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Objectives: To elicit the relative importance of the benefits and harms of colorectal cancer (CRC) screening among potential screening participants in the Dutch population. Methods: In a consensus meeting with 11 experts, risk reduction of CRC and CRC deaths (benefits) and complications from colonoscopy, stress of receiving positive fecal immunological test (FIT) results, as well as false-positive and false-negative FIT results (harms) were selected as determinant end points to consider during decision making. We conducted an online best-worst scaling survey among adults aged 55 to 75 years from the Dutch Health Care Consumer Panel of The Netherlands Institute for Health Services Research to elicit preference values for these outcomes. The preference values were estimated using conditional logit regression. Results: Of 265 participants, 234 (89%) had ever participated in CRC screening. Compared with the stress of receiving a positive FIT result, the outcome perceived most important was the risk of CRC death (odds ratio [OR] 4.5; 95% confidence interval [CI] 3.9-5.1), followed by risk of CRC (OR 4.1; 95% CI 3.6-4.7), a false-negative FIT result (OR 3.1; 95% CI 2.7-3.5), colonoscopy complications (OR 1.6; 95% CI 1.4-1.8), and a false-positive FIT result (OR 1.4; 95% CI 1.3-1.6). The magnitude of these differences in perceived importance varied according to age, educational level, ethnic background, and whether the individual had previously participated in CRC screening. Conclusion: Dutch men and women eligible for FIT-based CRC screening perceive the benefits of screening to be more important than the harms.

A large proportion of the insects which have invaded new regions and countries are emerging species, being found for the first time outside their native range. Being able to detect such species upon arrival at ports of entry before they establish in non-native countries is an urgent challenge. The deployment of traps baited with broad-spectrum semiochemical lures at ports-of-entry and other high-risk sites could be one such early detection tool. Rapid progress in the identification of semiochemicals for cerambycid beetles during the last 15 years has revealed that aggregation-sex pheromones and sex pheromones are often conserved at global levels for genera, tribes or subfamilies of the Cerambycidae. This possibly allows the development of generic attractants which attract multiple species simultaneously, especially when such pheromones are combined into blends. Here, we present the results of a worldwide field trial programme conducted during 2018–2021, using traps baited with a standardised 8-pheromone blend, usually complemented with plant volatiles. A total of 1308 traps were deployed at 302 sites covering simultaneously or sequentially 13 European countries, 10 Chinese provinces and some regions of the USA, Canada, Australia, Russia (Siberia) and the Caribbean (Martinique). We intended to test the following hypotheses: 1) if a species is regularly trapped in significant numbers by the blend on a continent, it increases the probability that it can be detected when it arrives in other countries/continents and 2) if the blend exerts an effective, generic attraction to multiple species, it is likely that previously unknown and unexpected species can be captured due to the high degree of conservation of pheromone structures within related taxa. A total of 78,321 longhorned beetles were trapped, representing 376 species from eight subfamilies, with 84 species captured in numbers greater than 50 individuals. Captures comprised 60 tribes, with 10 tribes including more than nine species trapped on different continents. Some invasive species were captured in both the native and invaded continents. This demonstrates the potential of multipheromone lures as effective tools for the detection of ‘unexpected’ cerambycid invaders, accidentally translocated outside their native ranges. Adding new pheromones with analogous well-conserved motifs is discussed, as well as the limitations of using such blends, especially for some cerambycid taxa which may be more attracted by the trap colour or other characteristics rather than to the chemical blend.

Oral cancer forms a major public health issue. In Saudi Arabia, Jazan region has the highest rate of oral cancer; > 35% of total cases. Furthermore, dentists’ engagement in oral cancer screening and patient education in Jazan region is limited. This paper aimed to describe the process used to develop a comprehensive oral cancer (OC) practice intervention to be implemented in dental clinics. The intervention was informed by the six steps of intervention mapping (IM). Steps 1–3 included mixed methods approach of reviewing relevant existing literature, focus group discussions, observations, one-on-one interviews, and questionnaires utilizing the community participatory approach. Step 4 used information form steps 1–3 to develop the intervention components and its associated tools to facilitate its delivery. Steps 5 and 6 specified the prospective plans for implementation and evaluation. ISAC is the developed intervention that comprises the following: Informing dental patients about performing routine OC screenings, Screenings for OC, Advising patients, and Connecting patients to the required services. ISAC practical applications were clustered into two components: (a) didactical session covering aspects related to OC practices and introducing ISAC and (b) practical session that included a step-by-step modeling of the intervention. Using IM facilitated the systematic planning of the ISAC intervention that covers the main issues revealed by the need’s assessments. Working towards developing the ISAC required extensive work in assessing dental public health issues in a specific context with limited data — and this constituted a great challenge. The development of the ISAC was a lesson that casts light on the advantages of engaging multidisciplinary expertise to tackle serious public health issue like OC.

