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BackgroundDigital cognitive assessments, particularly those that can be done at home, present as low-burden biomarkers for participants and patients alike, but their effectiveness in the diagnosis of Alzheimer's disease (AD) or predicting its trajectory is still unclear. Here, we assessed what utility or added value these digital cognitive assessments provide for identifying those at high risk of cognitive decline.MethodsWe analyzed >500 Alzheimer's Disease Neuroimaging Initiative participants who underwent a brief digital cognitive assessment and amyloid beta (Aβ)/tau positron emission tomography scans, examining their ability to distinguish cognitive status and predict cognitive decline.ResultsPerformance on the digital cognitive assessment was superior to both cortical Aβ and entorhinal tau in detecting mild cognitive impairment and future cognitive decline, with mnemonic discrimination deficits emerging as the most critical measure for predicting decline and future tau accumulation.DiscussionDigital assessments are effective at identifying at-risk individuals, supporting their utility as low-burden tools for early AD detection and monitoring.HighlightsPerformance on digital cognitive assessments predicts progression to mild cognitive impairment at a higher proficiency compared to amyloid beta and tau. Deficits in mnemonic discrimination are indicative of future cognitive decline. Impaired mnemonic discrimination predicts future entorhinal and inferior temporal tau.

BACKGROUND: About one-third of older adults aged 65 years and older often have mild cognitive impairment or dementia. Acoustic and psycho-linguistic features derived from conversation may be of great diagnostic value because speech involves verbal memory and cognitive and neuromuscular processes. The relative decline in these processes, however, may not be linear and remains understudied. OBJECTIVE: This study aims to establish associations between cognitive abilities and various attributes of speech and natural language production. To date, the majority of research has been cross-sectional, relying mostly on data from structured interactions and restricted to textual versus acoustic analyses. METHODS: In a sample of 71 older (mean age 83.3, SD 7.0 years) community-dwelling adults who completed qualitative interviews and cognitive testing, we investigated the performance of both acoustic and psycholinguistic features associated with cognitive deficits contemporaneously and at a 1-2 years follow up (mean follow-up time 512.3, SD 84.5 days). RESULTS: Combined acoustic and psycholinguistic features achieved high performance (F1-scores 0.73-0.86) and sensitivity (up to 0.90) in estimating cognitive deficits across multiple domains. Performance remained high when acoustic and psycholinguistic features were used to predict follow-up cognitive performance. The psycholinguistic features that were most successful at classifying high cognitive impairment reflected vocabulary richness, the quantity of speech produced, and the fragmentation of speech, whereas the analogous top-ranked acoustic features reflected breathing and nonverbal vocalizations such as giggles or laughter. CONCLUSIONS: These results suggest that both acoustic and psycholinguistic features extracted from qualitative interviews may be reliable markers of cognitive deficits in late life.

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.

Quickly locating new populations of non‐native species can reduce the ecological and economic costs of species invasions. However, the difficulty of predicting which new non‐native species will establish, and where, has limited active post‐border biosurveillance efforts. Because pathways of introduction underlie spatial patterns of establishment risk, an intuitive approach is to search for new non‐native species in areas where many non‐native species have first been detected in the past. We formalize this intuition via first records distribution models (FRDMs), which apply species distribution modeling methods to the collection of first occurrence records across species (i.e. one record per species). We define FRDMs as statistical models that quantify environmental conditions associated with species' first naturalized records to predict spatial patterns of establishment risk. We model the first records of non‐native plants in the conterminous USA as a proof‐of‐concept. The novelty of FRDMs is that their inferences apply not just to the species that contributed data; they provide a rigorous framework for predicting hotspots of invasion for new non‐native taxa that share a pathway of introduction with the modeled species. FRDMs can guide survey efforts for new non‐native taxa at multiple scales and across ecosystems. [ABSTRACT FROM AUTHOR]

Background: Persistent ground-glass nodules (GGNs) carry a potential risk of malignancy, however, early diagnosis remained challenging. This study aimed to investigate the cut-off values of seven autoantibodies in patients with ground-glass nodules smaller than 3cm, and to construct machine learning models to assess the diagnostic value of these autoantibodies. Methods: In this multi-center retrospective study, we collected peripheral blood specimens from a total of 698 patients. A total of 466 patients with ground-glass nodular lung adenocarcinoma no more than 3cm were identified as a case group based on pathological reports and imaging data, and control group (n=232) of patients consisted of 90 patients with benign nodules and 142 patients with health check-ups. Seven antibodies were quantified in the serum of all participants using enzyme-linked immunosorbent assay (ELISA), and the working characteristic curves of the subjects were plotted to determine the cut-off values of the seven autoantibodies related ground-glass nodular lung adenocarcinoma early. Subsequently, the patients were randomly divided into a training and test set at a 7:3 ratio. Eight machine-learning models were constructed to compare the diagnostic performances of multiple models. The model performances were evaluated using sensitivity, specificity, and the area under the curve (AUC). Results: The serum levels of the seven autoantibodies in case group were significantly higher than those in the control group (P < 0.05). The combination of the seven autoantibodies demonstrated a significantly enhanced diagnostic efficacy in identifying ground-glass nodular lung adenocarcinoma early when compared to the diagnostic efficacy of the autoantibodies when used respectively. The combined diagnostic approach of the seven autoantibodies exhibited a sensitivity of 84.05%, specificity of 91.85%, and AUC of 0.8870, surpassing the performance of each autoantibody used individually. Furthermore, we determined that Sparrow Search Algorithm-XGBoost (SSA-XGBOOST) had the best diagnostic performance among the models (AUC=0.9265), with MAGEA1, P53, and PGP9.5 having significant feature weight proportions. Conclusions: Our research assessed the diagnostic performance of seven autoantibodies in patients with ground-glass nodules for benign-malignant distinction, and the nodules are all no more than 3cm especially. Our study set cut-off values for seven autoantibodies in identifying GGNs no more than 3cm and constructed a machine learning model for effective diagnosis. This provides a non-invasive and highly discriminative method for the evaluation of ground-glass nodules in high-risk patients. [ABSTRACT FROM AUTHOR]

AbstractObjectives: To evaluate neurologists and other clinicians’ contributions to U.S. ALS diagnostic timelines. Background: Over the past two decades, the average time to ALS diagnosis in the U.S. has remained unchanged at 12 months. ALS patients see 3-4 clinicians prior to referral to an ALS specialist for diagnosis confirmation and/or treatment initiation. There is an urgent need to identify where delays occur, so that targeted clinician awareness may be raised about early suspicion and referrals. Methods: Review of Medicare claims database for health care utilization patterns by ALS beneficiaries during diagnostic journey. Survey of typical clinic wait times for new consultations reported by 75-78 ALS Certified Treatment Centers of Excellence (2019-2021). Results: During 2011-2021, 78,520 Medicare beneficiaries were diagnosed with ALS (T0). The mean (median) timelines between first neurologist ambulatory visit and T0, is 16.5 (11.0) months; mean ± SD for ALS/neuromuscular providers being 9.6 ± 12.6 months versus 16.7 ± 17.5 months for non-neuromuscular neurologists. During the 12-months preceding T0, an ALS patient undergoes median(max) 1.5(4.0) brain-MRIs, 1.6(6.0) spine-MRIs, and 1.3(4.0) electromyography studies. Greater than 75% of ALS centers consistently report ≤ 4 week wait times for new ALS consults. This study introduces “thinkALS,” an easy-to-use clinical diagnostic and referral guide for non-ALS neurologists to tackle this challenge. Conclusions: This study is the first to provide metrics on how non-neuromuscular/ALS specialists contribute to ALS diagnostic timelines in the U.S. [ABSTRACT FROM AUTHOR]

