Important ethical, legal and societal barriers associated with the implementation of digital health technologies in memory clinics need to be addressed. The envisioned Digitized Memory Clinic aims to improve diagnostics and enable precise disease-tracking prognostication for individuals with memory disorders and to open new possibilities, such as precision medicine for prevention and treatment. In this Perspective, the authors present the Digitized Memory Clinic, a new framework for the diagnostic pathway of neurodegenerative dementias that incorporates digital health technologies with currently available assessment tools, such as fluid and imaging biomarkers, in an interplay with the physician. [ABSTRACT FROM AUTHOR]

Sarcoidosis is a disease whose prevalence varies considerably worldwide. In Colombia, the Ministry of Health has strengthened the Comprehensive System of Social Protection Information (SISPRO) to collect data on the provision of health services at the national level, thus providing a source of information available for research. This study aims to estimate the prevalence of sarcoidosis using consultation records collected between January 2018 and December 31, 2022. Cross-sectional study. A descriptive cross-sectional study was conducted using the SISPRO database and the relevant International Classification of Diseases (ICD-10) codes for sarcoidosis (D860, D861, D862, D863, D868, D869, G532, M633). During the mentioned period, a total of 6828 patients were identified, of which 66.9 % were women (4571 patients). The prevalence of sarcoidosis was estimated at 12.8 cases per 100,000 inhabitants, being higher in the age group of 60–69 years. The information obtained from this study highlights the importance of continuing to monitor and evaluate the burden of sarcoidosis in the country, which can contribute to a better understanding of the disease's epidemiology and, in turn, to the planning of early diagnosis and timely treatment strategies. [ABSTRACT FROM AUTHOR]

Background: Encephalitis originates from diverse autoimmune and infectious etiologies. Diagnostic challenges arise due to the spectrum of presentation and the frequent absence of specific biomarkers. This study aimed to comprehensively characterize and differentiate autoimmune encephalitis (AE) from infectious encephalitis (IE) in adults, and disentangle clinical, paraclinical, and therapeutic differences. Methods: A cohort study spanning 10 years was conducted across three Austrian tertiary care hospitals. Inclusion criteria comprised adults with probable or definite encephalitis. Demographics, clinical features, technical findings, treatment modalities, and outcomes were collected from the electronic patient files. A follow-up was performed via telephone interviews and clinical visits. Results: Of 149 patients, 17% had AE, 73% IE, and 10% encephalitis of unknown etiology. Significant differences between AE and IE included the prevalence of acute symptomatic seizures (AE: 85% vs. IE: 20%, p < 0.001), fever (8% vs. 72%, p < 0.001), headache (15% vs. 61%, p < 0.001), and focal neurological deficits (56% vs. 23%, p = 0.004), respectively. Paraclinical differences comprised lower CSF pleocytosis in AE compared to IE (median 6 cells/µl vs. 125 cells/µl, p < 0.001). Epileptic discharges on EEG and MRI lesions were more prevalent in AE than IE (50% vs. 14%, p < 0.001; 50% vs. 28%, p = 0.037). The modified Rankin Scale scores at discharge and last follow-up (median duration 2304 days, IQR 1433–3274) indicated favorable outcomes in both groups. Conclusion: This comprehensive analysis provides insights into the epidemiology, clinical, paraclinical, and therapeutic aspects and the outcomes of AE and IE in adults. We developed a diagnostic tool that facilitates early differentiation between AE and IE, aiding in timely therapeutic decision-making. [ABSTRACT FROM AUTHOR]

Alport syndrome (AS) is one of the most common inherited kidney disorders, involving pathogenic variants of COL4A3, COL4A4 and COL4A5 genes that lead to disruption of the normal structure of collagen IV protein through improper chain or heterotrimer folding or degradation of heterotrimer components. Lipoprotein glomerulopathy (LPG) is an autosomal dominant disease involving APOE gene mutations disturbing lipoprotein metabolism. We report the first case with both AS and LPG in an 11‐year‐old girl. The patient presented with blepharedema, and decreased vision. Laboratory examinations showed hematemesis, proteinuria, hypoproteinemia, hyperlipidemia and progressive renal failure. Renal biopsy showed the changes of LPG and AS. Whole‐exome sequencing (WES) identified two pathogenic variants, c.127C > T in exon 3 of APOE gene, and c.930 + 1G > A in exon 15 of COL4A4 gene. We emphasize the importance of early completion of renal biopsy and WES for early diagnosis of LPG and AS. [ABSTRACT FROM AUTHOR]

