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This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.

BackgroundFungal pathogens significantly impact the quality of fruits and vegetables at different stages of the supply chain, leading to substantial food losses. Understanding how these persistent fungal infections occur and progress in postharvest conditions is essential to developing effective control strategies.ResultsIn this study, we developed a reliable and consistent inoculation protocol to simulate disease spread from infected fruits to adjacent healthy fruits during postharvest storage. We tested different combinations of relevant fruit commodities, including oranges, tomatoes, and apples, against impactful postharvest pathogens such as Penicillium digitatum, Penicillium italicum, Botrytis cinerea, and Penicillium expansum. We assessed the efficacy of this protocol using fruits treated with various postharvest methods and multiple isolates for each pathogen. We optimized the source of infected tissue and incubation conditions for each fruit-pathogen combination. Disease incidence and severity were quantitatively evaluated to study infection success and progression. At the final evaluation point, 80% or higher disease incidence rates were observed in all trials except for the fungicide-treated oranges inoculated with fungicide-susceptible Penicillium spp. isolates. Although disease incidence was lower in that particular scenario, it is noteworthy that the pathogen was still able to establish itself under unfavorable conditions, indicating the robustness of our methodology. Finally, we used multispectral imaging to detect early P. digitatum infections in oranges before the disease became visible to the naked eye but after the pathogen was established.ConclusionsWe developed a non-invasive inoculation strategy that can be used to recreate infections caused by contact or nesting in postharvest. The observed high disease incidence and severity values across fruit commodities and fungal pathogens demonstrate the robustness, efficacy, and reproducibility of the developed methodology. The protocol has the potential to be tailored for other pathosystems. Additionally, this approach can facilitate the study of fruit-pathogen interactions and the assessment of innovative control strategies.

Over the past decades, liquid biopsy, especially circulating tumor DNA (ctDNA), has received tremendous attention as a noninvasive detection approach for clinical applications, including early diagnosis of cancer and relapse, real-time therapeutic efficacy monitoring, potential target selection and investigation of drug resistance mechanisms. In recent years, the application of next-generation sequencing technology combined with AI technology has significantly improved the accuracy and sensitivity of liquid biopsy, enhancing its potential in solid tumors. However, the increasing integration of such promising tests to improve therapy decision making by oncologists still has complexities and challenges. Here, we propose a conceptual framework of ctDNA technologies and clinical utilities based on bibliometrics and highlight current challenges and future directions, especially in clinical applications such as early detection, minimal residual disease detection, targeted therapy, and immunotherapy. We also discuss the necessities of developing a dynamic field of translational cancer research and rigorous clinical studies that may support therapeutic strategy decision making in the near future. [ABSTRACT FROM AUTHOR]

This study aimed to determine the effectiveness of a designed training program for nurses toward early detection of developmental disabilities among children aged 0–3 years. A group of 21 licensed nurses with professional experience ranging from 5–11 years participated in the study. The participants completed the measurements to evaluate their current knowledge, practice, and perception pre- and post-training program, as well as during a follow-up, in relation to early detection of disabilities. The results showed highly statistically significant difference between the studied nurses' total knowledge, perception, and practice in pre- and post-program application (p = 0.01). However, there was no statistically significant difference between the post-program and follow-up application (p = 0.180). [ABSTRACT FROM AUTHOR]

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

• Trees affected by green attack were identified within a limited time window. • The detection rates increased from 0.3 to 0.8 two weeks before brood emergence began. • The highest separability was found in the green shoulder region (490–550 nm). • Green shoulder infection/curvature points shifted during vitality decline. • Indices were developed with higher accuracy and simplified with three bands. Forest stress monitoring and in-time identification of forest disturbances are important to improve forest resilience to climate change. Fast-developing drone techniques and hyperspectral imagery provide tools for understanding the forest decline process under stress and contribute to focused monitoring. This study explored and developed hyperspectral drone imagery for early detection of forest stress caused by European spruce bark beetle Ips typographus (L.), before offspring emergence, which is crucial in controlling the spread but has been shown to be challenging. This study challenges the highest possible detectability of infested trees using a hyperspectral drone system that provided images with very high spectral, spatial, and temporal resolutions in Southern Finland. Images were acquired bi-weekly, four times (T1, T2, T3, T4), covering 8 weeks from trees being attacked by the first filial generation (F1) to the beginning of second filial generation (F2) brood emergence. Very low separability was observed for the reflectance from healthy and attacked trees, but the first and second derivative reflectance captured vitality changes, with the green shoulder region (wavelengths 490–550 nm) exhibiting the highest separability of all wavelengths (400–1700 nm). We discovered that the peak and valley values of the first and second derivative curves in the green shoulder region consistently shifted with longer infestation time. Based on this finding, we developed green shoulder indices. The detection rates were 0.24–0.31 and 0.76–0.83 for T3 and T4, higher than commonly used VIs such as the Photochemical Reflectance Index and the Red Edge Inflection Position, with detection rates of 0.69 and 0.34 for T4, respectively. We also proposed simplified green shoulder indices using the reflectance from three bands that can be used with multispectral cameras and satellite images for large area monitoring of forest health. We concluded that the detectability of infestations was very low for the first month after attack, and then rapidly increased before brood emergence. We highlighted the great potential of green shoulder indices in quantifying the photochemical functioning of the vegetation under stress. The methodology can potentially be applied for early identification of forests with declining vitality caused by various sources of forest stress and disturbances, such as infestations, diseases and drought. [ABSTRACT FROM AUTHOR]

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Background: The early diagnosis of pancreatic cancer (ICD-10 C25) can improve the patient's prognosis. The association between pancreatic cancer and type 2 diabetes (T2D) is known, but not yet fully understood. It is, therefore, necessary to investigate the impact of hemoglobin A1c (HbA1c) serum levels on pancreatic cancer development and the potential intervention options. Methods: In the case–control study, patients from the German IQVIATM Disease Analyzer database aged ≥18 years with a diagnosis of pancreatic cancer (ICD-10 C25) and a diagnosis of T2D (ICD-10: E11) were included. The patients' propensity score matched 1:5 with individuals without pancreatic cancer. Logistic regression models were used to estimate odds ratios (ORs) with 95% confidence intervals (95% CI). Results: An elevated serum HbA1c prior to the index date was found to be significantly associated with an increased risk of a subsequent pancreatic cancer diagnosis for the mean HbA1c values of 6.5–8.4% (OR: 1.38; 95% CI: 1.22–1.57) as well as for mean HbA1c values ≥8.5% (OR: 1.41; 95% CI: 1.16–1.73). The only antihyperglycemic agent negatively associated with the subsequent pancreatic cancer diagnosis was the sodium–glucose cotransporter-2 (SGLT-2) inhibitor, with an odds ratio of 0.80 (95% confidence interval: 0.74–0.87 per year of therapy). This correlation was observed in both age- and sex-stratified subgroups. Conclusions: The data indicate that elevated serum HbA1c levels in patients with T2D are associated with an increased risk of pancreatic cancer development. It is possible that SGLT2 therapy may prove an effective means of reducing the risk of pancreatic cancer, thereby offering a potential avenue for the future reduction in pancreatic cancer incidence in patients with T2D. [ABSTRACT FROM AUTHOR]

This comprehensive review offers a thorough examination of fetal heart rate (fHR) monitoring methods, which are an essential component of prenatal care for assessing fetal health and identifying possible problems early on. It examines the clinical uses, accuracy, and limitations of both modern and traditional monitoring techniques, such as electrocardiography (ECG), ballistocardiography (BCG), phonocardiography (PCG), and cardiotocography (CTG), in a variety of obstetric scenarios. A particular focus is on the most recent developments in textile-based wearables for fHR monitoring. These innovative devices mark a substantial advancement in the field and are noteworthy for their continuous data collection capability and ergonomic design. The review delves into the obstacles that arise when incorporating these wearables into clinical practice. These challenges include problems with signal quality, user compliance, and data interpretation. Additionally, it looks at how these technologies could improve fetal health surveillance by providing expectant mothers with more individualized and non-intrusive options, which could change the prenatal monitoring landscape. [ABSTRACT FROM AUTHOR]