Early detection of cancer typically facilitates improved patient outcomes; however, many cancers are not easily diagnosed at an early stage. One potential route for developing new, non-invasive methods of cancer detection is by testing for cancer-related volatile organic compounds (VOCs) biomarkers in patients' urine. In this review, 44 studies covering the use and/or identification of cancer-related VOCs were examined, including studies which examined multiple types of cancer simultaneously, as well as diverse study designs. Among these studies the most studied cancers included prostate cancer (29% of papers), lung cancer (22%), breast cancer (20%), and bladder cancer (18%), with a smaller number of studies focused on colorectal cancer, cervical cancer, skin, liver cancer and others. Importantly, most studies which produced a VOC-based model of cancer detection observed a combined sensitivity and specificity above 150%, indicating that urine-based methods of cancer detection show considerable promise as a diagnostic tool. Mass spectrometry (MS) and electronic noses (eNose) were the most employed tools used in the detection of VOCs, while animal-based models were less common. In terms of VOCs of interest, 47 chemical species identified as correlated with various types of cancer in at least two unrelated papers, some of which were consistently up- or down-regulated in cancer patients, and which may represent useful targets for future studies investing urinary VOC biomarkers of cancer. Overall, it was concluded that research in this field has shown promising results, but more work may be needed before the widespread adoption of these techniques takes place. [ABSTRACT FROM AUTHOR]

Introduction: Current methods for reading difficulty risk detection at school entry remain error-prone. We present a novel approach utilizing machine learning analysis of data from GraphoGame, a fun and pedagogical literacy app. Methods: The app was played in class daily for 10 min by 1,676 Norwegian first graders, over a 5-week period during the first months of schooling, generating rich process data. Models were trained on the process data combined with results from the end-of-year national screening test. Results: The best machine learning models correctly identified 75% of the students at risk for developing reading difficulties. Discussion: The present study is among the first to investigate the potential of predicting emerging learning difficulties using machine learning on game process data. [ABSTRACT FROM AUTHOR]

Peripheral neuropathy is a common cause of morbidity in diabetes. Despite recent advancements in early diagnosis methods, there is a need for practical, highly sensitive, and cost‐effective screening methods in clinical practice. This study summarizes evidence from systematic reviews and meta‐analyses on the diagnostic accuracy of validated screening methods for diabetic peripheral neuropathy. Two independent reviewers assessed methodological quality and bias using AMSTAR and ROBIS tools. Seven reviews with 19,531 participants were included. The monofilament test showed inconsistent sensitivity (S: 0.53–0.93) and specificity (Sp: 0.64–1.00), along with high variability in its application. Neuropad exhibited high S (86%, 95% CI 79–91). However, variations in the interpretation of results across the included studies may have impacted its Sp (65%, 95% CI 51–76). The Ipswich touch test exhibited adequate diagnostic accuracy (S: 0.77, Sp: 0.96, DOR: 75.24) but lacked comparison with gold standard tests. In vibration perception studies, the biothesiometer outperformed the tuning fork (S: 0.61–0.80 vs. 0.10–0.46). In general, heterogeneity was observed due to varied reference tests, thresholds, and patient differences. The development of automated analysis methods, as well as determination of predictive value of the combination of screening tools, is needed for further studies. Based on the study results, we suggest that clinicians should select screening tools tailored to their patient population, clinical setting, and available resources, as no single test can be universally recommended for all clinical scenarios. [ABSTRACT FROM AUTHOR]

Lung cancer, the leading cause of cancer deaths globally, has better survival rates with early detection. Annual low-dose CT (LDCT) screenings are recommended for high-risk individuals due to age and smoking. These individuals are also at risk for other cancers. Our study explores gastrointestinal (GI) cancer mortality in lung cancer screening participants and the potential of LDCT screenings to detect pancreatic cancer. We utilized data from a prospective multi-institutional cohort study, the International Early Lung Cancer Action Project (I-ELCAP). We analyzed GI cancer deaths among participants in New York State (1992–2010), exploring demographics and GI cancer distribution. Radiologists retrospectively reviewed pancreatic cancer cases within 24 months post-LDCT, comparing findings with original reports. Among 10,150 participants, 189 died from GI cancers; mean age 75, mostly male smokers. Pancreatic cancer (41.8%) led, followed by esophageal (17.5%) and colon cancer (16.9%). Median time between baseline LDCT and death was 116 months (9.7 years). 82/189 (43.4%) participants died within 5 years of their last LDCT screening, with pancreatic cancer again prominent (45.1%). In 79 pancreatic cancer deaths, 17.7% occurred within 24 months post-LDCT. A re-review identified previously undetected pancreatic findings, with 4 out of 14 participants (28.6%) showing abnormalities. This underscores the potential of lung cancer screening programs to provide insights beyond lung health. This study of over 10,000 participants in a lung cancer screening program reveals that they are at risk for GI cancer deaths, particularly pancreatic cancer. Re-reviews of LDCT scans revealed previously undocumented pancreatic findings in a third of participants who died from pancreatic cancer, underscoring the need to identify, document, and follow up on these findings. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is the prevailing malignancy among women, exhibiting a discernible escalation in incidence within our nation; hormone receptor‐positive (HR+) human epidermal growth factor receptor 2‐negative (HER2−) breast cancer is the most common subtype. In this study, we aimed to search for a non‐invasive, specific, blood‐based biomarker for the early detection of luminal A breast cancer through proteomic studies. Methods: To explore new potential plasma biomarkers, we applied data‐independent acquisition (DIA), a technique combining liquid chromatography and tandem mass spectrometry, to quantify breast cancer‐associated plasma protein abundance from a small number of plasma samples in 10 patients with luminal A breast cancer, 10 patients with benign breast tumors, and 10 healthy controls. Results: The proteomes of 30 participants in all cohorts were analyzed using the DIA method, and a total of 517 proteins and 3584 peptides were quantified. We found that there were significant differences in plasma protein expression profiles between breast cancer patients and non‐breast cancer patients, and breast cancer was mainly related to lipid metabolism pathways. Finally, the optimal protein combinations for the diagnosis of breast cancer were PON3, IGLV3‐10, and IGHV3‐73 through multi‐model analysis, which had a high prediction accuracy for breast cancer (AUC = 0.92), and the model could also distinguish breast cancer from HC (AUC = 0.92) and breast cancer from benign breast tumor (AUC = 0.91). Conclusions: The study revealed proteomic signatures of patients with luminal A breast cancer, identified multiple differential proteins, and identified three plasma proteins as potential diagnostic biomarkers for breast cancer. It provides a reference for the screening of biomarkers for breast cancer. [ABSTRACT FROM AUTHOR]