In order to maintain sustainable agriculture, it is vital to monitor plant health. Since all species of plants are prone to characteristic diseases, it necessitates regular surveillance to search for any symptoms, which is utterly challenging and time-consuming. Besides, farmers may struggle to identify the type of plant disease and its potential symptoms. Hence, the interest in research like image-based computer-aided automated plant leaf disease detection by analyzing the early symptoms has increased enormously. However, limitations in the plant leaf image database, for instance, unfitting backgrounds, blurry images, and so on, sometimes cause underprivileged feature extraction, misclassification, and overfitting issues in existing models. As a result, we have proposed a real-time plant leaf disease detection architecture incorporating proposed PLD-Det model, which is based on improved YOLOv7 with the intention of assisting farmers while reducing the issues in existing models. The architecture has been trained on the widely used PlantVillage dataset, which resulted in an accuracy of 98.53%. Furthermore, SHapley Additive exPlanations (SHAP) values have been analyzed as a unified measure of feature significance. According to the experimental findings, the proposed PLD-Det model, which is an improved YOLOv7 architecture, outperformed the original YOLOv7 model in test accuracy by approximately 4%. [ABSTRACT FROM AUTHOR]

In the recent years, neuroimaging and deep learning have received notable scientific attention for the diagnosis of grade IV tumor de novo glioblastoma in the central nervous system. However, the scarce amount of neuroimaging data for training has resulted in significant overfitting issues for numerous deep learning models. To address these challenges, we propose the implementation of a meta-learning-based IRAM–NET model that utilizes the ResNet-50 as a deep learning-based model and incorporates the e-MAML ensemble technique from meta-learning for the early diagnosis of glioblastoma. The methodology developed was trained and validated using brain MRI images taken from numerous national and international cancer initiative data repositories. In the training phase, this study employed detailed procedures, including the handling of exceptions and the application of normalization techniques. These measures were implemented to guarantee precise data representation, mitigate the risk of overfitting, and enhance the proposed model's capacity for making meaningful generalizations. The proposed IRAM–NET model surpasses the most recent studies in accurately predicting glioblastoma diagnosis, achieving a training, testing and validation accuracy of 97.22%, 96.10%, and 94.74%, respectively. Overall, the research not only enhances the diagnosis of rare disorders like glioblastoma, but also promotes the wider inclusion of meta-learning in healthcare. This underlines the importance of adaptation and efficiency in situations with limited data availability. [ABSTRACT FROM AUTHOR]

Neurodegenerative disorders, including Alzheimer's disease (AD) and Parkinson's disease (PD), significantly affect cognitive function, particularly language, which is one of the first domains to affect disease progression. This study investigated linguistic marker-specific changes in language patterns as potential tools for early diagnosis of these disorders. Employing a mixed-methods approach allowed us to analyze speech samples from patients with AD and PD, identifying differences in lexical retrieval, syntactic complexity, speech prosody, and fluency. Our findings revealed distinct linguistic profiles for each disorder, suggesting that language analysis could serve as a noninvasive and cost-effective diagnostic tool. These results underscore the need for additional research to refine these markers and to explore their applicability in other neurodegenerative conditions. [ABSTRACT FROM AUTHOR]

This study explores vocal development as an early marker of autism, focusing on canonical babbling rate and onset, typically established by 7 months. Previous reports suggested delayed or reduced canonical babbling in infants later diagnosed with autism, but the story may be complicated. We present a prospective study on 44 infants later diagnosed with autism spectrum disorder compared with 127 infants later identified as typically developing who were followed longitudinally with day-long recordings from 0 to 13 months. Eight 5-min segments from each of their recordings were coded for canonical and noncanonical syllables. The results confirmed many reports that canonical babbling is a robust feature of human vocal development in the first year of life, with small overall mean differences in canonical babbling rates between the autism spectrum disorder and typically developing groups beginning around 9 months, primarily in males. Our findings highlight the importance of considering sex differences in vocal communication as part of the early detection and diagnosis of autism when determining the need for communication supports to maximize outcomes. Our study examined how babies develop their ability to talk to help identify early signs of autism. We looked at babies' production of babbling with mature syllables across the first year of life. Babies usually start producing mature babbling at 7 months of age before they say their first words. Some studies have suggested that babies who are later diagnosed with autism produce this kind of babbling less frequently in their first year of life, but other studies have shown complicated outcomes. In this new study, we followed 44 autistic babies and compared them to 127 typically developing babies. We recorded the babies once every month, all day long, from the time that they were born until they were around 13 months old. Then, we studied their mature babbling from segments of these recordings. We found that the rate at which babies used mature babbling was lower in boys with autism, and higher in girls with autism, compared to babies without autism. This research helps us understand how babies with autism learn to talk. It also raises important questions about differences between boys and girls with autism. Our study can help us improve how scientists and clinicians can identify autism earlier, which could lead to better communication supports for autistic children and their families. [ABSTRACT FROM AUTHOR]

