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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]

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]

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]

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]

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]

Background: Our previous small-sample study indicated that serum levels of interleukin enhancer binding factor 2 (ILF2) may have the potential for gastric cancer (GC) detection. The present study was conducted to further validate the diagnostic value of serum ILF2 protein for GC. Methods: Serum specimens and clinical data were collected from patients with GC (n = 99) or benign gastric disease (BGD) (n = 49) and healthy controls (HC) (n = 51). Serum ILF2 levels were measured using enzyme-linked immunosorbent assay. The diagnostic performance of ILF2 was evaluated using the area under the receiver operating characteristic curve (AUC). The independence and synergy of ILF2 in GC diagnosis were analyzed by modeling with conventional blood indicators. Results: The median serum ILF2 level was higher in the GC group (227.8ng/mL) than in the BGD group (72.0ng/mL) and the HC group (56.8ng/mL) (p < 0.001), and no significant difference across GC subgroups. The AUCs of ILF2 were 0.915 (95%CI 0.873–0.957) for GC vs. HC, 0.854 (95%CI 0.793–0.915) for GC vs. BGD, 0.885 (95%CI 0.841–0.929) for GC vs. BGD + HC, and 0.888 (95% CI 0.830–0.945) for TNM I stage GC vs. BGD + HC, outperforming conventional blood indicators (corresponding AUCs ranging from 0.641 to 0.782). ILF2 was independent of and synergistic with conventional blood indicators in GC diagnosis, and a simple diagnostic model based on ILF2 and red blood cell count improved the diagnostic performance, with positive rates of approximately 90% in various subgroups of GC. Conclusions: Serum ILF2 protein is a novel and potential serum biomarker for the detection of GC, especially for early GC. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) have gained prominence as important players in various biological processes such as gastric cancer (GC). Identification of several dysregulated circRNAs may serve as biomarkers for early diagnosis or as novel therapeutic targets. Predictive models can suggest potential new interactions and regulatory roles of circRNAs in GCs. Experimental validations of key interactions are being performed using in vitro models, confirming the significance of identified circRNA networks. The aim of this review is to highlight the important circRNAs associated with GC. On top of that an overview of the mechanistic details of the biogenesis and functionalities of the circRNAs are also presented. Furthermore, the potentialities of the circRNAs in the field of new drug discovery are deciphered. [ABSTRACT FROM AUTHOR]

Background: Osteonecrosis of the femoral head (ONFH) is a significant postoperative complication following femoral neck fractures (FNFs) in adolescents, that has garnered considerable attention from researchers. Despite this interest, the incidence of ONFH in adolescents post-FNF surgery has not been extensively evaluated. To contribute to the body of research, we performed a meta-analysis utilizing articles sourced from multiple databases, with an emphasis on the rate of ONFH occurrence in young adolescents following FNF surgery. Objective: The purpose of this study was to investigate the incidence of ONFH after FNFs in adolescents worldwide and analyse potential risk factors affecting its occurrence to provide guidance for the treatment and rehabilitation after FNFs in adolescents and reduce the incidence of ONFH. Methods: A comprehensive search of medical literature databases, including MEDLINE, Web of Science, the Cochrane Library, and Embase, was conducted to identify relevant studies on ONFH and its associated risk factors after surgical treatment for FNFs in adolescents, covering the period from the establishment of the database to April 2024. Studies that did not meet the inclusion criteria were excluded. The study was conducted based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA). Literature extraction, management, and data entry were performed using Zotero 6.0 and Excel 2016. The meta-analysis was conducted using R Studio 4.2.2. Subgroup analysis, sensitivity analysis, and assessment of publication bias were performed to explore sources of heterogeneity and evaluate the reliability of the results. This study has been registered on Prospero. (registration number: CRD4202452794; date of registration: 03/04/2024) Results: A total of 17 publications involving 862 patients with FNFs were included in this meta-analysis. The findings revealed that the incidence of ONFH after FNF surgery in adolescents was 24.02% [95% CI (0.2118, 0.2712)]. The included studies demonstrated good consistency, and no publication bias was observed. Conclusion: This study revealed that the incidence of ONFH after FNFs in adolescents is high (24.02%). Early screening and effective treatment of postoperative patients are crucial. [ABSTRACT FROM AUTHOR]