Aims: The study aims are to present the Italian adaptation of the Abbreviated Clinical Structured Interview for DSM‐5 Attenuated Psychosis Syndrome (Mini‐SIPS) and illustrate its implementation in a clinical setting. Methods: The Mini‐SIPS was developed from the original extended version as a tool designed to identify, within the clinical high risk (CHR) framework, the DSM‐5 Attenuated Psychosis Syndrome (APS) and to be implemented in clinical settings. The Mini‐SIPS was translated in Italian by a Yale‐certified SIPS trainer, then back‐translated in English by a trained psychologist, then approved by the Mini‐SIPS authors. Since September 2021, the adapted Italian version of the Mini‐SIPS has been implemented at the First Episode Psychosis (FEP) Program in Ferrara, Italy. Results: The Italian version of Mini‐SIPS was successfully administered to 15 individuals subsequently referred to the First Episode Psychosis service in Ferrara. Within this sample, the tool has proven to be both an effective and efficient tool for the identification of CHR and FEP, and a valid instrument to help with the differential diagnosis. It also performed as a valuable guide for retrieving information about psychiatric history, to date the onset of APS and/or full‐blown psychosis, to track the progression from CHR to psychosis or to symptoms resolution and to describe patients' pathways to care. Conclusion: The Mini‐SIPS is an efficient and easy‐to‐use interview to identify the early stages of psychosis and established psychosis in clinical contexts. The Italian adaptation of this interview could be effectively implemented in other Italian FEP Programs as a screening and monitoring tool. Formal validation of the instrument is needed to assessed validity and reliability in the diagnosis of the CHR and FEP. [ABSTRACT FROM AUTHOR]

The lack of understanding of topics in uncharted research areas can often be mitigated through a careful review of the existing literature. However, when a field is not well-studied, relying on assumptions before starting a project should be avoided. This article highlights the dangers of such presumptions as demonstrated by the case of brown planthopper (Nilaparvata lugens) detection in rice field. Although unpiloted aerial vehicles (UAVs) have shown promise in various agricultural applications, their effectiveness in the early detection of brown planthopper damage was initially assumed based on the expectation of visible symptoms. The image analysis in the current study indicated that images obtained from a camera mounted on a UAV could not detect the symptoms of the very early stages of damage from brown planthoppers. An overlooked factor was whether the pest damage was uniformly distributed across an entire rice plant. If symptoms appear consistently, early detection using a top-down view from a UAV is possible; otherwise, detection may be delayed. Our findings emphasize the need for thorough preliminary research to avoid failure. By investigating the biological characteristics of the target pest and the potential limitations of detection methods, researchers can greatly improve their chances of success. We hope that readers will recognize the importance of thoroughly examining unexplored areas before embarking on new research. [ABSTRACT FROM AUTHOR]

Screening and early detection programs for breast cancer (BC) have significantly reduced mortality rates and enhanced survival rates globally. Breast cancer screening programs in low‐ and middle‐income countries like Iran need improvement. This study examines Iran's breast cancer screening and early detection (BCSED) policies, employing the Policy Triangle Model in two phases: a review of 27 documents including various related sources related of policies and strategies, followed by 20 semi‐structured interviews across four distinct levels: policy, regional, clinical, and community levels. Content analysis for documents and MAXQDA2020 software for the second phase were used, and findings integrated. In our analysis of 27 national documents and 20 interviews, we found 10 themes, 19 sub‐themes, and 42 specific codes across four dimensions of the mentioned framework. Main themes in the context dimension covered deficient interactions, deficiency in knowledge management, cultural challenges, and socioeconomic challenges. From the analysis of 27 national documents, we recognized service excellence schemes, personal health encouragement, and service levels. Policy formulation and monitoring and evaluation emerged as central themes for the process dimension, while stakeholder challenges were prominent in the actors' dimension. BCSED programs are multifactorial issues. Proposed strategies to address these issues include the dedicated funds for BC prevention programs, strengthening the Ministry of Health's stewardship role, elevating public awareness, and conducting systematic screening trials in select cities to provide valuable national evidence for future policymaking. Highlights: Breast cancer is recognized as the most widespread cancer and the primary cause of death among women across the world.Previous research have demonstrated that screening and early detection programs aim to detect diseases in their asymptomatic stages, thereby enhancing the potential for successful health outcomes.Although Iran launched a breast cancer screening program in 2014, its implementation faces challenges, and there is limited data on the participation and adherence of Iranian women to breast prevention programs.Some low‐ and middle‐income countries have experienced positive effects from implementing screening programs and it shows that Iran may achieve to this potential to improve health outcomes.This study aims to conduct a comprehensive analysis of Iranian policies for breast cancer screening and early detection programs during last two decades using the policy triangle model, filling a gap in previous research. [ABSTRACT FROM AUTHOR]

Background: Our aim was to target the unsatisfied need for early detection of the at‐risk population and determine the subgroup of patients whose psoriasis (PsO) could transform into psoriatic arthritis (PsA). Methods: A retrospective and longitudinal case–control study was conducted at Beijing Chao‐yang Hospital. It included 75 patients who were clinically diagnosed with PsA in the case group and 345 who solely suffered from PsO without PsA in the control group. A variety of baseline covariates were gathered from every patient with PsO. Univariate and multivariate analyses and receiver operating characteristic (ROC) curves were used to identify underlying risk factors and determine whether it was necessary to examine the imaging of PsO patients. Results: In multivariate logistic regression analysis, age ≥40 (odds ratio (OR): 1.04, 95% confidence interval (CI): 1.02–1.06, P < 0.01), nail involvement (OR: 1.17, 95% CI: 1.09–1.32, P < 0.01), erythrocyte sedimentation rate (ESR) (OR: 1.03, 95% CI: 1.01–1.06, P < 0.05) and elevated high‐sensitivity C‐reactive protein (hs‐CRP) (OR: 1.31, 95% CI: 1.13–1.53, P < 0.01) were perceived to be risk factors for the transformation from PsO into clinical PsA. By combining magnetic resonance imaging (MRI)‐detected enthesitis with tenosynovitis, combined predictors demonstrated better diagnostic efficacy, with an improvement in specificity (94.3% vs. 69%) and similarities in sensitivity (89% vs. 84.6%). The areas under the ROC curve (AUCs) amounted to 0.925 (95% CI: 0.882–0.967, P < 0.01) and 0.858 (95% CI: 0.814–0.903, P < 0.01). Conclusions: It was identified that age ≥40, nail involvement, as well as an elevated ESR, and hs‐CRP served as independent risk factors for PsO transforming into PsA. Additionally, MRI provides additional value for the early recognition of PsA. [ABSTRACT FROM AUTHOR]