Early detection of autism spectrum condition is crucial for children to maximally benefit from early intervention. The study examined a machine learning model predicting the increased likelihood for autism from wellness records from 0 to 24 months. The study included 591,989 non-autistic and 12,846 autistic children. A gradient boosting model with a threefold cross-validation and SHAPley additive explanation tool quantified feature importance. The model had an average area under the curve of 0.81 (SD = 0.004). The high-likelihood group detected by the model had a 0.073 autism spectrum condition incidence rate; 3.42-fold more than in the entire cohort (0.02). Sex-specific models had higher specificity (0.81 boys and 0.79 girls) than sensitivity (0.64 boys and 0.66 girls). The common predictors were more parental concerns, older mothers, never nursing, lower initial and higher last weight percentiles, and several delayed milestones. SHAPley additive explanation tool results show common, important predictors in the full sample and separate boys' and girls' models. These included birth, growth, familial, postnatal parameters and delayed language, fine motor, and social milestones from 12 to 24 months. Machine learning algorithms can help detect increased autism signs by relying on the multidimensional data routinely recorded during the first 2 years. Timely identification of autism spectrum conditions is a necessity to enable children to receive the most benefit from early interventions. Emerging technological advancements provide avenues for detecting subtle, early indicators of autism from routinely collected health information. This study tested a model that provides a likelihood score for autism diagnosis from baby wellness visit records collected during the first 2 years of life. It included records of 591,989 non-autistic children and 12,846 children with autism. The model identified two-thirds of the autism spectrum condition group (boys 63% and girls 66%). Sex-specific models had several predictive features in common. These included language development, fine motor skills, and social milestones from visits at 12–24 months, mother's age, and lower initial growth but higher last growth measurements. Parental concerns about development or hearing impairment were other predictors. The models differed in other growth measurements and birth parameters. These models can support the detection of early signs of autism in girls and boys by using information routinely recorded during the first 2 years of life. [ABSTRACT FROM AUTHOR]

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]

The availability and cost of germline and somatic genetic testing have dramatically improved over the past two decades, enabling precision medicine approaches in oncology, with significant implications for prostate cancer (PCa) care. Roughly 12% of individuals with advanced disease are carriers of rare pathogenic germline variants that predispose to particularly aggressive and earlier-onset disease. Several of these variants are already established as clinically actionable by modern precision oncology therapeutics, while others may come to aid the selection of active surveillance, definitive local therapies, and systemic therapies. Concurrently, the number of common variants (ie, incorporated into polygenic risk scores) associated with PCa risk has continued to grow, but with several important considerations both at the intersection of race and ancestry and for early detection of aggressive disease. Family history has historically been used as a proxy for this inherited genetic risk of PCa, but recently emerging evidence examining this relation has shifted our understanding of how best to leverage this tool in PCa care. This review seeks to clarify and contextualize the existing and emerging precision oncology paradigms that use inherited genetic risk in PCa care, for both early detection and localized disease management. [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]