Background: The largest identified kindred worldwide with a single mutation causing autosomal-dominant Alzheimer's disease (ADAD) is a family from Antioquia, Colombia, carrying the Presenilin-1 (PSEN1) E280A (Paisa) mutation. The majority of mutation carriers develop dementia, typically commencing in their late 30 s, with a median onset age of 49 years. Cognitive decline is a hallmark feature. Objective: This review synthesizes the existing literature on neuropsychological assessments in PSEN1 E280A mutation carriers throughout their lifespan. We provide a comprehensive overview of cognitive outcomes in this unique population. Methods: We reviewed and integrated the published research, analyzing studies on neuropsychological assessments in PSEN1 E280A carriers. Our focus was on measures of verbal, semantic, episodic, and spatial memory, and encompassed other cognitive domains such as language, attention, visuospatial memory, and executive functioning. Results: Verbal, semantic, episodic, and spatial memory emerged as the most sensitive indicators of preclinical changes in PSEN1 E280A carriers. Inconsistencies were noted in findings from tests assessing language, attention, visuospatial memory, and executive functioning, suggesting potential limitations in detecting early cognitive changes in PSEN1 mutation carriers. Specific cognitive tasks developed for this population proved effective but underutilized. Conclusions: The review underscores the importance of continued test development tailored to detect early cognitive changes in PSEN1 E280A carriers, potentially enhancing ADAD screening. Furthermore, investigating ADAD mutations in children may identify early changes in AD and enhance our understanding of neuropsychological functioning across the lifespan. This synthesis provides valuable insights for researchers, clinicians, and policymakers engaged in the study and management of ADAD. [ABSTRACT FROM AUTHOR]

Background: Coronary heart disease (CHD) and stroke have become the leading cause of premature mortality and morbidity worldwide. Therefore, sensitive and accurate biomarkers for early detection of CHD and stroke are urgently needed for effective prevention and treatment. We aim to investigate the association between blood-based HYAL2 methylation and the risk of CHD and stroke in Chinese population. Methods: In a prospective nested case–control study comprising 171 CHD cases, 139 stroke cases, who developed the diseases after recruitment and 356 controls who remained healthy during the 2.5 years of follow-up time, the methylation level of HYAL2 in the peripheral blood was quantified using mass spectrometry, and the association was calculated by logistic regression adjusted for covariant. Results: Significant association between HYAL2 methylation in the peripheral blood and increased risk of preclinical CHD and stroke were identified [odds ratios (ORs) per − 10% methylation: 1.35–1.64, p ≤ 0.045 for HYAL2_CpG_1, HYAL2_CpG_2 and HYAL2_CpG_3 in CHD; ORs per − 10% methylation: 0.76–1.64, p ≤ 0.033 for HYAL2_CpG_2 and HYAL2_CpG_4 in stroke]. The association in CHD was further enhanced by female gender, younger age (< 70 years old), without the history of hypertension and cancer. The combination of four HYAL2 methylation sites showed an effective discrimination of CHD and stroke cases without hypertension from controls [area under curve (AUC) = 0.78 and 0.75, respectively]. Conclusions: This study presents a strong association of altered HYAL2 methylation in peripheral blood with preclinical CHD and stroke, providing a novel biomarker for risk assessment and early detection of cardiovascular diseases. [ABSTRACT FROM AUTHOR]

This study aimed to assess the potential of home monitoring using a monitoring application for the early prediction of acute exacerbations (AEs) in patients with fibrosing interstitial lung diseases (F-ILDs) by tracking symptoms, peripheral blood oxygen saturation (SpO2), and heart rate (HR). Data on symptoms, SpO2, and HR before and after a 1-min sit-to-stand test (1STST) were collected using an online home monitoring application. Symptoms were recorded at least 3 times a week, including cough intensity and frequency (Cough Assessment Test scale (COAT) score), breathlessness grade (modified Medical Research Council (mMRC) score), and SpO2 and HR before and after 1STST. Eighty-five patients with stable F-ILDs were enrolled. We observed a significant increase in COAT and mMRC scores, alongside a significant decrease in SpO2 before and after 1STST, 2 weeks before the first recorded AE. Furthermore, a combination of variables-an increase in COAT (≥ 4) and mMRC(≥ 1) scores, a decrease in SpO2 at rest (≥ 5%), and a decrease in SpO2 after 1STST (≥ 4%)- proved the most effective in predicting AE onset in patients with F-ILDs at 2 weeks before the first recorded AE. Home telemonitoring of symptoms, SpO2 holds potential value for early AE detection in patients with F-ILDs. [ABSTRACT FROM AUTHOR]

Forest ecosystems are long-term exposed to dry periods in Europe, which leads to a significant loss of vitality and higher mortality, especially in coniferous forests. Identifying stress in the early stages when measures can be taken to protect the forest and living trees is crucial. Current detection methods are based on field surveys by forest workers or remote sensing methods to cover larger areas, which use changes in spectral reflectance of the forest canopy. In some cases, the attacked trees do not change their appearance, and based on calculations of vegetation indices from remote sensing data, the attack cannot be mapped. We present an innovative methodology based on non-optical analysis, namely identifying a group of volatile compounds and microclimate signs in forest stands that indicate stress factors in forest stands. An attacked tree by a bark beetle produces increased amounts of biogenic volatile organic compounds associated with defense, and the microclimate changes due to interrupted transpiration. In addition, the bark beetle uses the aggregation pheromone to attract more individuals and to attack the tree massively. In this study, we tested three electronic noses (Miniature Bosch sensor device with 25,419 samples, Sensory device for environmental applications with 193 samples, Handheld VOC Detector Tiger with 170 samples) in a freshly infested spruce stand. The measurement was conducted at ground level with the help of a human operator and was repeated six times to verify the detection capability of the electronic noses. To verify the capability of electronic noses to predict tree infestation, we used machine learning Random Forest. The results demonstrated that electronic noses can detect bark beetle infestation start (within 1 week of the first attack). The Miniature Bosch sensor device achieved the highest accuracy with a value of 95%, in distinguishing forest sections that are healthy and infested; the second most accurate electronic nose is the Sensory device for environmental applications, with an accuracy of 89%. Our proposed methodology could be used to detect bark beetle presence. [ABSTRACT FROM AUTHOR]

Background: Oral cancers, which include tumors of the oral cavity, salivary glands, and pharynx, are becoming increasingly prevalent worldwide. Squamous cell carcinoma accounts for over 90% of malignant oral lesions, with oral squamous cell carcinoma (OSCC) being notably common in the Indian subcontinent and other regions of Asia. This is especially true in South-Central Asia, including Sri Lanka, where it is particularly prevalent among men. This study aims to evaluate the levels of Vascular Endothelial Growth Factor-A (VEGF-A) and Cytokeratin-19 (CK-19) mRNAs in whole blood as a potential method for the early detection of OSCC. Methods: The study included 40 patients (each from OSCC, Oral Submucous Fibrosis (OSF), Oral Leukoplakia (OLK), Oral Lichen Planus (OLP), and 10 healthy controls. The expression levels of VEGF-A and CK-19 mRNAs were measured from extracellular RNA extracted from whole blood samples using real-time reverse transcription polymerase chain reaction (RT-PCR) with sequence-specific primers. Receiver operating characteristic (ROC) curve analysis was used to evaluate the effectiveness of these biomarkers in detecting OSCC. Results: The results demonstrated a significant increase in blood transcripts of the candidate mRNAs CK-19 and VEGF-A in patients with OSCC, OSF, OLK, and OLP. The Wilcoxon signed-rank test revealed a p-value of 0.002 for each specific comparison between diseased patients and healthy controls (i.e., OSCC vs. HC, OSF vs. HC, OLP vs. HC, OLK vs. HC) for both CK-19 and VEGF-A. When these two biomarkers were used together, they provided a 60% predictive probability for patients with OSCC (p = 0.023). Conclusion: This study highlights the efficacy of blood mRNA transcriptome diagnostics in detecting OSCC. This innovative clinical approach has the potential to be a robust, efficient, and reliable tool for early cancer detection. Blood-based transcriptomes could be further explored for their effectiveness in various health contexts and for routine health monitoring. [ABSTRACT FROM AUTHOR]