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

Peripheral neuropathy (PN) significantly impacts the quality of life, causing substantial morbidity and increased mortality, as well as escalating healthcare costs. While PN can have various causes, the most common form, diabetic peripheral neuropathy, poses considerable risks for potential complications. Diabetic peripheral neuropathy (DPN) affects over 50% of people with prediabetes and diabetes. Despite its prevalence, a global gap in diagnosis and management exists, exacerbated by the COVID-19 pandemic. This expert consensus was formulated through a comprehensive evaluation by a panel of experts, informed by a focused literature review, aiming to establish a clinically robust approach to diagnosing and managing pre- and diabetic PN with the early utilization of neurotropic B vitamins. This document offers a consensus perspective on the existing challenges in diagnosing and managing PN, focusing on DPN. The expert panel proposes measures to address this underdiagnosed burden, highlighting the importance of early intervention through innovative screening methods, integrated care approaches, and therapeutic strategies. The document advocates for increased awareness, targeted campaigns, and proactive care strategies to bridge gaps in the patient care of individuals with diabetes, emphasizing the importance of early detection and timely management to improve overall health outcomes. Specific recommendations include incorporating simplified questionnaires and innovative screening methods into routine care, prioritizing neurotropic B vitamin supplementation, optimizing glucagon-like peptide 1 (GLP-1) receptor agonist treatments, and adopting a holistic approach to neuropathy management. The consensus underscores the urgent need to address the underdiagnosis and undertreatment of PN, offering practical measures to enhance early detection and improve health outcomes for individuals with DPN. [ABSTRACT FROM AUTHOR]

Introduction: Women constitute about one-half of the workforces in most countries and spend a significant proportion of their time at the workplace, contributing to a lot of physical and mental stress. The health-care professional in the workforce was the most affected during the COVID-19 pandemic. COVID-19 and noncommunicable diseases (NCDs) and associated risk factors have a substantial correlation. According to numerous studies from around the world, people who already have established risk factors such as diabetes, hypertension, obesity, or other vascular risk factors, are more likely to contract COVID-19 and experience complications or even pass away from it. Objectives: The objective is to assess the health status of working women in a tertiary health-care center in Kerala during the COVID-19 pandemic. Methodology: The study was done in a tertiary health-care center in Kozhikode district, Kerala, South India. This center comprises medical, paramedical and nursing institutes. A total of 262 women were included in the study, and data were collected using a semi-structured questionnaire. Results: This study demonstrated that working women's stress was prevalent. It reveals that 94 (99.4%) women experience stress, 60.7% experience depression, 72.5% do not engage in daily physical activity and 80.8% of women were at risk of having NCDs. Conclusion: This study was an attempt to explore the health status of the women's working health status during COVID-19 time. It found that some women are hypertensive, depressed and have stress-related disorders such as anxiety and worries. Mental health is directly linked with physical activity. The stress levels were controlled for those who were involved in physical activity on regular basis. Hence, we should work on this area to help improve these conditions. [ABSTRACT FROM AUTHOR]

Coronary heart disease (CHD) is the leading cause of death in the world. The risk of coronary heart disease can be reduced or even prevented by early detection. Early detection of CHD has been widely developed using machine learning, but the machine learning algorithms used sometimes have low interpretability. Low interpretability makes it difficult for users to understand the cause of the decision. Referring to this, this research aims to propose an early detection model using machine learning interpretability, which is implemented using the C5.0 algorithm and interpreted using Shapley additive explanations (SHAP). This research method is divided into 3 stages, namely preprocessing, interpretable machine learning, and performance evaluation. This study used 215 patient data from Dr. Moewardi Surakarta Hospital. Testing the resulting model using the k-folds cross-validation method. The test results show that the risk factors that make a high contribution to the output of the coronary heart disease detection model are systolic blood pressure, diastolic blood pressure, and employment level, with the resulting accuracy performance of 84.64%. The proposed model can be an alternative for early prediction of coronary heart disease which can explain the influence of each selected risk factor on the model output. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease that is challenging to detect at an early stage. Biomarkers are needed that can detect PDAC early in the course of disease when interventions lead to the best outcomes. We highlight study design and statistical considerations that inform pancreatic cancer early detection biomarker evaluation. We describe experimental design strategies in this setting useful for streamlining biomarker evaluation at each Early Detection Research Network (EDRN) phase of biomarker development. We break the early EDRN phases into sub-phases, proposing objectives, study design strategies, and biomarker performance benchmarks. The goal of early detection in populations at high-risk of PDAC is described. Evaluating biomarker behavior in patients under surveillance without disease can winnow candidate biomarkers. Potential resources for biomarker validation studies are described. Multisite and multidisciplinary collaboration can facilitate study design strategies in this lethal but low incidence disease and streamline the path from biomarker discovery to clinical use. Improvements in analytical and experimental design methods could help accelerate biomarker evaluation through the phases of biomarker development. [ABSTRACT FROM AUTHOR]

Urban ecosystems are suitable for the introduction and spread of alien bird species, and early detection of their establishment and expansion is crucial to reduce potential negative impacts. In this context, the use of opportunistic citizen science data can have considerable advantages in relation to conventional scientific approaches. We gathered records of parakeets and parrots (Psittaciformes) and mynas and starlings (Sturnidae) for the urban area of Lisbon (Portugal) from the eBird database. We used this opportunistic citizen science data to document the establishment and expansion and to estimate distribution trends of alien bird species in urban ecosystems. In the last decade there has been a considerable increase in the amount of opportunistic citizen science data available in eBird for our study area. The probability of presence of the Senegal parrot, rose-ringed parakeet, blue-crowned parakeet, and crested myna was positively influenced by the number of lists. For the rose-ringed parakeet, blue-crowned parakeet, and crested myna, the year positively influenced the probability of presence, suggesting an increase in distribution range. We observed that spatio-temporal variations in effort associated to opportunistic citizen science data sources may generate bias in trend estimates, and therefore we recommend the effort should always be accounted for. Our approach agreed with the documented expansion of alien bird species in the study area, supporting the potential usefulness of opportunistic citizen science in providing early detection on biological invasions in urban ecosystems, particularly where this is the best or the only source of information available. [ABSTRACT FROM AUTHOR]

Cancer screenings aid in the early detection of cancer and can help reduce cancer-related mortality. The current model of care for cancer screening is often siloed, based on the targeted cancer site. We tested the acceptability of a new model of care, called the One-Stop-Shop Cancer Screening Clinic, that centralizes cancer screenings and offers patients the option to complete all their recommended cancer screenings within one to two visits. We administered surveys to 59 community members and 26 healthcare providers to gather feedback about the One-Stop-Shop model of care. Both community members and providers identified potential benefits (e.g., decreased patient burden, increased completion of cancer screenings) and also potential challenges (e.g., challenges with workflow and timing of care) of the model of care. The results of the study support the acceptability of the model of care. Of the community members surveyed, 89.5% said, if offered, they would be interested in participating in the One-Stop-Shop Cancer Screening Clinic. Future studies are needed to formally evaluate the impact and cost effectiveness of the One-Stop-Shop Cancer Screening Clinic. [ABSTRACT FROM AUTHOR]