Background: Celiac disease (CD) is underdiagnosed and associated with diagnostic delays. This has long‐term consequences for the health and well‐being of people living with the condition. Little is known about the qualitative configurations of the assessment processes of people living with CD. Methods: Using a thematic network analysis of 24 in‐depth interviews, this study explored the experiences of people living with CD related to their assessment processes leading to being diagnosed. Results: A significant diagnostic delay (up to 26 years) was evident in many interviews. Factors contributing to diagnostic delay included limited knowledge about CD among general practitioners (GP) and in the general population, categorisations of symptoms as 'typical' or 'atypical' and psychosomatic explanations of symptoms. Diagnostic delay resulted in (1) decreased psychological well‐being due to severe symptoms, changes in self‐perception and self‐blame; (2) decreased physiological well‐being due to comorbidities; and (3) mistrust in the healthcare system, leading to an increase in informants' responsibility for expediting their assessment processes. This suggested the presence of a neoliberal tendency because informants felt they were primarily responsible for their assessment processes. Conclusions: We encourage the implementation of initiatives to increase awareness of CD among GPs as well as more consistent and frequent use of the screening guideline due to variations in its clinical presentation. Increased awareness and consistency could reduce variations in assessment processes given GPs' varying knowledge about the condition. Key points: Factors contributing to diagnostic delay included varying and inadequate knowledge about celiac disease (CD) among general practitioners (GP) and informants. This highlights the importance of ensuring more consistent and frequent use of existing clinical guidelines in GPs' screening for CD and implementing initiatives to increase public awareness of CD and its diverse symptoms.For informants, diagnostic delays resulted in mistrust in the healthcare system, decreased physiological well‐being and decreased psychological well‐being due to changes in self‐perception and self‐blame. This signals the importance of increasing GPs' awareness of approaching patients with understanding in a nonstigmatising manner. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphism (SNP), as one of the key components of the genetic factors, is important for disease detection and early screening of hereditary diseases. Current SNP genotyping methods require laboratory instruments or long operating times. To facilitate the diagnosis of hereditary diseases, we developed a new method referred to as the LwaCas13a-based SNP genotyping platform (Cas13a platform), which is useful for detecting disease-related SNPs. We report a CRISPR/Cas13a-based SNP genotyping platform that couples recombinase-aided amplification (RAA), T7 transcription, and Leptotrichia wadei Cas13a (LwaCas13a) detection for simple and fast genotyping of human disease-related SNPs. We used this Cas13a platform to identify 17 disease-related SNPs, demonstrating that position 2 in gRNA is suitable for the introduction of additional mismatches to achieve high discrimination in genotyping across a wide range of SNP targets. The discrimination specificity of 17 SNPs was improved 3.0–35.1-fold after introducing additional mismatches at position 2 from the 5′-end. We developed a method, which has a lower risk of cross-contamination and operational complexity, for genotyping SNPs using human saliva samples in an one-pot testing that delivers results within 60 min. Compared to TaqMan probe qPCR, RFLP, AS-PCR and other SNP genotyping methods, the Cas13a platform is simple, rapid and reliable, expanding the applications of the CRISPR/Cas system in nucleic acid detection and SNP genotyping. [ABSTRACT FROM AUTHOR]

Over the past 20+ years, researchers have worked toward identifying early behavioral predictors of autism spectrum disorder (ASD) and developing observation-based screeners to supplement existing parent-report methods. This study is a follow-up, 3 to 8 years later, with parents/caregivers of 57 children previously enrolled in a U.S. university-based study evaluating early ASD-risk. The original study evaluated infants' (ages 15–35 months) ASD-risk through both observation-based and parent-report screeners. At follow-up, caregivers completed a phone interview inquiring about their child's developmental progress and diagnostic outcomes. Results indicated screener at-risk status agreement in infancy predicted only one of the four parent-reported ASD diagnoses at follow-up. Single instrument at-risk status aligned with two additional ASD diagnoses (one per screener), and both screeners missed one ASD diagnosis at follow-up. Results did not indicate significant added utility for the observation-based screener over the commonly used parent-report screener, suggesting that ASD behavioral markers may be hard to observe at early ages. [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]

Objective: This review aims to emphasize the role of pharmacists for optimization of evidence-based outcomes of finerenone in multidisciplinary kidney care teams during the early detection process of CKD patients. Data Sources: A PubMed literature search was performed using keywords pharmacists, chronic kidney disease (CKD), type 2 diabetes (T2D), and finerenone. Study Selection and Data Extraction: All English-language studies on the role of pharmacists in managing CKD patients or finerenone prescriptions were evaluated. Data Synthesis: CKD is a major health problem affecting millions worldwide, especially those with T2D. In recent years, new drugs have been added to the treatment options for patients with T2D and CKD, which have been shown to reduce the risk of cardiovascular and renal complications in large clinical trials. Conclusions: Pharmacists can help detect and treat CKD in patients with T2D. They may use indicators to identify potential candidates for appropriate finerenone therapy, such as stage of CKD, albuminuria level, serum potassium concentration, and use of RAAS inhibitors. Pharmacists can provide education on the benefits and usage of finerenone, monitor response to therapy, adjust the medications and doses, prevent drug interactions, help with adherence and tolerability issues, and coordinate with other healthcare providers. [ABSTRACT FROM AUTHOR]