Background: Nasopharyngeal carcinoma (NPC) is diagnosed relatively late and has a poor prognosis, requiring early detection to reduce the disease burden. This diagnostic test accuracy meta-analysis evaluated the serological diagnostic value of nine EBV-related IgA antibody panels (EBNA1-IgA, VCA-IgA, EA-IgA, Zta-IgA, EBNA1-IgA + VCA-IgA, VCA-IgA + EA-IgA, VCA-IgA + Rta-IgG, EBNA1-IgA + VCA-IgA + Zta-IgA and VCA-IgA + EA-IgA + Rta-IgG), aiming to identify suitable serological detection biomarkers for NPC screening. Methods: PubMed, Embase, China National Knowledge Infrastructure and Chinese BioMedical Literature Database were searched from January 1st, 2000 to September 30th, 2023, with keywords nasopharyngeal carcinoma, IgA, screening, early detection, early diagnosis, sensitivity and specificity. Articles on the diagnostic value of serum EBV-related IgA antibody panels for NPC were included. Study selection, data extraction, and quality assessment were performed independently by two researchers, and a third researcher was consulted in the case of disagreement. Bivariate models were used for statistical analysis. The quality of included studies was evaluated through Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2). Results: A total of 70 articles were included, involving 11 863 NPC cases and 34 995 controls. Among the nine EBV-related IgA antibody panels, EBNA1-IgA + VCA-IgA [0.928 (0.898, 0.950)], VCA-IgA + Rta-IgG [0.925 (0.890, 0.949)], EBNA1-IgA + VCA-IgA + Zta-IgA [0.962 (0.909, 0.985)] and VCA-IgA + EA-IgA + Rta-IgG [0.945 (0.918, 0.964)] demonstrated higher pooled sensitivity (95%CI). In terms of diagnostic odds ratio (DOR) (95%CI), EBNA1-IgA + VCA-IgA [107.647 (61.173, 189.430)], VCA-IgA + Rta-IgG [105.988 (60.118, 186.857)] and EBNA1-IgA + VCA-IgA + Zta-IgA [344.450 (136.351, 870.153)] showed superior performance. Additionally, the SROC curves for EBNA1-IgA + VCA-IgA and VCA-IgA + Rta-IgG were more favorable. However, publication bias was detected for VCA-IgA (P = 0.005) and EBNA1-IgA + VCA-IgA (P = 0.042). Conclusions: In general, parallel detection of serum EBNA1-IgA, VCA-IgA and Zta-IgA antibodies using ELISA demonstrates better pooled sensitivity and DOR among the studied panels. In the cases where fewer indicators are used, serum VCA-IgA and EBNA1-IgA/Rta-IgG antibody panel exhibits a comparable performance. Trial registration: The International Prospective Register of Systematic Reviews registration number: CRD42023426984, registered on May 28, 2023. [ABSTRACT FROM AUTHOR]

Due to its decade‐long progression, colorectal cancer (CRC) is most suitable for population screening to achieve a significant reduction in its incidence and mortality. DNA methylation has emerged as a potential marker for the early detection of CRC. However, the current mainstream methylation detection method represented by bisulfite conversion has issues such as tedious operation, DNA damage, and unsatisfactory sensitivity. Herein, a new high‐performance CRC screening tool based on the promising specific terminal‐mediated polymerase chain reaction (STEM‐PCR) strategy is developed. CRC‐related methylation‐specific candidate CpG sites are first prescreened through The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases using self‐developed bioinformatics. Next, 9 homebrew colorectal cancer DNA methylated STEM‒PCR assays (ColoC‐mSTEM) with high sensitivity (0.1%) and high specificity are established to identify candidate sites. The clinical diagnostic performance of these selected methylation sites is confirmed and validated by a case‐control study. The optimized diagnostic model has an overall sensitivity of 94.8% and a specificity of 95.0% for detecting early‐stage CRC. Taken together, ColoC‐mSTEM, based on a single methylation‐specific site, is a promising diagnostic approach for the early detection of CRC which is perfectly suitable for the screening needs of CRC in primary healthcare institutions. [ABSTRACT FROM AUTHOR]

Detecting early plant diseases autonomously poses a significant challenge for self-navigating robots and automated systems utilizing Artificial Intelligence (AI) imaging. For instance, Botrytis cinerea, also known as gray mold disease, is a major threat to agriculture, particularly impacting significant crops in the Cucurbitaceae and Solanaceae families, making early and accurate detection essential for effective disease management. This study focuses on the improvement of deep learning (DL) segmentation models capable of early detecting B. cinerea on Cucurbitaceae crops utilizing Vision Transformer (ViT) encoders, which have shown promising segmentation performance, in systemic use with the Cut-and-Paste method that further improves accuracy and efficiency addressing dataset imbalance. Furthermore, to enhance the robustness of AI models for early detection in real-world settings, an advanced imagery dataset was employed. The dataset consists of healthy and artificially inoculated cucumber plants with B. cinerea and captures the disease progression through multi-spectral imaging over the course of days, depicting the full spectrum of symptoms of the infection, ranging from early, non-visible stages to advanced disease manifestations. Research findings, based on a three-class system, identify the combination of U-Net++ with MobileViTV2-125 as the best-performing model. This model achieved a mean Dice Similarity Coefficient (mDSC) of 0.792, a mean Intersection over Union (mIoU) of 0.816, and a recall rate of 0.885, with a high accuracy of 92%. Analyzing the detection capabilities during the initial days post-inoculation demonstrates the ability to identify invisible B. cinerea infections as early as day 2 and increasing up to day 6, reaching an IoU of 67.1%. This study assesses various infection stages, distinguishing them from abiotic stress responses or physiological deterioration, which is crucial for accurate disease management as it separates pathogenic from non-pathogenic stress factors. The findings of this study indicate a significant advancement in agricultural disease monitoring and control, with the potential for adoption in on-site digital systems (robots, mobile apps, etc.) operating in real settings, showcasing the effectiveness of ViT-based DL segmentation models for prompt and precise botrytis detection. [ABSTRACT FROM AUTHOR]

The tiger longicorn beetle, Xylotrechus chinensis Chevrolat (Coleoptera: Cerambycidae), has posed a significant threat to mulberry trees in Greece since its invasion in 2017, which may be associated with global warming. Detection typically relies on observing adult emergence holes on the bark or dried branches, indicating severe damage. Addressing pest threats linked to global warming requires efficient, targeted solutions. Remote sensing provides valuable, swift information on vegetation health, and combining these data with machine learning techniques enables early detection of pest infestations. This study utilized airborne multispectral data to detect infestations by X. chinensis in mulberry trees. Variables such as mean NDVI, mean NDRE, mean EVI, and tree crown area were calculated and used in machine learning models, alongside data on adult emergence holes and temperature. Trees were classified into two categories, infested and healthy, based on X. chinensis infestation. Evaluated models included Random Forest, Decision Tree, Gradient Boosting, Multi-Layer Perceptron, K-Nearest Neighbors, and Naïve Bayes. Random Forest proved to be the most effective predictive model, achieving the highest scores in accuracy (0.86), precision (0.84), recall (0.81), and F-score (0.82), with Gradient Boosting performing slightly lower. This study highlights the potential of combining remote sensing and machine learning for early pest detection, promoting timely interventions, and reducing environmental impacts. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, and the need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. Addressing these challenges and fostering collaboration among AI experts, clinicians, and researchers is crucial for developing equitable and effective AI applications. The future of AI in HNC holds significant promise, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes. [ABSTRACT FROM AUTHOR]