Central Europe experienced severe droughts from 2018 to 2020, which led to bark beetle infestations in Norway spruce (Picea abies L.). While recent studies have identified these droughts as a major factor in the dieback of spruces, the exact mechanism through which drought stress influences spruce vitality and increases vulnerability to infestations is not well understood. To gain insights into the spatial and temporal relations between infestations and drought stress, this study compared soil moisture index (SMI) data for the entire soil layer from the German Drought Monitor and Sentinel-2 satellite time series for healthy and bark beetle infested spruce stands preceding the infestation. The study used t-tests to compare pre-infestation monthly sums of SMI and vegetation indices (VIs) for healthy and infested stands. Yearly trajectories of six VIs were used to compare the separability between infested and healthy plots. The results showed that the monthly sum values of SMI for healthy spruce plots were statistically significantly higher than the infested plots from May 2017 to January 2018, while the monthly sum values of Inverted Red Edge Chlorophyll Index of healthy spruce plots were significantly higher than infested plots during growing seasons. With these findings, the study provides useful information for the understanding of the relation of soil moisture and remote sensing VIs patterns in relation to bark beetle infestations. [ABSTRACT FROM AUTHOR]

Simple Summary: Since the U.S. National Lung Screening Trial (NLST) demonstrated the efficacy of low-dose computed tomography (LDCT) for early lung cancer detection, LDCT has emerged as a critical tool for identifying lung cancer in high-risk populations. This retrospective study evaluates trends in non-small cell lung cancer (NSCLC) staging at Kaohsiung Medical University Hospital (KMUH) from 2011 to 2020, with a particular focus on the effects of LDCT screening, which was introduced in 2013. We examined correlations between the number of LDCT screenings and NSCLC stage distribution, emphasizing early-stage (stages 0 and I) and late-stage (stage IV) diagnoses. Additionally, we assessed histopathological differences between adenocarcinoma and squamous cell carcinoma identified via LDCT and evaluated the impact of early diagnosis on five-year survival rates. Background: Low-dose computed tomography (LDCT) has been widely adopted for lung cancer screening due to its proven ability to reduce lung cancer mortality, especially among high-risk populations. Methods: This retrospective study aims to evaluate the impact of LDCT screening on non-small cell lung cancer (NSCLC) staging at Kaohsiung Medical University Hospital (KMUH) from 2011 to 2020, following the introduction of LDCT in 2013. The study examines the correlation between LDCT screening volume and changes in the distribution of NSCLC stages, particularly early-stage (stages 0 and I) and late-stage (stage IV) diagnoses. Additionally, it explores the differences in histopathological subtypes, focusing on adenocarcinoma and squamous cell carcinoma, and assesses the impact of early detection on five-year survival rates. Results: The results show a significant increase in early-stage NSCLC diagnoses, particularly in adenocarcinoma cases, where early-stage diagnoses rose from 10.4% in 2010 to 38.7% in 2019. However, the number of stage IV cases remained stable, indicating that LDCT may not substantially reduce late-stage diagnoses. Pearson's correlation analysis demonstrated a strong positive correlation between LDCT screening and early-stage NSCLC detection, particularly for adenocarcinoma (p < 0.001), though the early detection of squamous cell carcinoma and small cell carcinoma remained limited. Conclusions: The study concludes that LDCT screening plays a crucial role in improving early NSCLC detection and five-year survival rates. Future research should focus on optimizing screening strategies to capture more at-risk populations and enhance the detection of harder-to-diagnose subtypes like squamous cell carcinoma. [ABSTRACT FROM AUTHOR]

Efficient early pathogen detection, before symptom apparition, is crucial for optimizing disease management. In barley, the fungal pathogen Pyrenophora teres is the causative agent of net blotch disease, which exists in two forms: P. teres f. sp. teres (Ptt), causing net-form of net blotch (NTNB), and P. teres f. sp. maculata (Ptm), responsible for spot-form of net blotch (STNB). In this study, we developed primers and a TaqMan probe to detect both Ptt and Ptm. A comprehensive k-mer based analysis was performed across a collection of P. teres genomes to identify the conserved regions that had potential as universal genetic markers. These regions were then analyzed for their prevalence and copy number across diverse Moroccan P. teres strains, using both a k-mer analysis for sequence identification and a phylogenetic assessment to establish genetic relatedness. The designed primer-probe set was successfully validated through qPCR, and early disease detection, prior to symptom development, was achieved using ddPCR. The k-mer analysis performed across the available P. teres genomes suggests the potential for these sequences to serve as universal markers for P. teres, transcending environmental variations. [ABSTRACT FROM AUTHOR]

The specificity and sensitivity of a simple non-invasive multi-cancer screening method in detecting breast, lung, prostate, and colorectal cancer in breath samples were evaluated in a double-blind study. Breath samples of 1386 participants (59.7% males, median age 56.0 years) who underwent screening for cancer using gold-standard screening methods, or a biopsy for a suspected malignancy were collected. The samples were analyzed using a bio-hybrid platform comprising trained detection canines and artificial intelligence tools. According to cancer screening/biopsy results, 1048 (75.6%) were negative for cancer and 338 (24.4%) were positive. Among the 338 positive samples, 261 (77.2%) were positive for one of the four cancer types that the bio-hybrid platform was trained to detect, with an overall sensitivity and specificity of 93.9% (95% confidence interval [CI] 90.3-96.2%) and 94.3% (95% CI 92.7%-95.5%), respectively. The sensitivity of each cancer type was similar; breast: 95.0% (95% CI 87.8-98.0%), lung: 95.0% (95% CI 87.8-98.0%), colorectal: 90.0% (95% CI 74.4-96.5%), prostate: 93.0% (95% CI 84.6-97.0%). The sensitivity of 14 other malignant tumors that the bio-hybrid platform was not trained to detect, but identified, was 81.8% (95% CI 71.8%-88.8%). Early cancer (0–2) detection sensitivity was 94.8% (95% CI 91.0%-97.1%). This bio-hybrid multi-cancer screening platform demonstrated high sensitivity and specificity and enables early-stage cancer detection. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is the second most prevalent neurological disorder, predominantly affecting older people. With no existing cure, the early detection of PD, where symptoms are not entirely evident but indicative of the disease's onset, is critical. This study aims to design and develop AI-based diagnostic methods that can detect these early signs of PD with high accuracy, thereby facilitating more effective disease management. This study focuses on developing a method that not only identifies PD at an early stage but also provides clinicians with a tool to interpret the decisions taken by the AI models to avoid misdiagnosis. In this study, a T2-weighted 3D Magnetic resonance imaging (MRI) dataset is used to analyze detailed morphological, textural, and structural changes. The MRI scans are pre-processed using brain extraction, image registration, bias correction, normalization, and segmentation processes. Upon segmentation, feature extraction was applied to the segmented subcortical regions using radiomics tools, resulting in the extraction of 107 features. The top 20 features were selected through Pearson's correlation, recursive feature elimination, and a ranking model, which are responsible for the ML model's class prediction. Statistical validation of these features was also performed using Analysis of Variance (ANOVA), pairwise t-tests, and Kruskal-Wallis H-tests to ensure that the identified 20 features were dominant for the prediction. Based on the identified features, several Machine Learning (ML) models were used to identify the best classifier for the provided real-world MRI scans. The Gradient Boosting (GB) algorithm achieved better prediction accuracy among the compared models. Incorporating the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalances significantly improved the model's performance, boosting accuracy to 96.8 % from 87 %. Further, multiple Explainable Artificial Intelligence (XAI) techniques were deployed to enhance the transparency and interpretability of the models. These techniques provide insights into how each identified feature influences predictions by the classifier, assisting clinicians in making trustworthy decisions when planning diagnosis and treatment measures. [Display omitted] • 3D MRI scans were taken from PPMI to detect Prodromal Parkinson's Disease (PD) with limited data. • Explainable AI (XAI) techniques suggest that the GLCM feature was the most dominant and consistent. • Analyzing the insights of the model using XAI, provides transparency of the model. [ABSTRACT FROM AUTHOR]