Introduction: The earlier the hearing loss in a child, the more difficult will it be for the child to achieve optimal development and lead a normal life. This study is directed toward early identification of deafness in neonates born to gestational diabetic mothers that help in the early rehabilitation of the child. Materials and Methods: Around 50 infants born to mothers with gestational diabetes were taken and screened after obtaining consent from parents and clearance from the ethics committee. Neonates born to gestational diabetic mothers in Father Muller Medical College were included. Critically ill neonates, neonates with hyperbilirubinemia, family history of deafness, use of ototoxic drugs, and low APGAR score babies (<6) were excluded. Detailed family history and information of the patients were obtained. The occupation of both parents, family income, and consanguinity was noted. It was also observed if the antenatal mother has attended ANC clinic regularly. Results: A longitudinal descriptive study was conducted on 50 infants born to gestational diabetic mothers. Hearing loss was assessed using distortion product otoacoustic emission. The majority of gestational diabetic mothers were homemakers followed by salesgirls. The other occupations also included Anganwadi workers, beedi workers, manual laborers, tailors, and farmers. The list also included cooks, hotel receptionist, office assistant, and floor manager. Most of the fathers were drivers (n = 10) followed by farmers. The list also included fishermen, manual laborers, painters, salesmen, security personnel, waiters, tailors, teachers, office assistants as well as storekeepers. Conclusion: Our study assessed if there was a correlation between hearing loss in infants and gestational diabetes. Hence, in our study, we found that there may not be any relationship between hearing loss and diabetes in pregnancy. [ABSTRACT FROM AUTHOR]

This narrative review describes the different diagnostic, prognostic, management aspects and complications of the Brodie Bite. A current literature review was completed to identify relevant case reports, case series, and reviews about the Brodie Bite. Brodie Bite cases, fortunately, are not frequent. A checklist for this malocclusion is presented. Additionally, a description of the advantages of an early diagnosis will be discussed to avoid its progression to a more complex clinical scenario. The importance of solving the Brodie Bite before correcting any other simultaneous malocclusion problem is stressed. Emphasis is given on a three-dimensional diagnosis and planning. Different management mechanics are presented - ranging from TADS to fixed and functional appliances. Alternative disocclusion methods are also discussed. It is noted that some cases benefit from a combined surgical orthodontic correction. Finally, periodontal issues and difficulties are addressed. [ABSTRACT FROM AUTHOR]

In 2020, the World Health Organization (WHO) estimated that 2.3 million women worldwide were diagnosed with breast cancer, which resulted in 685,000 deaths. According to the projections, the number of women who have been diagnosed with breast cancer over the last five years before and by the end of 2020 was expected to reach 7.8 million, making it the most common type of cancer worldwide. Early diagnosis could prevent the ailment, however, lack of availability of health facilities and the cost of accessing treatment, especially in developing nations, are among the challenges confronting the solution. With the advent of artificial intelligence and machine learning models, specifically Convolutional Neural Networks (CNNs), considering their multiple architectures is highly promising to address the challenge of early diagnosis. Therefore, this study aims to propose an architecture of CNNs that gives the best accuracy, F1 score, and Cohen Kappa score among the Custom Optimized CNN, ResNet, and EfficientNet architectures. From the results, ResNet's performance across the five metrics outweighs the other two architectures. While ResNet reported an accuracy, precision, and F1 score of 0.9987, 0.9934, and 0.9950, respectively, EfficientNet, which has the second performance, reported 0.9977, 0.9914, and 0.9939 as accuracy, precision, and F1 score, respectively. Therefore, the best-performing architecture can be deployed for other available breast cancer datasets in order to ensure its total efficiency. [ABSTRACT FROM AUTHOR]