The Covid-19 pandemic has an impact on every aspect of human life, one of which is the mental health of the children. There are drastic changes in the child's life cycle and activities that cause mood fluctuations, feelings of sadness, fear, that cause excessive stress reactions. The Covid-19 pandemic is a difficult situation for children, even the impact affects their future. Therefore, there is a need for early detection to track children's mental health. This research aimed to inform teachers' in Bekasi city of their understanding of children's mental health conditions during the Covid-19 pandemic. The purpose of this research is that teachers will have skills in early detection of mental health in children. The method employed descriptive analysis and used survey methods, and questionnaires in collecting the data. The results of this study reveal that early detection knowledge is very important to be understood by teachers as a preventive step in preventing young children's mental health disorders in the future, so that children can be productive and active again to live their lives

Item does not contain fulltext The purpose of this study was to describe the development and preliminary testing of a new scale assessing knowledge about melanoma risk factors and early detection/secondary prevention. Data was drawn from a longitudinal study assessing barriers and facilitators of skin self- examination among patients diagnosed with melanoma. For the current analysis, 191 patients who completed the new 9-item Knowledge About Melanoma Early Detection Scale and other study measures were included. Exploratory factor analysis with were conducted, which identified a robust scale comprised of 6 items with factor loadings ranging from .56 to .81. Higher scores on the Knowledge About Melanoma Early Detection Scale were associated with younger age and more positive attitudes about melanoma prevention, but not with biological sex, education, melanoma stage, or past self-administered and physician-provided skin checks. Further studies are needed to replicate these findings and to further establish the predictive validity of this scale and its usefulness for health research. 9 p.

El propósito de este trabajo es explorar una tensión y un deseo. La tensión es la que encontramos entre nuestra capacidad para cambiar de opinión, componente crucial casi de cualquier interpretación del progreso, y la tendencia que parecemos exhibir a atrincherarnos en las creencias que están en el centro de la identidad política con la que nos identificamos en un contexto determinado. Nos gustaría ser capaces de detectar y jubilar nuestros prejuicios, de avanzar hacia posiciones más inclusivas y que nos hicieran mejores ciudadanos. Y, sin embargo, nos vemos arrastrados, en ocasiones contra nuestro mejor juicio, hacia posiciones que heredamos de las personas con las que compartimos las cosas que nos importan. A lo largo de este trabajo nos preguntaremos cómo es posible intervenir sobre esta tensión para retener aspectos necesarios de ambas tendencias. En concreto, pensaremos cómo es posible intervenir en este aparente problema cumpliendo un deseo: queremos combatir el mal discurso con más discurso. Las estrategias de intervención destinadas a contrarrestar los efectos negativos de la polarización que desarrollaremos tendrán como objetivo mejorar la calidad de la deliberación democrática. El diálogo con aquellas personas con quienes estamos en desacuerdo nos parece un ideal democrático irrenunciable. No hay, en este sentido, progreso sin desacuerdo., The purpose of this paper is to explore a tension and a desire. The tension is the one we find between our ability to change our mind, a crucial component of almost any conception of progress, and the tendency we apparently show to cling to the core beliefs of the political identity we identify with in a particular context. In order to adopt more inclusive positions and become better citizens, we would like to be able to detect and replace our prejudices. And yet, we are dragged, sometimes against our better judgment, toward positions that we inherited from those with whom we share the things we care about. Throughout this paper we wonder how it is possible to intervene on this tension in order to retain necessary aspects for both tendencies. In particular, we explore how it is possible to intervene on this apparent problem by fulfilling a desire: we want to combat bad discourse with more discourse. The intervention strategies devised to counteract the negative effects of the rise of polarization that we develop are aimed at improving the quality of democratic deliberation. Deliberating with those whom we disagree with seems to us to be an essential democratic ideal. In this sense, there is no progress without disagreement.