The genus Agrilus is one of the most diverse insect genera worldwide. The larval feeding activity causes extensive damage in both forests and orchards. In addition, more than 30 species have been introduced outside their native range so far, including the emerald ash borer Agrilus planipennis Fairmaire. Thus, the availability of efficient trapping protocols for early detection of Agrilus species at entry points is of utmost importance. In this study we tested whether trapping protocols developed for surveillance of A. planipennis in North America were also effective for other Agrilus species. In particular, through a multi-country assessment we compared the efficacy of detecting Agrilus species on: (i) green glue-coated prism traps vs. green Fluon-coated multi-funnel traps when baited with the green leaf volatile (Z)-3-hexenol or left unbaited; and (ii) green multi-panel traps vs. green multi-panel traps baited with dead adult Agrilus beetles (decoys). A total of 23,481 individuals from 45 Agrilus species were caught. Trap design significantly affected both species richness and abundance of Agrilus species in several of the countries where the trapping experiments were carried out, and green prism traps outperformed green multi-funnel traps in most cases. On the contrary, the addition of a (Z)-3-hexenol lure or dead adult beetle decoys on to traps did not improve trap catches. Our study highlights that reliable trap models to survey Agrilus species are already available, but also that there is the clear need to further investigate chemical ecology of Agrilus species to develop semiochemical lures that can improve detection efficacy. [ABSTRACT FROM AUTHOR]

Background: Monitoring patient outcomes and influencing planning for future service provision requires accurate and trustworthy assessments of the COVID-19 epidemiology in critical care. This research sought to pinpoint specific characteristics in COVID-19 patients upon arrival at the hospital that could indicate their likelihood of becoming critically ill. Methods: This study is a retrospective cohort study that examined the records of 130 COVID-19 patients hospitalized between October 2021 and February 2022. The data was retrieved from the official hospital records, with a focus on predicting risk factors for critical illness in COVID-19 patients. Skilled doctors double-checked the data and extracted the variables needed to predict the risk characteristics of COVID-19 from the validation cohort. The study used the area under the receiver operating characteristic curve (AUC) to evaluate how well the identified risk factors for COVID-19 could predict disease severity. Results: The most common co-morbidities in all patients were diabetes mellitus, hypertension, cardiac diseases, and COPD. The most frequently reported symptoms across all patients were dyspnea, cough, fatigue, body aches, expectoration, and headache. The CO-RADS assessment revealed a correlation between higher scores, particularly CO-RADS 5, and the severity of COVID-19 pneumonia. Chest CT scans showed significantly more extensive lung involvement in non-survivors compared to survivors. Laboratory results revealed significantly higher levels of lymphopenia, IL6, and procalcitonin in non-survivors than survivors (p<0.001). Conclusions: The study concluded that cardiac and renal diseases were prevalent among non-survivors of COVID-19 patients. Significant elevations in IL-6, procalcitonin, and ferritin levels were observed in nonsurvivors, highlighting their potential as biomarkers for disease severity. Non-survivors exhibited greater lung involvement and higher CO-RADS scores, along with elevated levels of IL-6 and procalcitonin with lymphopenia. Additionally, vomiting and diarrhea were markedly higher in non-survivors. Early identification of these high-risk patients is crucial for optimizing patient care and resource allocation. [ABSTRACT FROM AUTHOR]

Prevalence estimates of Parkinson's disease (PD)—the fastest-growing neurodegenerative disease—are generally underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating the gap between patient symptoms and diagnosis. Early-stage PD patients (n = 82) and age-matched comparison participants (n = 50) completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) random forest classification model (AUC = 0.92) that performed well across environmental and platform contexts. These findings provide robust support for further exploration of consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance. [ABSTRACT FROM AUTHOR]

Borderline personality disorder is a common, treatable condition that usually presents in late adolescence or early adulthood. Patients with borderline personality disorder are disproportionately represented in many clinical settings. Early identification and intervention of borderline personality disorder could help address the current mental health affecting young adults. College and university mental health settings have an opportunity to identify borderline personality disorder and to help guide students and families to appropriate treatment. College-based clinicians also have a role in educating campus administrators who may have little or no familiarity with standard borderline personality disorder symptoms or the trajectory of the disorder. [ABSTRACT FROM AUTHOR]

Objective: Routine health care visits offer the opportunity to screen older adults for symptoms of Alzheimer's disease (AD). Many women see their gynecologist as their primary health care provider. Given this unique relationship, the Women's Preventive Services Initiative and the American College of Obstetrics and Gynecology advocate for integrated care of women at all ages. It is well-established that women are at increased risk for AD, and memory screening of older women should be paramount in this effort. Research is needed to determine the feasibility and value of memory screening among older women at the well-woman visit. Materials and Methods: Women aged 60 and above completed a 5-item subjective memory screener at their well-woman visit at the Columbia University Integrated Women's Health Program. Women who endorsed any item were considered to have a positive screen and were given the option to pursue clinical evaluation. Rates of positive screens, item endorsement, and referral preferences were examined. Results: Of the 530 women approached, 521 agreed to complete the screener. Of those, 17.5% (n = 91) were classified as positive. The most frequently endorsed item was difficulty with memory or thinking compared with others the same age. Among women with positive screens, 57.5% were interested in pursuing clinical referrals to a memory specialist. Conclusion: Results support the feasibility and potential value of including subjective memory screening as part of a comprehensive well-woman program. Early identification of memory loss will enable investigation into the cause of memory symptoms and longitudinal monitoring of cognitive change. [ABSTRACT FROM AUTHOR]

The invasive fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), poses a significant global threat with serious economic implications. Global concern heightened following the first major outbreak in Africa in 2016. Our research, which confirmed the presence of FAW in Indonesia in 2015 through re-examined samples from a maize field in Lampung region of Indonesia, supports the hypothesis that the invasion pattern does not follow the predicted west-to-east chronological timeline. Historical records and reidentification suggest that FAW may have been established in Indonesia, Africa, and Australia as early as nineteenth century, suggesting its potential presence in these regions much earlier than previously thought. The spread of FAW could be linked to the movement of people and plants such as maize or Para grass (Brachiaria mutica), at least at the end of the nineteenth century. This underscores the importance of rigorous identification methods for accurately tracking invasive species like FAW. Analyzing these historical records alongside the genetic structure of populations in Southeast Asia, particularly in Indonesia, is crucial for future research aimed at developing effective management strategies to mitigate the impact on the global agriculture. [ABSTRACT FROM AUTHOR]

Summary: Breast cancer is the most prevalent kind of tumor to occur in females and the primary cause of death for women. Early detection is perhaps the most successful strategy to minimize breast cancer mortality. Early diagnosis necessitates a consistent and efficient diagnostics method that allows doctors to differentiate benign from malignant breast cancers without a surgical sample. The goal of this endeavor is to develop a sophisticated breast cancer diagnosis method. The primary goal of the paper is to reduce the death rate among women by promoting early detection of breast cancer. First, pre‐processing techniques such as median filtering and contrast limiting adaptive histogram equalization are used to the obtained raw images. By doing this, the machine‐learning model's computational complexity is decreased and the image quality is enhanced. K‐means clustering is used to segregate the pre‐processed image. Additionally, features including the enhanced local vector pattern, grey‐level co‐occurrence matrix and local vector patterns are produced in the course of the feature extraction stage. Finally, an optimized deep belief network (DBN) is carrying out the classification process. To boosts the classification accuracy, activation function of DBN (tanh, softmax, ReLu) as well as its weight function is optimized by the proposed grey wolf updated whale optimization algorithm The accuracy of the greywolf updated whale optimization algorithm+DBN is above 93% for datasets 1 and 2 when compared to extant models. Finally, calculation of the performance validates the proposed model's performance. [ABSTRACT FROM AUTHOR]