Wearable heart monitors are crucial for early diagnosis and treatment of heart diseases in non‐clinical settings. However, their long‐term applications require skin‐interfaced materials that are ultrasoft, breathable, antibacterial, and possess robust, enduring on‐skin adherence—features that remain elusive. Here, we have developed multifunctional porous soft composites that meet all these criteria for skin‐interfaced bimodal cardiac monitoring. The composite consists of a bilayer structure featuring phase‐separated porous elastomer and slot‐die‐coated biogel. The porous elastomer ensures ultrasoftness, breathability, ease of handling, and mechanical integrity, while the biogel enables long‐term on‐skin adherence. Additionally, we incorporated ε‐polylysine in the biogel to offer antibacterial properties. Also, the conductive biogel embedded with silver nanowires was developed for use in electrocardiogram sensors to reduce contact impedance and ensure high‐fidelity recordings. Furthermore, we assembled a bimodal wearable cardiac monitoring system that demonstrates high‐fidelity recordings of both cardiac electrical (electrocardiogram) and mechanical (seismocardiogram) signals over a 14‐day testing period. [ABSTRACT FROM AUTHOR]

BACKGROUND: Rice bakanae disease (RBD) has longstanding challenges impacted rice production, which is predominantly induced by Fusarium fujikuroi Nirenberg. Early diagnosis of F. fujikuroi is important to control RBD and improve quality and quantity of rice production. This study presents a novel on‐site diagnosis platform combined with CRISPR/LbCas12a and LAMP to detect F. fujikuroi. RESULTS: LAMP amplification of TEF1‐α, a characteristic gene of F. fujikuroi were performed, followed with trans‐cleavage reaction of LbCas12a, cleaving the single‐stranded DNA reporter, which is modified by the terminal fluorophore and quencher groups, producing fluorescence signal. The platform was confirmed with high specificity and sensitivity (LOD <1 aM). Furthermore, we designed a lateral flow strip experiment based on the trans‐cleavage activity of LbCas12a, which was identified with similar sensitivity and specificity to the fluorescence detection method. CONCLUSION: In summary, this study achieved a platform with remarkable sensitivity and specificity for F. fujikuroi detection and provide potential for on‐site and ultrasensitive diagnostic tools for RBD. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

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Burkholderia cepacia complex (Bcc) is a bacterial group with 'natural' multi-antimicrobial resistance. This complex has generated epidemic outbreaks across the world. In people with cystic fibrosis (CF), Bcc can cause severe lung infections that lead to accelerated lung damage, which can be complicated by necrotizing pneumonia accompanied by high fevers, leucocytosis, and bacteraemia, which commonly causes fatal outcomes. Specifically, infection by Burkholderia cenocepacia is considered an exclusion criterion for lung transplantation. The species of Bcc exhibit both genetic and phenotypic hypervariability that complicate their accurate microbiological identification. Automated methods such as MALDI-TOF can err in the determination of species. Their slow growth even in selective agars and the absence of international consensuses on the optimal conditions for their isolation make early diagnosis a difficult challenge to overcome. The absence of correlations between antibiograms and clinical results has resulted in the absence of standardized cut-off values of antimicrobial susceptibility, a fact that brings a latent risk since incorrect antibiotic therapy can induce the selection of more aggressive variants that worsen the clinical picture of the host, added to the absence of a clear therapeutic guide for the eradication of pulmonary infections by Bcc in patients with CF, resulting in frequently ineffective treatments. There is an urgent need to standardize methods and diagnostic tools that would allow an early and accurate diagnosis, as well as to perform clinical studies of the effectiveness of available antibiotics to eradicate Bcc infections, which would allow us to establish standardized therapeutic schemes for Bcc-infected patients. [ABSTRACT FROM AUTHOR]