Funding: The LUNGSCOT study is funded by the Chief Scientists Office of the Scottish Government, reference HIPS/19/52. Introduction Targeted lung cancer screening is effective in reducing lung cancer and all-cause mortality according to major trials in the United Kingdom and Europe. However, the best ways of implementing screening in local communities requires an understanding of the population the programme will serve. We undertook a study to explore the views of those potentially eligible for, and to identify potential barriers and facilitators to taking part in, lung screening, to inform the development of a feasibility study. Methods Men and women aged 45–70, living in urban and rural Scotland, and either self-reported people who smoke or who recently quit, were invited to take part in the study via research agency Taylor McKenzie. Eleven men and 14 women took part in three virtual focus groups exploring their views on lung screening. Focus group transcripts were transcribed and analysed using thematic analysis, assisted by QSR NVivo. Findings Three overarching themes were identified: (1) Knowledge, awareness and acceptability of lung screening, (2) Barriers and facilitators to screening and (3) Promoting screening and implementation ideas. Participants were largely supportive of lung screening in principle and described the importance of the early detection of cancer. Emotional and psychological concerns as well as system-level and practical issues were discussed as posing barriers and facilitators to lung screening. Conclusions Understanding the views of people potentially eligible for a lung health check can usefully inform the development of a further study to test the feasibility and acceptability of lung screening in Scotland. Patient or Public Contribution The LUNGSCOT study has convened a patient advisory group to advise on all aspects of study development and implementation. Patient representatives commented on the focus group study design, study materials and ethics application, and two representatives read the focus group transcripts. Publisher PDF

Giant splenic artery aneurysms are rare and associated with high morbidity and mortality. Early detection is the key to decreasing morbidity and mortality. We present a giant splenic artery aneurysm which was managed by en bloc resection of the spleen, distal pancreas.

The Asian walnut moth, Garella musculana (Erschov, 1874) (Lepidoptera, Nolidae) is an alien pest originating from Central Asia and is now spreading in Europe, attacking walnut trees. In this study, we updated the current distribution of G. musculana, focusing on the Italian range, where it was reported for the first time in 2021. Field surveys showed an extensive distribution of G. musculana in northern Italy, particularly in the Veneto Region. In this area, the Asian walnut moth developed on English and black walnut, attacking almost exclusively tree shoots. Based on current distribution data, further investigations are required in the nearby regions as well as in those that were less surveyed. Lastly, it is imperative to conduct more studies on insect biology and the impact on walnut production.

Background Workplace absenteeism increases significantly during influenza epidemics. Sick leave records may facilitate more timely detection of influenza outbreaks, as trends in increased sick leave may precede alerts issued by sentinel surveillance systems by days or weeks. Sick leave data have not been comprehensively evaluated in comparison to traditional surveillance methods. The aim of this paper is to study the performance and the feasibility of using a detection system based on sick leave data to detect influenza outbreaks. Methods Sick leave records were extracted from private French health insurance data, covering on average 209,932 companies per year across a wide range of sizes and sectors. We used linear regression to estimate the weekly number of new sick leave spells between 2016 and 2017 in 12 French regions, adjusting for trend, seasonality and worker leaves on historical data from 2010 to 2015. Outbreaks were detected using a 95%-prediction interval. This method was compared to results from the French Sentinelles network, a gold-standard primary care surveillance system currently in place. Results Using sick leave data, we detected 92% of reported influenza outbreaks between 2016 and 2017, on average 5.88 weeks prior to outbreak peaks. Compared to the existing Sentinelles model, our method had high sensitivity (89%) and positive predictive value (86%), and detected outbreaks on average 2.5 weeks earlier. Conclusion Sick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks.

Early detection is important for the management of invasive alien species. In the last decade citizen science has become an important source of such data. Here, we used opportunistic records from the “LIFE ARTEMIS” citizen science project, in which people submitted records from places where they observed tree pests, to understand the distribution of a rapidly-spreading forest pest: the oak lace bug (Corythucha arcuata) in Slovenia. These citizen science records were not distributed randomly. We constructed a species distribution model for C. arcuata that accounted for the biased distribution of citizen science by using the records of other tree pests and diseases from the same project as pseudo-absences (so-called constrained pseudo-absences), and compared this to a model with pseudo-absences selected randomly from across Slovenia. We found that the constrained pseudo-absence model showed that C. arcuata was more likely to be found in east, in places with more oak trees and at lower elevations, and also closer to highways and railways, indicating introduction and dispersal by accidental human transport. The outputs from the model with random pseudo-absences were broadly similar, although estimates from this model tended to be higher and less precise, and some factors that were significant (proximity to minor roads and human settlements) were artefacts of recorder bias, showing the importance of taking the distribution of recording into account wherever possible. The finding that C. arcuata is more likely to be found near highways allows us to design advice for where future citizen science should be directed for efficient early detection.