Introduction: Lung Cancer (LC) continues to be a leading cause of cancer-related mortality globally, largely due to the asymptomatic nature of its early stages and the limitations of current diagnostic methods such as Low-Dose Computed Tomography (LDCT), whose often result in late diagnosis, highlighting an urgent need for innovative, minimally invasive diagnostic techniques that can improve early detection rates. Areas covered: This review delves into the potential of genomic characterization and mutational profiling to enhance early LC diagnosis, exploring the current state and limitations of traditional diagnostic approaches and the revolutionary role of Liquid Biopsies (LB), including cell-free DNA (cfDNA) analysis through fragmentomics and methylomics. New genomic technologies that allow for earlier detection of LC are scrutinized, alongside a detailed discussion on the literature that shaped our understanding in this field. Expert opinion: Despite the promising advancements in genomic characterization techniques, several challenges remain, such as the heterogeneity of LC mutations, the high cost, and limited accessibility of Next-Generation Sequencing (NGS) technologies. Additionally, there is a critical need of standardized protocols for interpreting mutational data. Future research should focus on overcoming these barriers to integrate these novel diagnostic methods into standard clinical practice, potentially revolutionizing the management of LC patients. [ABSTRACT FROM AUTHOR]

Background: To establish and validate a machine learning model using pretreatment multiparametric magnetic resonance imaging‐based radiomics data with clinical data to predict radiation‐induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity‐modulated radiotherapy (IMRT). Methods: Data from 230 patients with NPC who received IMRT (130 with RTLI and 130 without) were randomly divided into the training (n = 161) and validation cohort (n = 69) with a ratio of 7:3. Radiomics features were extracted from pretreatment apparent diffusion coefficient (ADC) map, T2‐weighted imaging (T2WI), and CE‐T1‐weighted imaging (CE‐T1WI). T‐test, spearman rank correlation, and least absolute shrinkage and selection operator (LASSO) algorithm were employed to identify significant radiomics features. Clinical features were selected with univariate and multivariate analyses. Radiomics and clinical models were constructed using multiple machine learning classifiers, and a clinical‐radiomics nomogram that combined clinical with radiomics features was developed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical‐radiomics nomogram. Results: A total of 5064 radiomics features were extracted, from which 52 radiomics features were selected to construct the radiomics signature. The AUC of the radiomics signature based on multiparametric MRI was 0.980 in the training cohort and 0.969 in the validation cohort, outperforming the radiomics signature only based on T2WI and CE‐T1WI (p < 0.05), which highlighted the significance of the DWI sequence in the prediction of temporal lobe injury. The area under the curve (AUC) of the clinical model was 0.895 in the training cohort and 0.905 in the validation cohort. The nomogram, which integrated radiomics and clinical features, demonstrated an impressive AUC value of 0.984 in the validation set; however, no statistically significant difference was observed compared to the radiomics model. The calibration curve and decision curve analysis of the nomogram demonstrated excellent predictive performance and clinical feasibility. Conclusions: The clinical‐radiomics nomogram, integrating clinical features with radiomics features derived from pretreatment multiparametric MRI, exhibits compelling predictive performance for RTLI in patients diagnosed with NPC. [ABSTRACT FROM AUTHOR]

Endometrial cancer (EC) is the most prevalent gynaecological cancer in high‐income countries and its incidence is continuing to rise sharply. Simple and objective tools to reliably detect women with EC are urgently needed. We recently developed and validated the DNA methylation (DNAme)‐based women's cancer risk identification—quantitative polymerase chain reaction test for endometrial cancer (WID‐qEC) test that could address this need. Here, we demonstrate that the stability of the WID‐qEC test remains consistent regardless of: (i) the cervicovaginal collection device and sample media used (Cervex brush and PreservCyt or FLOQSwab and eNAT), (ii) the collector of the specimen (gynaecologist‐ or patient‐based), and (iii) the precise sampling site (cervical, cervicovaginal and vaginal). Furthermore, we demonstrate sample stability in eNAT medium for 7 days at room temperature, greatly facilitating the implementation of the test into diagnostic laboratory workflows. When applying FLOQSwabs (Copan) in combination with the eNAT (Copan) sample collection media, the sensitivity and specificity of the WID‐qEC test to detect uterine (i.e., endometrial and cervical) cancers in gynaecologist‐taken samples was 92.9% (95% confidence interval [CI] = 75.0%–98.8%) and 98.6% (95% CI = 91.7%–99.9%), respectively, whilst the sensitivity and specificity in patient collected self‐samples was 75.0% (95% CI = 47.4%–91.7%) and 100.0% (95% CI = 93.9%–100.0%), respectively. Taken together these data confirm the robustness and clinical potential of the WID‐qEC test. [ABSTRACT FROM AUTHOR]

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Brain tumors, whether benign or malignant, present a complex and multifaceted challenge in healthcare, affecting individuals across various age groups. Predicting the vulnerability of brain tumors using health risk factors and symptoms is crucial, yet there have been limited research studies, particularly those integrating artificial intelligence (AI) technology. This research explores machine learning models such as support vector machines (SVMs), multi-layer perceptrons (MLPs), and logistic regression (LR) for the early detection of brain tumors. Evaluation metrics, including accuracy, precision, recall, and F1-score, are employed to assess model performance. The results indicate that the SVM outperforms other models, providing a robust foundation for predictive accuracy. To enhance accessibility and usability, the research also integrates these models into a mobile application predictor. The application is beneficial for assisting individuals in early detection by identifying potential risk factors and symptoms that may lead to a brain tumor. In conclusion, the integration of machine learning through a mobile application represents a transformative approach to personalized healthcare. By empowering individuals with cutting-edge technology, this research strives to enhance early detection and decision-making regarding potential brain tumor risks and symptoms, ultimately contributing to improved patient outcomes and quality of life. [ABSTRACT FROM AUTHOR]

Effective management of invasive species often relies upon early detection and rapid implementation of management actions. Unfortunately, early detection can be hampered in coastal regions where sites may be challenging to access and survey or resources are limited. Conventionally considered a freshwater plant with a near-global invaded range, yellow flag iris (Iris pseudacorus) is sufficiently tolerant of salinity to disperse through seawater and successfully invade coastal habitats. Clarifying the characteristics that influence coastal habitat suitability for I. pseudacorus and identifying locations at risk of invasion is critical for monitoring and control efforts. We developed a Maxent species distribution model to inform the risk of coastal invasion by I. pseudacorus in southern British Columbia (Canada). Among the variables assessed, precipitation during the driest quarter was the most important contributor in predicting I. pseudacorus's coastal habitat suitability (40.6% variable contribution [VC]). Urban areas (24.9% VC), south-facing aspects (6.2% VC) and locations at elevations (7.9% VC) at or above the mean high tide extent were also considered suitable for establishment. Though proximity to perennial freshwater sources (7% VC) was also predicted to provide suitable habitat, close distance to these features was not found to be essential. Regionally, invasion hotspots were predicted in coastal habitats around Tofino/Ucluelet (western Vancouver Island), the Fraser River delta (metro Vancouver), and among the southern Gulf Islands and southern Vancouver Island (capital region). Land managers can use these variables of interest and geographic locations to inform early detection monitoring efforts that help attenuate the emerging threat of I. pseudacorus in coastal ecosystems. [ABSTRACT FROM AUTHOR]