Coronary artery disease is a prominent contributor to cardiovascular death. Automatic segmentation of Coronary Angiography (CAG) images is crucial for early diagnosis and treatment and holds significant importance in clinical diagnosis, surgical planning, and treatment evaluation. However, due to challenges such as poor image quality, complex backgrounds, and fine vessel structures, the task of automatic segmentation has always been difficult. This study aims to improve the accuracy and robustness of automatic CAG segmentation. Therefore, we propose a UNet model improved with the Convolutional Block Attention Module (CBAM), which enhances the model’s ability to capture important feature areas by combining the Channel Attention Module (CAM) and the Spatial Attention Module (SAM), making the identified vessels completer and more accurate. Specifically, the CBAM module is integrated into each convolutional layer to enhance the feature representation capabilities of the channel and space. We evaluate the CBAM-UNet based on X-ray Angiography Coronary Artery Disease (XCAD) datasets. The experimental results indicate that the improved model’s accuracy increased to 97%, and precision reached 89.95%. The results demonstrate that the improved model’s overall performance on various metrics surpasses traditional methods, with significant improvements in anti-background noise interference and handling complex vascular structures. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Aims Materials and Methods Discussion Conclusion Portal hypertension (PH) is a critical complication in patients with hepatic diseases. Its accurate evaluation is essential for early diagnosis, risk stratification, and management. Endoscopic ultrasound (EUS) has emerged as a promising diagnostic tool, offering high‐resolution imaging of the portal venous system, hepatic vasculature, and surrounding structures.This review aims at providing an overview of the evolving role of EUS in PH evaluation in patients with liver disease.A systematic search was conducted in PubMed and Google Scholar until 31 May 2024. Relevant studies were identified using keywords related to EUS and PH. Additional references were included based on expert knowledge and citation analysis. Only full‐length papers and abstracts in English were considered. Results: EUS demonstrates significant utility in PH assessment, offering high‐resolution imaging and advanced tools like contrast enhancement (CE) and shear‐wave elastography (SWE) for evaluating liver stiffness and correlating it with PH severity. EUS‐guided portal pressure gradient (PPG) measurement provides a less invasive method for evaluating PH, potentially offering a safer alternative to conventional techniques.EUS offers unique advantages in PH assessment, enabling comprehensive evaluation in a single session. Despite its potential, limitations such as invasiveness, sedation‐related variability, and restricted availability persist. Emerging techniques require further validation in larger cohorts and standardised training.EUS is a valuable diagnostic tool for PH evaluation, with the potential to improve outcomes through earlier diagnosis and better stratification. Addressing its limitations through further research and standardised protocols is critical to optimize its clinical utility.Trial Registration: NCT04115046, NCT05728697, NCT05097963 and NCT03155282 [ABSTRACT FROM AUTHOR]

BACKGROUND: Cervical spondylotic myelopathy is a progressive disease leading to dysfunction in the middle-aged and elderly, and early diagnosis is difficult. In recent years, some clinical scholars have found that dynamic magnetic resonance imaging technology can detect spinal cord compression in a dynamic position earlier, but its specific biomechanical mechanism needs to be clarified. OBJECTIVE: To investigate the biomechanical compression characteristics of early cervical spondylotic myelopathy in hyperextension and flexion position, and to verify the effectiveness of dynamic magnetic resonance imaging in the diagnosis of early cervical spondylotic myelopathy. METHODS: A retrospective analysis was made on the patients who underwent cervical dynamic magnetic resonance imaging in the Department of Orthopedics of First Affiliated Hospital of Guangxi University of Chinese Medicine from January to June 2022. 16 subjects were selected and divided into two groups. The pathological group included 8 patients with early cervical spondylotic myelopathy with hypertrophy of ligamentum flavum as the main sign, with 5 male patients and 3 female patients. The normal group included 8 normal degenerative people, with 4 male patients and 4 female patients. All patients were photographed with cervical CT plain scan, magnetic resonance imaging plain scan, and dynamic magnetic resonance imaging plain scan. This study was divided into the following three parts: (1) collect the dynamic magnetic resonance imaging image DCOM data of two groups of subjects, and collect the cervical vertebra CT and neutral magnetic resonance imaging image DCOM data to understand the bone and soft tissue of the two groups of subjects in the neutral position. (2) Based on the DCOM data of magnetic resonance imaging and CT plain scan, the three-dimensional finite element models of lower cervical vertebra (C3-7) of normal degenerative population and early cervical spondylotic myelopathy patients were established by reverse engineering software. The equivalent stress and equivalent elastic strain of the spinal cord and posterior dura were analyzed, and the distribution of stress and strain was observed. (3) After obtaining the stress and strain data, the data between groups were compared to analyze the mechanical characteristics of spinal cord compression caused by early cervical spondylotic myelopathy in a dynamic position and to verify the effectiveness of dynamic magnetic resonance imaging in the diagnosis of early cervical spondylotic myelopathy. RESULTS AND CONCLUSION: (1) When simulating the posterior extension, flexion and neutral position of the lower cervical vertebrae (C3-7) in the two groups, the values of stress and strain in the posterior part of the spinal cord were in the following order: extension > flexion > neutral (P < 0.05). The strain values from large to small were as follows: extension > flexion > neutral (P < 0.05). (2) Compared with the normal degenerative population model, the equivalent stress and strain of the spinal cord in the pathological group were higher than those in the normal group under two degrees of freedom of flexion and extension (P < 0.05). The distribution area of stress and strain in the posterior part of the spinal cord was irregular. (3) In the neutral position, there was no significant difference in the strain value of the spinal cord between the two groups (P > 0.05), and the strain distribution was uniform and regular. (4) It is indicated that in the cervical extension position, the dural sac and the posterior part of the spinal cord were compressed and deformed in the early cervical spondylotic myelopathy patients with the hypertrophy of ligamentum flavum as the main sign, and the degree of compression deformation of the spinal cord was significantly higher than that in the anterior flexion position and neutral position. In the neutral position, there were no obvious signs of spinal cord deformation in patients with early cervical spondylotic myelopathy. This study verified the role of dynamic magnetic resonance imaging in the diagnosis of early cervical spondylotic myelopathy from the point of view of biomechanics. [ABSTRACT FROM AUTHOR]