In order to comprehend the dynamics of disease propagation within a society, mathematical formulations are essential. Examining how hand contamination might induce pink eye infection (conjunctivitis virus) and treating it early with immunizations is the goal of this research. An immune system-boosting mathematical model is established, and it is converted to a fractional order model by using the Caputo fractional operator. To find the steady position of a recently constructed system SEVIR, a qualitative and quantitative analysis is conducted. Reliable bounded findings are ensured by assessing the generated system's boundedness, positivity, and uniqueness all crucial characteristics of epidemic models. The proposed non-linear system is verified to be present, and a unique solution is shown using fixed-point theorems to validate reliable solutions. Reproductive number with sensitivity analysis of parameters are also determined to verify the rate of spread and see how rate of change of each parameter is most sensitive. Using Lyapunov first derivative functions, the system is examined for local and global stability in order to evaluate the overall effect of early detection strategies and vaccination programs for people with weakened immune systems. Caputo operator is utilized for reliable solution using power law kernel with different fractional values for continuous monitoring of spread of pink eyes infection. Simulations have been made to see the real behavior and effects of pink eyes (conjunctivitis virus) infection to verify that the low immune individuals become strengthen due to early detection and vaccination combine measures. Also identify the true behavior for the control of pink eyes (conjunctivitis virus) infection after early detection and treatment as well as vaccination due to strong immune system of the patients. Investigating the transmission and management of diseases, as well as creating novel control techniques based on our validated findings to stop the conjunctivitis virus from spreading, would be aided by this kind of research. [ABSTRACT FROM AUTHOR]

Epstein–Barr virus (EBV) is detected in nearly 100% of nonkeratinizing nasopharyngeal carcinoma (NPC) and EBV‐based biomarkers are used for NPC screening in endemic regions. Immunoglobulin A (IgA) against EBV nuclear antigen 1 (EBNA1) and viral capsid antigen (VCA), and recently identified anti‐BNLF2b antibodies have been shown to be the most effective screening tool; however, the screening efficacy still needs to be improved. This study developed a multiplex serological assay by testing IgA and immunoglobulin G (IgG) antibodies against representative EBV antigens that are highly transcribed in NPC and/or function crucially in viral reactivation, including BALFs, BNLF2a/b, LF1, LF2, and Zta (BZLF1). Among them, BNLF2b‐IgG had the best performance distinguishing NPC patients from controls (area under the curve: 0.951, 95% confidence interval [CI]: 0.913–0.990). Antibodies to lytic antigens BALF2 and VCA were significantly higher in advanced‐stage than in early‐stage tumors; in contrast, antibodies to latent protein EBNA1 and early lytic antigen BNLF2b were not correlated with tumor progression. Accordingly, a novel strategy combining EBNA1‐IgA and BNLF2b‐IgG was proposed and validated improving the integrated discrimination by 15.8% (95% CI: 9.8%–21.7%, p <.0001) compared with the two‐antibody method. Furthermore, we found EBV antibody profile in patients was more complicated compared with that in healthy carriers, in which stronger correlations between antibodies against different phases of antigens were observed. Overall, our serological assay indicated that aberrant latent infection of EBV in nasopharyngeal epithelial cells was probably a key step in NPC initiation, while more lytic protein expression might be involved in NPC progression. [ABSTRACT FROM AUTHOR]

Background: The large-scale identification of people at risk of transitioning from relatively lower-risk to higher-risk alcohol use (e.g. problem drinking) remains a public health challenge despite advances in the identification of risk and protective factors.Objective: This observational study used machine learning to identify Reddit (social media platform) posting activity associated with transitioning from lower- to higher-risk forms of alcohol use.Methods: We employed bottom-up and top-down approaches to identify lower- and higher-risk alcohol-related subreddits. Using a non-parametric negative control procedure, we estimated each of 10,006 Reddit communities’ risk of progression from lower- to higher-risk alcohol-related communities and applied a random forest model to predict progression among individual Reddit members. Eligible Reddit members had posted on Reddit for two or more years before their first post in a lower-risk alcohol-related community and for three or more years after that (N = 4,160).Results: Our methodology identified 42 alcohol-related communities, four of which were suggestive of problem drinking. Five communities were significantly associated with progression. Random forests model’s risk scores for individual members correlated with their progression to higher-risk communities at 0.30; the model predicted progression of individual Reddit members with a 0.92 area under the curve.Conclusions: Posting in communities dedicated to other substance use, depression, and occupation in the food service industry was associated with posting activity suggestive of problem drinking 3 years later. Posting activity on Reddit may be used for early detection of people at higher risk of transitioning from lower- to higher-risk forms of alcohol use. [ABSTRACT FROM AUTHOR]

Background: Individual perceptions, socio-cultural beliefs and health system factors are key determinants of people's health seeking behavior and are widely cited as the causes of delayed breast cancer diagnosis among women from structurally vulnerable settings. Asking: "how do women with a non-lived experience of cancer understand the disease and, what informs their health seeking behaviors?", we qualitatively explored, individual, sociocultural and health system elements from a conceptual model derived from the Socioecological, Health Belief and Cancer Stigma Frameworks, to understand perspectives of breast cancer in a South African urban community setting. Methods: Using a deductive approach and allowing new themes to emerge inductively, we investigated phenomenologically, breast cancer perceptions among 34 women from Soweto, Johannesburg (aged 35–74 years) in 6 Focus Group Discussions. We then conducted 20 follow-up semi-structured in-depth interviews to explore novel themes and suggestions for increasing breast cancer screening. Results: Findings revealed some awareness of breast and other cancers, but confusion and gaps in understanding of the disease, resulting in socio-culturally influenced misperceptions of risks, causes, and outcomes following treatment of breast cancer. This fueled perceptions of profound fear and stigma against people with breast and other cancers. These findings together with participant perceptions of primary healthcare providers being unwelcoming, under-resourced, and insufficiently trained to deal with breast cancer, resulted in women reporting being reluctant to participating in screening/early detection care seeking behavior. Women only accessed primary care when experiencing extreme pain or ill-health. Participants suggested as solutions for future interventions, the need for sustained community engagement, harnessing existing clinic and community stakeholders and resources to provide clear and understandable breast cancer information and encouragement for screening uptake. Conclusions: Health literacy gaps surrounding breast cancer fuels socio-culturally influenced misperceptions, fear, stigma, and fatalism among women from Soweto. Women perceive primary care providers of having insufficient knowledge, skills, and resources to provide effective breast cancer screening services. Participants suggested the need for greater community engagement involving primary clinics and existing community stakeholders working collaboratively. Clear, understandable, and consistent information about breast cancer must be regularly disseminated and communities must be regularly encouraged to utilize breast cancer screening services. [ABSTRACT FROM AUTHOR]