The availability and linkage of comprehensive data on cancer mortality, incidence, screening and vaccination (HPV/HB) are essential for monitoring the process and impact of all screening and vaccination programmes, and to generate evidence to improve prevention policies. Linkage of data registries will allow better exploitation of real-world data. Simplification of data sharing for epidemiological research is of utmost importance. Human, as well as technical/technological and legal/regulatory barriers need to be tackled in Belgium to aid actors involved in screening, to facilitate organised linkage of data. Rapid advances in technologies for early detection and screening for a broad range of cancer types provide significant opportunities. Such tests are not yet ready for adoption in screening programmes. Emerging evidence should be monitored to ensure that promising innovations can transition to implementation studies in a timely manner. More and sustained research is needed to generate the necessary scientific relevance for new screening techniques in relevant cancer types. [ABSTRACT FROM AUTHOR]

Serial CA125 and second line transvaginal ultrasound (TVS) screening in the UKCTOCS indicated a shift towards detection of earlier stage ovarian cancer (OvCa), but did not yield a significant mortality reduction. There remains a need to establish additional biomarkers that can complement CA125 for even earlier and at a larger proportion of new cases. Using a cohort of plasma samples from 219 OvCa cases (59 stage I/II and 160 stage III/IV) and 409 female controls and a novel Sensitivity Maximization At A Given Specificity (SMAGS) method, we developed a blood-based metabolite-based test consisting of 7 metabolites together with CA125 for detection of OvCa. At a 98.5% specificity cutpoint, the metabolite test achieved sensitivity of 86.2% for detection of early-stage OvCa and was able to capture 64% of the cases with low CA125 levels (< 35 units/mL). In an independent test consisting of 65 early-stage OvCa cases and 141 female controls, the metabolite panel achieved sensitivity of 73.8% at a 91.4% specificity and captured 13 (44.8%) out of 29 early-stage cases with CA125 levels < 35 units/mL. The metabolite test has utility for ovarian cancer screening, capable of improving upon CA125 for detection of early-stage disease. [ABSTRACT FROM AUTHOR]

Background: Early detection of colorectal cancer (CRC) significantly enhances patient outcomes. Conventional CRC screening tools, like endoscopy and stool-based tests, have constraints due to their invasiveness or suboptimal patient adherence. Recently, liquid biopsy employing plasma cell-free DNA (cfDNA) has emerged as a potential noninvasive screening technique for various malignancies. Methods: In this research, we harnessed the Mutation Capsule Plus (MCP) technology to profile an array of genomic characteristics from cfDNA procured from a single blood draw. This profiling encompassed DNA methylation, the 5' end motif, copy number variation (CNV), and genetic mutations. An integrated model built upon selected multiomics biomarkers was trained using a cohort of 93 CRC patients and 96 healthy controls. Results: This model was subsequently validated in another cohort comprising 89 CRC patients and 95 healthy controls. Remarkably, the model achieved an area under the curve (AUC) of 0.981 (95% confidence interval (CI), 0.965–0.998) in the validation set, boasting a sensitivity of 92.1% (95% CI, 84.5%-96.8%) and a specificity of 94.7% (95% CI, 88.1%-98.3%). These numbers surpassed the performance of any single genomic feature. Importantly, the sensitivities reached 80% for stage I, 89.2% for stage II, and were 100% for stages III and IV. Conclusion: Our findings underscore the clinical potential of our multiomics liquid biopsy test, indicating its prospective role as a noninvasive method for early-stage CRC detection. This multiomics approach holds promise for further refinement and broader clinical application. [ABSTRACT FROM AUTHOR]

Nasopharyngeal carcinoma (NPC) arises from the mucosal epithelium of the nasopharynx and is frequently located in the pharyngeal crypts. This is a highly aggressive malignant tumor that frequently leads to distant metastases in many cases and poses a significant public health challenge, particularly in certain geographic regions globally. This review discusses the epidemiology, risk factors, diagnosis, and treatment options for NPC, emphasizing the importance of early detection and comprehensive management strategies in improving patient outcomes. Moreover, the article explores the intricate mechanisms that cause NPC. Comprehending these fundamental principles can assist in creating specific prevention and therapy approaches for NPC. Recent advances in diagnostic methods, including imaging tests and molecular biomarkers, are emphasized to improve early diagnosis and individualized treatment strategies for individuals with NPC. The review also explores the most recent advancements in treating early-stage (stage I and II) NPC patients, highlighting the changing landscape of individualized therapy approaches for this particular set of patients. [ABSTRACT FROM AUTHOR]

Protein glycosylation is the co‐ and/or post‐translational modification of proteins with oligosaccharides (glycans). This process is not template based and can introduce a heterogeneous set of glycan modifications onto substrate proteins. Glycan structures preserve biomolecular information from the cell, with glycoproteins from different cell types and tissues displaying distinct patterns of glycosylation. Several decades of research have revealed that glycan structures also differ between normal physiology and disease. This suggests that the information stored in glycoproteins and glycans can be utilized for disease diagnosis and monitoring. Methods that enable sensitive and site‐specific measurement of protein glycosylation in clinical settings, such as nano‐flow liquid chromatography tandem mass spectrometry, are therefore essential. The purpose of this perspective is to discuss recent advances in mass spectrometry and the potential of these advances to facilitate the detection and monitoring of disease‐specific glycoprotein glycoforms. Glycoproteomics, the system‐wide characterization of glycoprotein identity inclusive of site‐specific characterization of carbohydrate modifications on proteins, and glycomics, the characterization of glycan structures, will be discussed in this context. Quantitative measurement of glycopeptide markers via parallel reaction monitoring is highlighted. The development of promising glycopeptide markers for autoimmune disease, liver disease, and liver cancer is discussed. Synthetic glycopeptide standards, ambient ionization mass spectrometry, and consideration of glyco‐biomarkers in two‐ and three‐dimensional space within tissue will be critical to the advancement of this field. The authors envision a future in which glycoprotein mass spectrometry workflows will be integrated into clinical settings, to aid in the rapid diagnosis and monitoring of disease. [ABSTRACT FROM AUTHOR]

Simple Summary: Early diagnosis of lung lesions in calves is crucial for prompt treatment intervention and management of respiratory diseases. Both a physical and a transthoracic ultrasonography scoring at time of entry offers the opportunity to re-evaluate action plans in case of illness and further facilitate implementation of prophylactic management measures e.g., intranasal vaccination. The present study highlights the advantages of an early diagnosis of respiratory diseases on various production parameters of veal calf fattening and offers the opportunity to evaluate the effect of an intranasal vaccine for bovine respiratory diseases (Bovalto® Respi Intranasal). This study evaluated the significance and predictive value of ultrasonographic and physical examination on arrival at an Austrian fattening farm. Treatment frequency and average daily weight gain (ADG) were related to physical and ultrasonographic examination results. Additionally, the effect of an intranasal vaccination in half of the examined calves was studied. The clinical and ultrasonographic health status 600 calves was recorded at the beginning and end of fattening. Half of the calves received an intranasal vaccination (Bovalto® Respi Intranasal). Overall, 44.5% showed an abnormal respiratory scoring (RS) and 56.0% showed signs of respiratory diseases in transthoracic ultrasonography (TUS) at arrival on the farm. For both RS and TUS, a categorization between ILL and HEALTHY was conducted. Results showed lower ADG in ILL calves (RS median: 0.93 kg/d; TUS median: 0.96 kg/d) compared to HEALTHY calves (RS median: 1.01 kg/d; TUS median: 1.01 kg/d). The median ADG was lower in not treated and ILL calves (RS median 0.90 kg/d; TUS: 0.93 kg/d) compared to treated and ILL calves (RS median 1.01 kg/d; TUS: 1.02 kg/d). Vaccination did not affect growth performance or occurrence of ILL, though treatment frequency was lower in VAC calves (17.0% in NVAC; 11.3% in VAC). The implementation of examination protocols for respiratory diseases may have a positive impact on production parameters (e.g., treatment frequency and ADG). [ABSTRACT FROM AUTHOR]