The objective of this study was to construct a prediction model for early diagnosis and classification of lung cancer based on multi-dimensional clinical data. Three advanced machine learning models — Random Forest, Decision Tree, and Support Vector Machine (SVM) — were employed to predict lung cancer using features such as age, chronic disease history, and clinical symptoms. The models were optimized through various strategies to improve predictive performance. The results demonstrated that all three models achieved high accuracy and sensitivity in lung cancer prediction. Furthermore, a detailed analysis of feature importance identified key factors such as age and chronic disease history that significantly influenced prediction outcomes. Among the models, the SVM exhibited particularly strong performance, providing robust support for accurate lung cancer prediction. Future work will focus on integrating multimodal data and optimizing model architecture and hyperparameters to further enhance the predictive accuracy and clinical utility of the model, thereby contributing to the early diagnosis and treatment of lung cancer. [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]

Background: Osteonecrosis of the femoral head (ONFH) is a progressive and debilitating condition characterized by the death of bone tissue due to inadequate blood supply. Despite advances in diagnostic imaging and treatment strategies, predicting the risk of femoral head collapse remains a significant clinical challenge. This study seeks to address this gap by developing a robust prognostic model that integrates clinical, imaging, and laboratory data to improve early diagnosis and guide therapeutic decision-making. Methods: We conducted a qualitative systematic review and employed the Delphi method to select key prognostic factors from clinical data, imaging findings, and laboratory indicators. The study included ONFH patients treated from January 2014 to December 2021. We used univariate and multivariate Cox regression analyses to develop a nomogram for predicting the risk of femoral head collapse. The model's performance was evaluated using the concordance index (C-index), calibration plots, and decision curve analysis (DCA). Results: The study included 297 patients (454 hips) with ONFH. Key prognostic factors identified included pain presence (p < 0.001, RR = 0.185, 95% CI: 0.11–0.31), JIC classification (C1: p < 0.001, RR = 0.096, 95% CI: 0.054–0.171; C2: p < 0.001, RR = 0.323, 95% CI: 0.215–0.487), necrotic area (3 < MNAI < 6: p < 0.001, RR = 0.107, 95% CI: 0.061–0.190; MNAI ≥ 6: p < 0.001, RR = 0.466, 95% CI: 0.314–0.692), weight-bearing reduction (p < 0.001, RR = 0.466, 95% CI: 0.323–0.672), preservation of the anterolateral pillar (p < 0.001, RR = 0.223, 95% CI: 0.223–0.473), and subchondral bone fracture on CT (p < 0.001, RR = 0.32, 95% CI: 0.217–0.472). The nomogram demonstrated a high C-index of 0.88, indicating excellent predictive accuracy. Calibration plots showed good agreement between predicted and observed outcomes, and DCA confirmed the model's clinical utility. Conclusions: The prognostic model developed in this study provides a reliable tool for predicting femoral head collapse in ONFH patients. It allows for early identification of high-risk patients, guiding personalized treatment strategies to improve patient outcomes and reduce the need for invasive surgical procedures. [ABSTRACT FROM AUTHOR]

Streptococcus oralis , belonging to the viridans group streptococci (VGS), has been considered a member of normal flora mainly inhabiting the oral cavity. However, more recently, there has been growing recognition of its role as a causative agent in various life-threatening infectious diseases such as infective endocarditis (IE) and meningitis. Additionally, the differences in the prevalence, clinical features, and prognosis of opportunistic infections between S. oralis and other VGS species have been addressed. Particularly the predominance of S. oralis in IE has drawn critical attention. In potentially fatal infections, clinical neglect of S. oralis as an instigating agent might significantly impede early diagnosis and treatment. Nevertheless, to date, the infectious diseases associated with S. oralis have not yet been comprehensively described. Therefore, this review will give an overview of infectious diseases caused by S. oralis to uncover its hidden role as an opportunistic pathogen. [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]