Early detection and treatment of skin cancer are important for patient recovery and survival. Dermoscopy images can help clinicians for timely identification of cancer, but manual diagnosis is time-consuming, costly, and prone to human error. To conduct this, an innovative deep learning-based approach has been proposed for automatic melanoma detection. The proposed method involves preprocessing dermoscopy images to remove artifacts, enhance contrast, and cancel noise, followed by feeding them into an optimized Convolutional Neural Network (CNN). The CNN is trained using an innovative metaheuristic called the Improved Chameleon Swarm Algorithm (CSA) to optimize its performance. The approach has been validated using the SIIM-ISIC Melanoma dataset and the results have been confirmed through rigorous evaluation metrics. Simulation results demonstrate the efficacy of the proposed method in accurately diagnosing melanoma from dermoscopy images by highlighting its potential as a valuable tool for clinicians in early cancer detection. [ABSTRACT FROM AUTHOR]

Blood-based tests for multi-cancer early detection (MCED) are being developed to facilitate the detection of various cancer types. The Detecting cancers Earlier Through Elective mutation-based blood Collection and Testing study (DETECT-A) study evaluated an MCED test in 9,911 women, age 65-75, without personal history of cancer. In a post-hoc analysis, we report on the detection of precancerous neoplasms consequent to MCED testing and follow-up. Participants with positive baseline and confirmatory MCED testing underwent 2-deoxy-2[fluorine-18] fluoro-D-glucose positron emission tomography-computed tomography (PET-CT) and diagnostic evaluation as indicated by PET-CT results. We reviewed the electronic health records of participants with a precancerous neoplasm and summarized their clinical course. MCED results were positive in 134 participants. Clinically significant pre-malignant conditions were identified in three of these participants: A 71-year-old with an ovarian mucinous cystadenoma, a 67-year-old with an appendiceal mucinous neoplasm, and a 70-year-old with colon adenomas displaying high-grade dysplasia. All three participants underwent surgical treatment and remain alive and cancer-free as of last follow up. The diagnostic evaluation of a positive MCED test may occasionally reveal clinically significant pre-cancerous conditions amenable to interventions. The frequency of such findings and their clinical impact warrants further study. [ABSTRACT FROM AUTHOR]

High‐altitude hypoxic brain injury (HHBI) is a kind of acute mountain sickness and the survival rate of patients with HHBI can be improved only if it is detected and treated at the early stage. However, limited by speediness and accuracy, it is still very difficult for most of current approaches to realize the early detection of HHBI. We propose herein a novel strategy for this goal based on spatiotemporal changes in the brain oxygen level. As revealed by in vivo electrochemistry, the characteristic changes of brain oxygen level under the high‐altitude exposure are directly associated with the brain hypoxia status. Given brain hypoxia is the main pathogenesis of HHBI, the degree of HHBI can be diagnosed by the variation of brain oxygen, making the early detection of HHBI feasible. In addition, the risk of HHBI for mouse exposed to high‐altitude hypoxia environments can be also prognosed days in advance. [ABSTRACT FROM AUTHOR]

Introduction: Glaucoma is the second leading cause of permanent blindness after cataracts in Indonesia, which sufferers often don't realize. The World Health Organization (WHO) predicts that the number of glaucoma sufferers in the world will reach around 60.7 million people in 2010 and will be 79.4 million in 2020. Community service activities in the form of health seminars as an educational facility for health workers in PPK 1 regarding early detection in patients with glaucoma so that it can be treated quickly and avoid worsening which can cause loss of vision. Method: The steps include planning, collection of materials and materials, education related to early detection and management of glaucoma, and administering pretest and posttest questionnaires as material for evaluating activities for health workers at PPK1 in the area around the Sebelas Maret University Hospital. Result and Discussion: There were significant differences between subjects regarding knowledge and management of early detection of glaucoma before and after the seminar based on pretest and posttest questionnaires given to 23 seminar participants consisting of health workers, the majority of whom are medical personnel. Conclusion: The service has been carried out well, the participants experienced an increase in knowledge regarding how to detect and treat glaucoma early. [ABSTRACT FROM AUTHOR]

While early diagnosis of brain tumors is essential for improving prognoses, several challenges prevent early diagnosis of these illnesses in low- and middle-income countries (LMICs). The objective of this systematic review is to identify and evaluate the barriers and challenges to early detection of brain tumors in LMICs, as well as to propose potential solutions. A thorough search of the literature was carried out with the aid of multiple databases, including Google Scholar, CINAHL, PubMed, and Scopus. Studies focusing on the early detection of brain tumors in low- and middle-income countries met the inclusion criteria for this review. Articles were screened independently by 2 reviewers. National Surgical, Obstetric, and Anesthesia plan framework was used to extract relevant data and develop themes. Microsoft Excel was then used to arrange and analyze these themes. Out of 21 articles included in the final analysis, notable challenges to early detection of brain tumors included healthcare providers' lack of awareness of early signs and symptoms of brain tumors (21%), limited diagnostic facilities (38%), misdiagnosis by healthcare providers (33%), and financial constraints (46%). Potential strategies for addressing these barriers and challenges include educating healthcare providers on early warning signs (50%), improving the early referral system (25%) and establishing local clinical guidelines (19%). Significant barriers to early detection persist in LMICs. Collaboration between stakeholders, legislators, health ministries, and governmental organizations is essential for developing and implementing focused interventions, such as establishment of referral pathways and specialized centers, adapting guidelines to local cultural contexts and raising public and professional awareness. [ABSTRACT FROM AUTHOR]

The role of digital rectal examination (DRE) in the early detection of significant prostate cancer (PCa) is being questioned in the era of magnetic resonance imaging (MRI). However, some men with suspected PCa may still be identified solely through DRE, even with low serum prostate-specific antigen (PSA) levels. Additionally, most predictive models designed to improve significant PCa diagnostic pathways incorporate DRE findings. We assessed the role of DRE among 5005 men with serum PSA levels > 3.0 ng/mL and/or suspicious DRE findings, who underwent pre-biopsy MRI and targeted and/or systematic biopsies, as part of the significant PCa opportunistic screening program in Catalonia (Spain) between 2016 and 2023. Significant PCa, defined as grade group > 2, was detected in 2097 men (41.9%). Suspicion of PCa was based solely on DRE in 206 cases (4.1%) with significant PCa detected in 50 of them (2.4%). Two pathways using the Barcelona predictive models, before and after MRI, with and without DRE findings showed specificities of 52.8 and 38.7%, respectively (p < 0.001), after fixing sensitivity at 90%. Prostate biopsy was avoided in 35.1 and 26.7%, respectively (p < 0.001), while its efficacy increased from 52.8 to 58%. We conclude that DRE improved the effectiveness of an opportunistic significant PCa-screening program. [ABSTRACT FROM AUTHOR]