This community service is carried out in Sugio Lamongan. Sugio village consists of five hamlets, namely Caron, Bulus, Gondangwaruk, Pandanan, and Sugio hamlets. To find out whether there are growth and development problems, it is necessary to detect early growth and development of preschool children and one way so that preschool children get early detection of growth and development is to empower the community in this case by involving PAUD teachers. PAUD teachers can carry out early detection of growth and development continuously or continuously at the end of the semester, media or tools in the form of applications are needed so that PAUD teachers can carry out early detection of electronic-based growth and development. With the increase in science and technology, the internet can be reached to villages including Sugio village. The community service method is carried out with ELENCODE socialization lectures and assistance in implementing early detection of growth and development of preschool children with ELENCODE so that there is an increase in PAUD teacher knowledge about early detection of growth and development and improvement of electronic-based early detection skills of growth and development using ELENCODE., This community service is carried out in Sugio Lamongan. Sugio village consists of five hamlets, namely Caron, Bulus, Gondangwaruk, Pandanan, and Sugio hamlets. To find out whether there are growth and development problems, it is necessary to detect early growth and development of preschool children, and one way so that preschool children get early detection of growth and development is to empower the community in this case by involving PAUD teachers. PAUD teachers can carry out early detection of growth and development continuously or continuously at the end of the semester, media or tools in the form of applications are needed so that PAUD teachers can carry out early detection of electronic-based growth and development. With the increase in science and technology, the internet can be reached in villages including Sugio village. The community service method is carried out with ELENCODE socialization lectures and assistance in implementing early detection of growth and development of preschool children with ELENCODE so that there is an increase in PAUD teacher knowledge about early detection of growth and development and improvement of electronic-based early detection skills of growth and development using ELENCODE.

Anterior tibiofemoral dislocation after total knee arthroplasty is an extremely rare and serious event. Amongst English-published papers, we found only 15 cases, 3 of which presented vascular complications. This manuscript aims to present a 77-year-old woman with a TC-Plus (Smith & Nephew) cruciate-retaining type in first time of knee prosthesis, who suffered an anterior tibiofemoral dislocation and were admitted to our hospital. The clinical management and outcome were evaluated. Furthermore, a review of literature was performed. We concluded that early detection and surgical intervention of the vascular injury is the key in the survival of the limbs. If there is still knee instability after acute recovery, it seems that revision surgery with constrained total knee arthroplasty can bring about good clinical and functional results.

Malware applications are one of the major threats that computing systems face today. While security researchers develop new defense mechanisms to detect malware, attackers continue to release new malware families that evade detection. New defense mechanisms must therefore be developed to effectively counter malware. Hardware Performance Counters (HPCs) have been recently proposed as a means to detect malware. However, recent work has also shown that malware detection is not effective when performance counters are sampled in realistic scenarios. We show how proper data pre-processing and the use of the XGBoost classifier can be used to improve the performance of malware detection using HPCs by at least 15%. We also show that the proposed method can detect malware early (shortly after its launch) by classifying HPC datastreams at short time intervals. In addition, we propose a multi-temporal classification model that ensures the early detection of a high percentage of malware while maintaining overall low false positive rates. Finally, we show that through robust training, the XGBoost classifier shows up to 50x less vulnerability to adversarial attacks that are intended to undermine its malware detection performance.