Highlights: 1. This is the first report identifying SCUBE1 and RNF103-CHMP3 as therapeutic targets for diabetic foot ulcers (DFU). 2. Our study found that SCUBE1 and RNF103-CHMP3 were significantly downregulated in patients who were cured of DFU. 3. This study discovered that SCUBE1 and RNF103-CHMP3 are associated with extracellular interactions. 4. Our findings indicate that SCUBE1 and RNF103-CHMP3 are related to immune cell infiltration. 5. This study provides new theoretical foundations and molecular targets for the diagnosis and treatment of DFU. Background: To utilize machine learning for identifying treatment response genes in diabetic foot ulcers (DFU). Methods: Transcriptome data from patients with DFU were collected and subjected to comprehensive analysis. Initially, differential expression analysis was conducted to identify genes with significant changes in expression levels between DFU patients and healthy controls. Following this, enrichment analyses were performed to uncover biological pathways and processes associated with these differentially expressed genes. Machine learning algorithms, including feature selection and classification techniques, were then applied to the data to pinpoint key genes that play crucial roles in the pathogenesis of DFU. An independent transcriptome dataset was used to validate the key genes identified in our study. Further analysis of single-cell datasets was conducted to investigate changes in key genes at the single-cell level. Results: Through this integrated approach, SCUBE1 and RNF103-CHMP3 were identified as key genes significantly associated with DFU. SCUBE1 was found to be involved in immune regulation, playing a role in the body's response to inflammation and infection, which are common in DFU. RNF103-CHMP3 was linked to extracellular interactions, suggesting its involvement in cellular communication and tissue repair mechanisms essential for wound healing. The reliability of our analysis results was confirmed in the independent transcriptome dataset. Additionally, the expression of SCUBE1 and RNF103-CHMP3 was examined in single-cell transcriptome data, showing that these genes were significantly downregulated in the cured DFU patient group, particularly in NK cells and macrophages. Conclusion: The identification of SCUBE1 and RNF103-CHMP3 as potential biomarkers for DFU marks a significant step forward in understanding the molecular basis of the disease. These genes offer new directions for both diagnosis and treatment, with the potential for developing targeted therapies that could enhance patient outcomes. This study underscores the value of integrating computational methods with biological data to uncover novel insights into complex diseases like DFU. Future research should focus on validating these findings in larger cohorts and exploring the therapeutic potential of targeting SCUBE1 and RNF103-CHMP3 in clinical settings. [ABSTRACT FROM AUTHOR]

Background: Non-cystic fibrosis (non-CF) bronchiectasis (BE) is defined as a clinical syndrome of recurrent, persistent wet cough and abnormal bronchial dilatation on chest High Resolution Computed Tomography (HRCT) scans. The aims of this study were to characterize the pattern of the trajectories of lung function parameters and to consider the relationship between the lung function and radiological severity according to the modified Reiff score. Methods: The study retrospectively considered 86 children (46.5% male, median age of 4 years) with non-CF BE, admitted at the Paediatric Pneumology Unit of Buzzi Children's Hospital from January 2015 to December 2022. The diagnosis of BE was made according to the presence of a suggestive clinical history and symptoms and key features of BE evidenced on chest HRCT scans. The modified Reiff score was adapted to quantify the severity of BE. Spirometry (COSMED MicroQuark spirometer) was performed at median age of 5.78 years (baseline or T0) and after 1 and 2 years from the baseline (T1 and T2, respectively). The general trends of lung function parameters were estimated by ANOVA models for repeated measurements. For each lung function parameter, a longitudinal regression model was fitted. The analysis was performed with the software R release 4.2.3. The statistical significance was deemed when the p-value resulted lower than 0.05. Results: The general trends of lung function parameters showed a statistically significant variation of forced vital capacity (FVC%) and forced expiratory volume in 1s (FEV1%) from T0 to T1 (p = 0.0062, 0.0009) and no significant change for FVC%, FEV1% and forced expiratory flow 25–75% of VC (FEF25/75%) from T1 to T2 (p = 0.145, 0.210, 0.600, respectively). Notably, we found no correlation between the age at diagnosis and the lung function parameters at T0 (r = 0.149, 0.103 and 0.042 for FVC%, FEV1% and FEF25/75%, respectively). Instead, a poor negative correlation resulted between the Reiff score and FVC%, FEV1% e FEF25/75% at baseline (Spearman coefficients: rho=-0.156, -0.204, -0.103, respectively). Conclusions: A stable pulmonary function is detectable within 2 years follow up from baseline spirometry. The modified Reiff score should be considered as a good tool not only to quantify the radiological lung involvement but also the degree of pulmonary function impairment. [ABSTRACT FROM AUTHOR]