Barrett's esophagus (BE) is a known precursor to esophageal adenocarcinoma (EAC). Guidelines recommend BE screening in populations with multiple risk factors, for which non-endoscopic esophageal cell collection with biomarker testing is considered as an acceptable alternative to esophagogastroduodenoscopy (EGD). The aim of this study was to evaluate analytical performance characteristics of EsoGuard® (EG), a DNA methylation biomarker assay, as a laboratory-developed test (LDT) in esophageal samples collected with the swallowable EsoCheck® (EC) device. EG is a next-generation sequencing (NGS) assay that evaluates methylated vimentin (VIM) and cyclin A1 (CCNA1), clinically validated biomarkers for the detection of BE and EAC. The studies were conducted according to standards of College of American Pathology (CAP), Clinical Laboratory Improvement Amendments (CLIA), and New York (NY) state requirements for the analytical validation of molecular assays. Comparison to Sanger sequencing showed that EG was 100% accurate at all 31 CpG sites evaluated by the assay. The analytical sensitivity, specificity, and accuracy of the assay were 89%, 100%, and 96%, respectively. Intra- and inter-assay precision was 100%. The limit of detection (LOD) was 1 in 400 methylated cells, and the reference range was 84%. In summary, EsoGuard demonstrates high analytical accuracy, repeatability, and reproducibility in samples collected using the EsoCheck device. [ABSTRACT FROM AUTHOR]

Penile cancer, while rare, is a critical public health issue due to its profound impact on patients and the complexities of its management. The disease's multifactorial etiology includes risk factors such as HPV infection, poor hygiene, smoking, genetic predispositions, and socioeconomic determinants. This article provides a comprehensive review and analysis of these diverse risk factors, aiming to enhance understanding of the disease's underlying causes. By elucidating these factors, the article seeks to inform and improve prevention strategies, early detection methods, and therapeutic interventions. A nuanced grasp of the multifactorial nature of penile cancer can enable healthcare professionals to develop more effective approaches to reducing incidence rates and improving patient outcomes. [ABSTRACT FROM AUTHOR]

Objective: To provide a brief summary of the high incidence, symptomatology, different types, and diagnosis of adenomyosis and to explore various aspects of the disease, with the primary aim of raising awareness among gynecologists for appropriate and early detection. Background: Adenomyosis, a benign gynecological condition characterized by the infiltration of endometrial tissue into the myometrium, poses significant challenges to women's reproductive health. Methods: A narrative review was conducted by searching PubMed, Scopus, and Cochrane databases and offering a non-systematic summary and critical analysis of current knowledge on the impact of adenomyosis on women's health. Articles published in the English language up to May 2023, including original scientific papers, clinical trials, meta-analyses, and reviews focusing on various aspects of adenomyosis, were included in the synthesis of this review. Conclusions: Approximately 20% of women are affected by adenomyosis, which manifests with various subtypes, distinct epidemiological profiles, symptomatology, and treatment responses. Despite its clinical significance, adenomyosis remains understudied, resulting in a significant disparity in research and the literature compared to other gynecological conditions. The severity of adenomyosis is compounded when coexisting with endometriosis, particularly deep-infiltrating endometriosis (DIE), leading to exacerbated fertility issues and severe symptomatology. The wide range of symptoms, including adverse pregnancy outcomes such as pre-eclampsia, highlights its wider impact and emphasizes the need for increased awareness of the condition. Adenomyosis is frequently associated with treatment failure in endometriosis, contributing to dienogest resistance, elevated discontinuation rates, and persistent pain post-endometriosis surgery. Additionally, the lack of specific treatments tailored to adenomyosis poses a considerable challenge in clinical management. [ABSTRACT FROM AUTHOR]

Cervical cancer screening is a critical public health measure, especially vital for underserved communities where disparities in access and outcomes are pronounced. Despite the life-saving potential of regular screening, numerous barriers--including geographical isolation, cultural and linguistic challenges, and socioeconomic factors--severely hinder accessibility for these populations. Multicancer early detection (MCED) tests emerge as a potentially effective intervention, offering a less invasive, more accessible approach that could transform how screenings are conducted. This paper explores the existing challenges in traditional cervical cancer screening methods, the potential of MCED tests to address these barriers, and the implications of these technologies for global health equity. Through a comprehensive review, we highlight the need for culturally sensitive, tailored interventions and the importance of effectively overcoming logistical and financial difficulties to implement MCED tests. Despite the promise shown by MCED tests, the paper acknowledges significant implementation challenges, including cost, logistical obstacles, and the need for cultural acceptance and validation studies. This study emphasizes the necessity for equitable MCED test implementation strategies, highlighting the potential of these innovative technologies to advance global health equity in cervical cancer prevention. [ABSTRACT FROM AUTHOR]

Prostate-Specific Antigen (PSA) based screening of prostate cancer (PCa) needs refinement. The aim of this study was the identification of urinary biomarkers to predict the Prostate Imaging—Reporting and Data System (PI-RADS) score and the presence of PCa prior to prostate biopsy. Urine samples from patients with elevated PSA were collected prior to prostate biopsy (cohort = 99). The re-analysis of mass spectrometry data from 45 samples was performed to identify urinary biomarkers to predict the PI-RADS score and the presence of PCa. The most promising candidates, i.e. SPARC-like protein 1 (SPARCL1), Lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1), Alpha-1-microglobulin/bikunin precursor (AMBP), keratin 13 (KRT13), cluster of differentiation 99 (CD99) and hornerin (HRNR), were quantified by ELISA and validated in an independent cohort of 54 samples. Various biomarker combinations showed the ability to predict the PI-RADS score (AUC = 0.79). In combination with the PI-RADS score, the biomarkers improve the detection of prostate carcinoma-free men (AUC = 0.89) and of those with clinically significant PCa (AUC = 0.93). We have uncovered the potential of urinary biomarkers for a test that allows a more stringent prioritization of mpMRI use and improves the decision criteria for prostate biopsy, minimizing patient burden by decreasing the number of unnecessary prostate biopsies. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is a significant global health challenge, ranking among the leading causes of cancer-related mortality worldwide. Despite efforts in prevention and early detection, CRC incidence and mortality rates are expected to rise substantially. Traditional screening methods like gFOBT, FIT, flexible sigmoidoscopy, colonoscopy, CTC, and colon capsule have limitations, including false positives/negatives, limited scope, or invasiveness. Recent developments in CRC screening involve DNA methylation biomarkers, showing promise in detecting early-stage CRC and precancerous lesions. Stool-based DNA testing is emerging as a noninvasive and convenient method for detecting CRC-associated DNA methylation alterations, offering potential for earlier detection compared to traditional methods. Several commercial stool-based DNA methylation tests targeting different genes associated with CRC have demonstrated varying sensitivity and specificity, some surpassing traditional screening methods. Challenges remain in optimizing their performance and accessibility. This review discusses how DNA methylation biomarkers could enhance CRC screening, and stool-based DNA methylation tests could revolutionize CRC screening practices, comparing them to the gold standard. [ABSTRACT FROM AUTHOR]