Grapevine (Vitis vinifera L.) varieties are particularly susceptible to the pathogens downy mildew Plasmopara viticola and powdery mildew Erysiphe necator. Conventional methods for identifying and classifying spores rely on time-consuming microscopic examinations susceptible to human error and requiring qualified personnel. The aim of the present work has focused on the establishment of a protocol for the rapid molecular detection of the fungal species P. viticola and E. necator from adhesive tapes used to trap spores in airborne inoculum collector cyclones. Four DNA extraction methods were tested. Subsequently, molecular detection of both pathogens was performed by validating some of the specific molecular markers available in the literature. PCR with the primers Nad9 cob-F/Nad9 cob-R and Uncin144/Uncin511 showed specific results for P. viticola and E. necator, respectively, and the best results were obtained with the T-CTAB method. The methodology developed in this work could be of great help for relating direct measurement of P. viticola and E. necator airborne inoculum to disease risk and detection of pathogens, which could be integrated into the early diagnosis of these grapevine pathogens, improving existing warning systems such as Decision Support Systems. [ABSTRACT FROM AUTHOR]

Bud Rot (BR) is the most significant phytosanitary threat to oil palm cultivation in Colombia. Early detection is essential for effective curative management, but current methods for detecting BR in adult palms are subjective and unreliable. This research aimed to develop an integrated system for digital field monitoring and image analysis, testing two detection methods: computer-assisted detection and automatic detection using artificial intelligence (AI). Monthly monitoring was conducted over a 12-month period (January–December 2022) on 672 African oil palms (Elaeis guineensis), 15 years old and susceptible to BR. Disease monitoring focused on the incidence, cumulative incidence, and labor performance based on the number and spatial distribution of palms detected with BR, with or without the use of the device proposed. Results showed that automatic detection using AI had low effectiveness (17.1%), identifying only a small portion of actual cases. In contrast, computer-assisted detection significantly improved accuracy, reaching 78.6% during peak months and reducing detection time by up to two months compared to traditional methods, although, its maximum performance point only reached 4.7 ha/wage. The implementation of digital monitoring provides crucial technological support by considerably improving the effectiveness of early detection in BR curative management. Future advancements in AI-based detection are expected to further improve the efficiency and functionality of this approach. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are the leading cause of death globally. The use of artificial intelligence (AI) methods—in particular, deep learning (DL)—has been on the rise lately for the analysis of different CVD‐related topics. The use of fundus images and optical coherence tomography angiography (OCTA) in the diagnosis of retinal diseases has also been extensively studied. To better understand heart function and anticipate changes based on microvascular characteristics and function, researchers are currently exploring the integration of AI with noninvasive retinal scanning. There is great potential to reduce the number of cardiovascular events and the financial strain on healthcare systems by utilizing AI‐assisted early detection and prediction of cardiovascular diseases on a large scale. A comprehensive search was conducted across various databases, including PubMed, Medline, Google Scholar, Scopus, Web of Sciences, IEEE Xplore, and ACM Digital Library, using specific keywords related to cardiovascular diseases and AI. The study included 87 English‐language publications selected for relevance, and additional references were considered. This article provides an overview of the recent developments and difficulties in using AI and retinal imaging to diagnose cardiovascular diseases. It provides insights for further exploration in this field. Researchers are trying to develop precise disease prognosis patterns in response to the aging population and the growing global burden of CVD. AI and DL are revolutionizing healthcare by potentially diagnosing multiple CVDs from a single retinal image. However, swifter adoption of these technologies in healthcare systems is required. This article is categorized under:Application Areas > Health CareTechnologies > Artificial Intelligence [ABSTRACT FROM AUTHOR]

Introduction: Individuals who develop new‐onset diabetes have been identified as a high‐risk cohort for pancreatic cancer (PC), exhibiting an incidence rate nearly 8 times higher than the general population. Hence, the targeted screening of this specific cohort presents a promising opportunity for early pancreatic cancer detection. We aimed to develop and validate a novel model capable of identifying high‐risk individuals among those with new‐onset diabetes. Methods: Employing the UK Biobank cohort, we focused on those developing new‐onset diabetes during follow‐up. Genetic and clinical characteristics available at registration were considered as candidate predictors. We conducted univariate regression analysis to identify potential indicators and used a 5‐fold cross‐validation method to select optimal predictors for model development. Five machine learning algorithms were used for model development. Results: Among 12,735 patients with new‐onset diabetes, 100 (0.8%) were diagnosed with PC within 2 years. The final model (area under the curve, 0.897; 95% confidence interval, 0.865–0.929) included 5 clinical predictors and 24 single nucleotide polymorphisms. Two threshold cut‐offs were established: 1.28% and 5.26%. The recommended 1.28% cut‐off, based on model performance, reduces definitive testing to 13% of the total population while capturing 76% of PC cases. The high‐risk threshold is 5.26%. Utilizing this threshold, only 2% of the population needs definitive testing, capturing nearly half of PC cases. Conclusions: We, for the first time, combined clinical and genetic data to develop and validate a model to determine the risk of pancreatic cancer in patients with new‐onset diabetes using machine learning algorithms. By reducing the number of unnecessary tests while ensuring that a substantial proportion of high‐risk patients are identified, this tool has the potential to improve patient outcomes and optimize healthcare sources. [ABSTRACT FROM AUTHOR]

Background: The increasing incidence of endometrial cancer (EC) has highlighted the need for improved early detection methods. This study aimed to develop and validate a novel DNA methylation classifier, EMPap, for EC detection using cervical scrapings. Methods: EMPap incorporated the methylation status of BHLHE22 and CDO1, along with age and body mass index (BMI), into a logistic regression model to calculate the endometrial cancer methylation (EM) score for identifying EC in cervical scrapings. We enrolled 1297 patients with highly suspected EC, including 196 confirmed EC cases, and assessed the EMPap performance in detecting EC. Results: EMPap demonstrated robust diagnostic accuracy, with an area under the curve of 0.93, sensitivity of 90.3%, and specificity of 89.3%. It effectively detected EC across various disease stages, grades, and histological subtypes, and consistently performed well across patient demographics and symptoms. EMPap correctly identified 87.5% of the type II ECs and 53.8% of premalignant lesions. Notably, compared with transvaginal ultrasonography (TVS) in patients with postmenopausal bleeding, EMPap exhibited superior sensitivity (100% vs. 82.0%) and specificity (85.2% vs. 38.5%). In asymptomatic postmenopausal women, EMPap maintained high sensitivity (89.5%) and negative predictive value (NPV) (98.3%). Conclusions: This study demonstrated the potential of EMPap as an effective tool for EC detection. Despite the limited sample size, EMPap showed promise for identifying type II EC and detecting over 50% of premalignant lesions. As a DNA methylation classifier, EMPap can reduce unnecessary uterine interventions and improve diagnosis and outcomes. [ABSTRACT FROM AUTHOR]