AbstractInfectious diseases remain a global health challenge, necessitating innovative approaches for their early diagnosis and effective treatment. Artificial Intelligence (AI) has emerged as a transformative force in healthcare, offering promising solutions to address this challenge. This review article provides a comprehensive overview of the pivotal role AI can play in the early diagnosis and treatment of infectious diseases. It explores how AI-driven diagnostic tools, including machine learning algorithms, deep learning, and image recognition systems, enhance the accuracy and efficiency of disease detection and surveillance. Furthermore, it delves into the potential of AI to predict disease outbreaks, optimise treatment strategies, and personalise interventions based on individual patient data and how AI can be used to gear up the drug discovery and development (D3) process.The ethical considerations, challenges, and limitations associated with the integration of AI in infectious disease management are also examined. By harnessing the capabilities of AI, healthcare systems can significantly improve their preparedness, responsiveness, and outcomes in the battle against infectious diseases. [ABSTRACT FROM AUTHOR]

Background: Plasma biomarkers have recently emerged for the diagnosis, assessment, and disease monitoring of Alzheimer's disease (AD), but have yet to be fully validated in preclinical AD. In addition to AD pathologic plasma biomarkers (amyloid-β (Aβ) and phosphorylated tau (p-tau) species), a proteomic panel can discriminate between symptomatic AD and cognitively unimpaired older adults in a dementia clinic population. Objective: Examine the added value of a plasma proteomic panel, validated in symptomatic AD, over standard AD pathologic plasma biomarkers and demographic and genetic (apolipoprotein (APOE) ɛ4 status) risk factors in detecting preclinical AD. Methods: 125 cognitively unimpaired older adults (mean age = 66 years) who completed Aβ PET and plasma draw were analyzed using multiple regression with Aβ PET status (positive versus negative) as the outcome to determine the best fit for predicting preclinical AD. Model 1 included age, education, and gender. Model 2 and 3 added predictors APOE ɛ4 status (carrier versus non-carrier) and AD pathologic blood biomarkers (Aβ42/40 ratio, p-tau181), respectively. Random forest modeling established the 5 proteomic markers from the proteomic panel that best predicted Aβ PET status, and these markers were added in Model 4. Results: The best model for predicting Aβ PET status included age, years of education, APOE ɛ4 status, Aβ42/40 ratio, and p-tau181. Adding the top 5 proteomic markers did not significantly improve the model. Conclusions: Proteomic markers in plasma did not add predictive value to standard AD pathologic plasma biomarkers in predicting preclinical AD in this sample. [ABSTRACT FROM AUTHOR]