Background Early detection of breast cancer significantly improves treatment outcomes and survival rates. While mammography is the standard screening tool, breast MRI is increasingly being used due to its higher sensitivity. This study aims to compare the diagnostic accuracy of mammography and breast MRI in detecting early breast cancer. Methods A total of 100 women aged 35-70 years (mean age: 52) participated in this study. Each participant underwent both mammography and breast MRI. Diagnostic measures calculated included true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). From these, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were derived for both diagnostic methods. Results Mammography identified 60 TP, 25 TN, 10 FP, and 5 FN cases. The sensitivity of mammography was 92.3%, specificity was 71.4%, PPV was 85.7%, NPV was 83.3%, and accuracy was 85.0%. Breast MRI identified 62 TP, 30 TN, 5 FP, and 3 FN cases. The sensitivity of breast MRI was 95.4%, specificity was 85.7%, PPV was 92.5%, NPV was 90.9%, and accuracy was 92.0%. Comparative analysis showed that breast MRI had higher sensitivity, specificity, PPV, NPV, and overall accuracy than mammography. Conclusion Breast MRI outperforms mammography in detecting early breast cancer, exhibiting superior sensitivity, specificity, PPV, NPV, and accuracy. Breast MRI identified more true positive cases and had fewer false negatives and false positives. These results suggest that breast MRI may be a more reliable diagnostic tool for early breast cancer detection. However, factors such as cost, availability, and patient-specific circumstances should be considered when choosing the diagnostic method. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach to detect solar panel defects early, leveraging distinct datasets comprising aerial and electroluminescence (EL) images. The decision to employ separate datasets with different models signifies a strategic choice to harness the unique strengths of each imaging modality. Aerial images provide comprehensive surface-level insights, while electroluminescence images offer valuable information on internal defects. By using these datasets with specialized models, the study aims to improve defect detection accuracy and reliability. The research explores the effectiveness of modified deep learning models, including DenseNet121 and MobileNetV3, for analyzing aerial images, and introduces a customized architecture and EfficientNetV2B2 models for electroluminescence image analysis. Results indicate promising accuracies for DenseNet121 (93.75%), MobileNetV3 (93.26%), ELFaultNet (customized architecture) (91.62%), and EfficientNetV2B2 (81.36%). This study's significance lies in its potential to transform solar panel maintenance practices, enabling early defect identification and subsequent optimization of energy production. [ABSTRACT FROM AUTHOR]

Background: With the arrival of disease-modifying treatments, it is mandatory to find new cognitive markers that are sensitive to Alzheimer's disease (AD) pathology in preclinical stages. Objective: To determine the utility of a newly developed Learning and Associative Memory face test: LAM test. This study examined the relationship between AD cerebrospinal fluid (CSF) biomarkers and performance on LAM test, and assessed its potential clinical applicability to detect subtle changes in cognitively healthy subjects at risk for AD. Methods: We studied eighty cognitively healthy volunteers from the Valdecilla cohort. 61% were women and the mean age was 67.34 years (±6.416). All participants underwent a lumbar puncture for determination of CSF biomarkers and an extensive neuropsychological assessment, including performance on learning and associative memory indices of the LAM-test after 30 min and after 1 week, and two classic word lists to assess verbal episodic memory: the Rey Auditory Verbal Learning Test (RAVLT) and the Free and Cued Selective Reminding Test (FCSRT). We analyzed cognitive performance according to amyloid status (A+ versus A–) and to ATN model (A–T–N–; A+T–N–; A+T+N–/A+T+N+). Results: Performance on the LAM-test was significantly correlated with CSF Aβ ratio. A+ participants performed worse on both learning (mean difference = 2.19, p = 0.002) and memory LAM measures than A– (mean difference = 2.19, p = 0.004). A decline in performance was observed along the Alzheimer's continuum, with significant differences between ATN groups. Conclusions: Our findings suggest that LAM test could be a useful tool for the early detection of subjects within the AD continuum, outperforming classical memory tests. [ABSTRACT FROM AUTHOR]

Background: Breast cancer remains a pervasive threat to women worldwide, with increasing incidence rates necessitating effective screening strategies. Timely detection with mammography has emerged as the primary tool for mass screening. This retrospective study, which is part of the Chiraiya Project, aimed to evaluate breast lesion patients identified during opportunistic mammography screening camps in Jammu Province, India. Methods: A total of 1505 women aged 40 years and older were screened using a mobile mammographic unit over a five-year period, excluding 2020 and 2021 due to the COVID-19 pandemic. The inclusion criterion was women in the specified age group, while the exclusion criterion was women with open breast wounds, history of breast cancer or a history of breast surgery. The screening process involved comprehensive data collection using a detailed Proforma, followed by mammographic assessments conducted within strategically stationed mobile units. Radiological interpretations utilizing the BI-RADS system were performed, accompanied by meticulous documentation of patient demographics, habits, literacy, medical history, and breastfeeding practices. Participants were recruited through collaborations with NGOs, army camps, village panchayats, and urban cooperatives. Screening camps were scheduled periodically, with each camp accommodating 90 patients or fewer. Results: Among the 1505 patients, most were aged 45–50 years. The number of screenings increased yearly, peaking at 441 in 2022. The BI-RADS II was the most common finding (48.77%), indicating the presence of benign lesions, while the BI-RADS 0 (32.96%) required further evaluation. Higher-risk categories (BI-RADS III, IV, V) were less common, with BI-RADS V being the rarest. Follow-up adherence was highest in the BI-RADS III, IV, and V categories, with BI-RADS V achieving 100% follow-up. However, only 320 of 496 BI-RADS 0 patients were followed up, indicating a gap in continuity of care. The overall follow-up rate was 66.89%. Compared to urban areas, rural areas demonstrated greater screening uptake but lower follow-up rates, highlighting the need for tailored interventions to improve follow-up care access, especially in rural contexts. Conclusion: This study underscores the efficacy of a mobile mammographic unit in reaching marginalized populations. Adherence to screening protocols has emerged as a linchpin for early detection, improved prognosis, and holistic public health enhancement. Addressing misconceptions surrounding mammographic screenings, especially in rural settings, is crucial. These findings call for intensified efforts in advocacy and education to promote the benefits of breast cancer screening initiatives. Future interventions should prioritize improving access to follow-up care and addressing screening to enhance breast cancer management in Jammu Province. [ABSTRACT FROM AUTHOR]

Objectives: Because of high frequency of extreme weather, railway subgrade disaster shows a trend of increasing, great harmfulness and is hard to detect in advance. Even a small-size subgrade disaster may cause railway paralysis, which prevents the development of transportation. Many incidents illustrate that the earliest subgrade disaster always appeared in areas where no case ever reported before. It means that traditional monitoring methods are hard to detect hidden danger area. Thus, a promising method is ur1,2,3gent to be proposed for meeting the monitoring requirement for railway safety. Methods: First, the charac1,2,3teristics of subgrade disaster are summarized under different disaster-pregnant environments, and the advan1,2,3tages of various monitoring methods are discussed to find a collaborative applications method for early iden1,2,3tification of subgrade disaster. The potential hazards that may cause damage to subgrade structure and the deterioration degree of subgrade are considered as the main monitoring object of subgrade service status, in1,2,3cluding subgrade deformation monitoring, structural health monitoring, geological hazard monitoring along the railway, track irregularity, and external environmental monitoring. Second, an investigative approach based on the integration of space-air-train-ground muti-source techniques is proposed to detect the geohazards and monitor the service status of railway subgrade. It means that collaborative application of different moni1,2,3toring methods, cooperative analysis of different scales and resolutions data and collaboration of between various railway departments are required. Two monitoring schemes for subgrade disasters and service sta1,2,3tus of subgrade are proposed based on the integration of multi-source and multi-scale monitoring technique. Finally, the development direction of subgrade service status monitoring is discussed. Results and Conclusions: This monitoring system has been applied in the identification of subgrade service status in the Shang1,2,3hai-Nanjing high speed railway, which can quickly investigate the location of the disasters and the deteriora1,2,3tion degree of subgrade [ABSTRACT FROM AUTHOR]