Because of early diagnosis and improved prognosis, patients with gastric cancer are now surviving longer and remnant gastric cancer after gastrectomy is becoming more common. Remnant gastric cancer is traditionally considered a malignancy with a dismal outcome. However, recent advances in diagnostic and therapeutic strategies have improved outcomes. In recent years, the development of endoscopic therapy has provided us with new therapeutic ideas. Although with some drawbacks, such as limited working space, gastric fibrosis and staples under the suture line, endoscopic management is still an effective treatment option with potential long-term survival advantage. For gastrectomy patients, endoscopic surveillance should also be recommended, for prompt surveillance and detection in the early remnant gastric cancer. This review aims to provide an overview of remnant gastric cancer, especially focusing on its pathogenesis as well as new advances in the diagnosis and endoscopic treatment of early-stage remnant gastric cancer. [ABSTRACT FROM AUTHOR]

Background: COVID-19, caused by the SARS-CoV-2 virus, is one of the most known pandemics ever affecting human life and global economics. Recently, it has shown several symptoms related to different organ systems, including the nervous system, represented in some reported neurological manifestations. Therefore, a smart prediction system that can determine the likelihood and certainty of having COVID-19 based on those neurological manifestations can help in early detection of the disease, which helps in diagnosis and limiting the prevalence of COVID-19. Patients and methods: This study involved a comprehensive data collection process. We gathered information from thousands of patients, encompassing both neurological and non-neurological manifestations of COVID-19. This data, derived from various research works, including mild and moderate cases, was then subjected to rigorous statistical analysis. The results of this analysis formed the basis for the design of a fuzzy interference system (FIS), which utilizes a fuzzy logic approach to determine the certainty of COVID-19 based on neurological symptoms. Results: Statistical analysis of the collected data showed neurological symptoms in all surveyed cases in the first week of the COVID-19 presentation. Headache has been reported in 70–80% of all cases; anosmia–dysgeusia showed up in 50–60% of total cases; Myalgia presented in 40–45% of all cases; Fatigue was there in 30–35% of the surveyed cases; dizziness was recorded in 30–35% of patients; 0–10% of subjects showed noncommon symptoms like numbness, migraine, loss of concentration, and seizures. By applying these statistical results to the fuzzification process and developing the rulesets, the fuzzy logic-based forecasting system could determine the certainty of COVID-19 with high accuracy, reaching 95% by comparing it with the clinical data. Conclusions: Surveying neurological and non-neurological symptoms of thousands of COVID-19 patients in many related literature showed neurological manifestations in all patients with different ratios and weights, including mild and moderate cases, by statistically analyzing these data to form the rulesets of a predesigned fuzzy logic-based forecasting system. The fuzzy logic system was able to yield a successful prediction of the likelihood of having COVID-19 in a group of patients based on their neurological symptoms with an accuracy of 95% by comparing the predicted data with the clinical data. [ABSTRACT FROM AUTHOR]