Continuously monitoring human airway conditions is crucial for timely interventions, especially when airway stents are implanted to alleviate central airway obstruction in lung cancer and other diseases. Mucus conditions, in particular, are important biomarkers for indicating inflammation and stent patency but remain challenging to monitor. Current methods, reliant on computational tomography imaging and bronchoscope inspection, pose risks due to radiation and lack the ability to provide continuous real-time feedback outside of hospitals. Inspired by the sensing ability of biological cilia, we report wireless sensing mechanisms in sensory artificial cilia for detecting mucus conditions, including viscosity and layer thickness, which are crucial biomarkers for disease severity. The sensing mechanism for mucus viscosity leverages external magnetic fields to actuate a magnetic artificial cilium and sense its shape using a flexible strain-gauge. Additionally, we report an artificial cilium with capacitance sensing for mucus layer thickness, offering unique self-calibration, adjustable sensitivity, and range, all enabled by external magnetic fields. To enable prolonged and wireless data access, we integrate Bluetooth Low Energy communication and onboard power, along with a wearable magnetic actuation system for sensor activation. We validate our method by deploying the sensor independently or in conjunction with an airway stent within a trachea phantom and sheep trachea ex vivo. The proposed sensing mechanisms and devices pave the way for real-time monitoring of mucus conditions, facilitating early disease detection and providing stent patency alerts, thereby allowing timely interventions and personalized care. [ABSTRACT FROM AUTHOR]

Background: Oral HIV self-testing (HIVST) among men is relatively low and still inadequate in Sub-Saharan Africa. Delivering HIVST kits by pregnant women attending antenatal care to their partners is a promising strategy for increasing HIV testing among men. However, even amidst the HIV testing interventions, most men do not know their HIV status. This study, aimed to determine the proportion of partners who received and used oral HIVST kits delivered by pregnant women, associated factors, barriers, and facilitators for uptake. Methods: We conducted an exploratory sequential mixed methods study among 380 sampled partners. Lists of partners from HIVST logbooks whose women picked an HIVST kit were obtained and systematic sampling was done to recruit participants. Fourteen (14) male partners were purposively selected for in-depth interviews to identify barriers and facilitators. We used modified poison regression to determine factors associated with oral HIVST. We used an inductive thematic analysis for qualitative analysis. Results: Out of 380 participants, 260(68.4%) received an oral HIVST kit from their pregnant women, and 215(82.7%) used it for HIVST. Oral HIVST was associated with; Information Education and Communication like availability of HIVST guiding materials (aPR = 1.64, 95%CI: 1.48–1.82), being reached at home (aPR = 1.04, 95%CI 1.01–1.08), and being aware of the woman's HIV status (aPR = 1.04, 95%CI 0.99–1.09). In-depth results identified barriers to uptake as, lack of trust in the HIVST kit results, fear of test outcome in the presence of their partner and inclination that the HIV status of their women is the same as theirs; Facilitators included convenience, ease of use, prior awareness of their HIV status, and fear of relationship consequences and breakup. Conclusion: Delivery of oral HIVST kits to men through pregnant women reached a high number of men and achieved high uptake. Accessing information, education, communication and the kit's convenience were major reasons for uptake among men who received the kit as; trust issues affected its use among partners. Scaling up the delivery of oral HIVST kits at all departments of hospitals through women seeking health services is paramount to support HIV screening among men to reach the UNAIDS 95 strategy. [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]

This study proposes a synthetic data generation model to create a classification framework for cerebellar ataxia patients using trajectory data from the visuomotor adaptation task. The classification objectives include patients with cerebellar ataxia, age-matched normal individuals, and young healthy subjects. Synthetic data for the three classes is generated based on class conditions and random noise by leveraging a combination of conditional adversarial generative neural networks and reconstruction networks. This synthetic data, alongside real data, is utilized as training data for the patient classification model to enhance classification accuracy. The fidelity of the synthetic data is assessed visually to measure the validity and diversity of the generated data qualitatively while quantitatively evaluating distribution similarity to real data. Furthermore, the clinical efficacy of the patient classification model employing synthetic data is demonstrated by showcasing improved classification accuracy through a comparative analysis between results obtained using solely real data and those obtained when both real and synthetic data are utilized. This methodological approach holds promise in addressing data insufficiency in the digital healthcare domain, employing deep learning methodologies, and developing early disease diagnosis tools. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusion Hepatocellular carcinoma (HCC) is a leading cause of cancer‐related mortality, and early diagnosis via gadolinium ethoxybenzyl‐diethylenetriamine pentaacetic acid (Gd‐EOB‐DTPA)‐enhanced magnetic resonance imaging (MRI) significantly impacts patient outcomes. However, patient anxiety during MRI can affect image quality. This study investigates the impact of pre‐examination video education on anxiety, satisfaction and image quality in Gd‐EOB‐DTPA‐enhanced liver MRI.We prospectively enrolled 480 patients who underwent Gd‐EOB‐DTPA‐enhanced liver MRI from January 2022 to May 2023 at our hospital. Patients were divided into study and control groups in order of odd and even days, with 240 cases in each group. Before the examination, the radiology staff provided routine verbal guidance and breathing training to the patients in the control group, while the study group was given additional video education. The state anxiety scores, satisfaction scores of the provided information and motion artefact scores of the images before and after the examination were compared between the two groups.The state anxiety scores of both groups of patients were lower than before the examination (all P < 0.05), but the change value of the study group was significantly greater than that of the control group (P = 0.004). The satisfaction rate of the information provided before the scan in the study group was significantly higher (P < 0.001). The image quality scores of the arterial phase were similar between the two groups (P = 0.403), but the image quality of the study group in the pre‐contrast, portal phase, transitional phase and hepatobiliary phase was significantly better than that of the control group (all P < 0.05).Supplementing routine pre‐scan care with video guidance for Gd‐EOB‐DTPA‐enhanced liver MRI offers several benefits, including reduced patient anxiety, increased satisfaction and improved image quality. These results suggest the potential for widespread application of video‐based interventions to enhance the MRI experience for patients. [ABSTRACT FROM AUTHOR]

Owing to the epidemic growth of diabetes, ophthalmologists need to examine the huge fundus images for diagnosing the disease of Diabetic Retinopathy (DR). Without proper knowledge, people are too lethargic to detect the DR. Therefore, the early diagnosis system is requisite for treating ailments in the medical industry. Therefore, a novel deep model-based DR detection structure is recommended to fix the aforementioned difficulties. The developed deep model-based diabetic retinopathy detection process is performed adaptively. The DR detection process is imitated by garnering the images from benchmark sources. The gathered images are further preceded by the abnormality segmentation phase. Here, the Residual TransUNet with Enhanced loss function is used to employ the abnormality segmentation, and the loss function in this structure may be helpful to lessen the error in the segmentation procedure. Further, the segmented images are passed to the final phase of retinopathy detection. At this phase, the detection is carried out through the Adaptive Multiscale MobileNet. The variables in the AMMNet are optimized by the Adaptive Puzzle Optimization to obtain better detection performance. Finally, the effectiveness of the offered approach is confirmed by the experimentation procedure over various performance indices. [ABSTRACT FROM AUTHOR]

Background: Traumatic duodenal rupture is rare which accounts for only 2–10% of all Blunt abdominal trauma. The purpose of this study was to investigate the experience of diagnosis and treatment of traumatic duodenal rupture in children. Methods: This was a retrospective case series study. Clinical data were collected from three children suffering from a traumatic duodenal rupture who received surgical treatment in our hospital from January 2013 to January 2023. Demographic characteristics, trauma mechanism, physical examination, auxiliary examination, operation timing and plan, postoperative management and follow-up of included patients were described. Summarize the importance of early diagnosis and treatment of this type of injury. Results: Three children (1 male and 2 females) with duodenal rupture due to traumatic abdominal injury were included in the study, with average age of 8.2 years (range 2.75–13.25 years). Among them, One child was injured by heavy objects. The other two children had lumbar fracture and seat belt sign. All three patients underwent emergency operation within 24 h, and recovered well after surgery. No related complications seen during follow-up. Conclusions: Duodenal rupture is a rare but fatal disease. Early identification and active abdominal exploration can reduce the incidence of related complications and mortality in children with blunt abdominal injury, especially those with seat belt sign. Simultaneously, the standardized postoperative management is significant for the cure of children. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by diverse clinical features. EEG biomarkers such as spectral power and functional connectivity have emerged as potential tools for enhancing early diagnosis and understanding of the neural processes underlying ASD. However, existing studies yield conflicting results, necessitating a comprehensive, data-driven analysis. We conducted a retrospective cross-sectional study involving 246 children with ASD and 42 control children. EEG was collected, and diverse EEG features, including spectral power and spectral coherence were extracted. Statistical inference methods, coupled with machine learning models, were employed to identify differences in EEG features between ASD and control groups and develop classification models for diagnostic purposes. Our analysis revealed statistically significant differences in spectral coherence, particularly in gamma and beta frequency bands, indicating elevated long range functional connectivity between frontal and parietal regions in the ASD group. Machine learning models achieved modest classification performance of ROC-AUC at 0.65. While machine learning approaches offer some discriminative power classifying individuals with ASD from controls, they also indicate the need for further refinement. [ABSTRACT FROM AUTHOR]

Placenta accreta spectrum is a prevalent cause of severe postpartum hemorrhage and emergency hysterectomy. In recent years, there has been an increasing incidence of placenta accreta spectrum. Besides traumatic uterine injury factors such as elevated cesarean section rates, other non-traumatic factors including uterine malformation, endometritis, uterine adenomyosis, in vitro fertilization-embryo transfer also warrant serious consideration. This article focuses on early disease detection, standardized diagnosis, prevention, and treatment of placental implantation disorders associated with non-traumatic factors to enhance the adverse outcomes for these parturients. [ABSTRACT FROM AUTHOR]

Highly sensitive biosensors which operate based on the utilization of random telegraph signal (RTS) noise spectroscopy might allow for the detection of diseases at the earliest stage. In this work, we provide an overview of our findings concerning RTS behavior in liquid-gated (LG) field-effect transistors (FETs). We propose different kinds of LG FET structures supported by modeling data for the extraction of characteristic RTS parameters for different carrier concentrations and operating voltages in the devices. We demonstrate the innovative aspects and opportunities of noise and fluctuations for biosensing applications. Moreover, we show that light excitation can be used to tune RTS parameters and increase the sensitivity of biosensors by at least 1.5 times. We also discuss challenges related to the utilization of RTS in different research fields, including biosensors and quantum information technology as well as ways of finding solutions to the problems. [ABSTRACT FROM AUTHOR]

Sidestream dark field (SDF) imaging is a tool for assessing microcirculation, commonly used for early diagnosis and monitoring of sepsis. In this study, we used SDF imaging to observe and assess the microcirculation of the intestinal mucosa during bowel surgery. We also compared different performance between normal mucosa and diseased mucosa using SDF imaging. SDF imaging was conducted in 13 patients to evaluate microcirculation parameters. All patients were assessed at distances of 0, 1, 2, 3 and 4 centimeters (cm) from the edge of the mesentery, respectively. Microcirculatory parameters such as microvascular flow index (MFI), proportion of perfused vessels (PPV), vascular density (VD), total vessel density (TVD), perfused vessel density (PVD) and heterogeneity index (HI) were measured in these patients. Compared to normal intestinal mucosa, the diseased intestinal mucosa exhibited higher values for VD (p = 0.044), TVD (p = 0.006) and PVD (p = 0.007). No significant differences in PPV, MFI and HI were observed between the two groups. The microcirculation parameters (MFI, PPV and PVD) of the intestine at the distal distance of 3 cm were significantly lower than those at a distance of 2 cm (MFI 1.5 (0.75) vs. 3 (0.5), PPV 57.6 (9.1) vs. 97.1 (8.6)% and PVD 11.395 (3.082) vs. 20.726 (4.115) mm/mm2). In conclusion, SDF imaging is an advanced technique that provide real‐time visualization of intestinal mucosal microcirculation. It has the potential to assess the blood perfusion of the intestine during surgery. [ABSTRACT FROM AUTHOR]