The electrocardiogram (ECG) plays an important role in systematically assessing cardiac electrical function, but the standard 12-lead ECG only provides only a distant view on cardiac electrical activity. Using non-invasive inverse ECG techniques, additional detailed information on cardiac electrical activity can be obtained by linking cardiac electrical activity to anatomy to enable the identification of subtle disease progression in arrhythmogenic cardiomyopathy. Arrhythmogenic cardiomyopathy is characterized by structural and electrical myocardial remodeling and can manifest as a broad range of lethal phenotypes. In arrhythmogenic cardiomyopathy, electrical remodeling can precede structural and functional changes and sudden cardiac death can be the first disease manifestation. This highlights the need for accurate screening and risk-stratification strategies. The first part of this thesis focusses on describing the optimization of a traditional inverse ECG technique to provide non-invasive insight in endocardial and epicardial cardiac electrical activity by combining 67-lead ECG data with patient specific CT/MRI-based anatomical models. To be able to identify early signs of arrhythmogenic cardiomyopathy development, accurate imaging of sinus rhythm is of importance. Therefore, in Chapter 2, we report on our work regarding the optimization of the inverse ECG technique for the estimation of sinus rhythm and report on its performance (Chapter 3) by comparing it to invasive local activation maps. With the incorporation of a subject-specific anatomy-based model of the His-Purkinje system a physiologically realistic and robust estimation of the ventricular activation sequence is obtained. The optimized inverse ECG technique detected local electrophysiological characteristics in the activation sequence in pathogenic variant carriers with and without any clinical signs of disease (Chapter 4). To further optimize the performance of the inverse ECG technique by developing a new method to model myocardial disease in ECG simulation in Chapter 5 to provide a realistic relation between ECG waveforms and underlying activation sequences. As traditional inverse ECG techniques are mathematically complex and computationally demanding, we focus on CineECG, a new method to image key features of the activation sequence that are difficult to reliably obtain from the ECG. We conceptually validated the technique in cases of bundle branch blocks (Chapter 6) and after evaluation, the method was optimized and validated through a simulation study (Chapter 7). As accurate assessment of subtle ECG changes is limited due to inconsistencies in electrode positioning, we focused on the optimization of the 12-lead ECG acquisition by introducing a 3D-camera based method to reduce electrode placement misplacement (Chapter 8). With this new technique, the identification of subtle changes in the QRS complex during arrhythmogenic cardiomyopathy follow-up may be improved. In Chapter 9, we describe how novel AI-based algorithms may aid current clinical practice together with its potential benefits and challenges. With such algorithms, the complex nature of disease progression in arrhythmogenic cardiomyopathy may be further unraveled. To conclude the thesis, the application of techniques presented in this thesis to enhance diagnosis and risk-stratification in arrhythmogenic cardiomyopathy is described (Chapter 10). The techniques are viewed within the context of possible fields of application in current clinical practice.

Monitoring the plant’s health and early detection of disease are essential to facilitate effective management, decrease disease spread, and minimize yield loss. Spectroscopic techniques in remote sensing offer less laborious methods and high spatiotemporal scale to monitor diseases in crops. Spectral measurements during the development of disease infection may reveal differences among diseases and determine the stage it can be effectively detected. In this study, spectral analysis was performed over the visible and near-infrared (400–850 nm) portions of the spectrum to detect and differentiate three major rice diseases in the Philippines, namely tungro, BLB, and blast disease. Reflectance of infected rice leaves was recorded repeatedly from inoculation to the late stage of each disease. Results show that spectral reflectance is characteristically affected by each disease, resulting in different spectral, signature sensitivity, and first-order derivatives. Red and red-edge wavelength ranges are the most sensitive to the three diseases. Near-infrared wavelengths decreased as tungro and blast diseases progressed. In addition, the spectral reflectance was resampled to common reflectance sensitivity bands of optical sensors and used in the cluster analysis. It showed that BLB and blast can be detected in the early disease stage on the IRRI Standard Evaluation System (SES) scale of 1 and 3, respectively. Alternatively, tungro was detected in its later stage, with an 11–30% height reduction and no distinct yellow to yellow-orange discoloration (5 SES scale). Three regression techniques, Partial Least Square, Random Forest, and Support Vector Regression were performed separately on each disease to develop models predicting its severity. The validation results of the PLSR and SVR models in tungro and blast show accuracy levels that are promising to be used in estimating the severity of the disease in leaves while RFR shows the best results for BLB. Early disease detection and regression models from spectral measurements and analysis for disease severity estimation can help in disease monitoring and proper disease management implementation.

PurposePregnancy is a risk period for the development of mental disorders. About 10% of pregnant women worldwide experience a mental disorder, mainly depression, and this percentage has been aggravated by the COVID-19 pandemic. This study aims to understand the impact of COVID-19 on the mental health of pregnant women.MethodsThree hundred and one pregnant women in the week 21.85 +/- 9.9 were recruited through social media and pregnant women forums from September 2020 to December 2020. A multiple-choice questionnaire was administered to evaluate the sociodemographic characteristics of the women, the care provided, and different aspects related to COVID-19. A Beck Depression Inventory was also delivered.ResultsOf the pregnant women 23.5% had seen or had considered seeing a mental health professional during pregnancy. Predictive models using multivariate logistic regression found that this fact was associated with an increased risk of depression (OR = 4.22; CI 95% 2.39-7.52; P < 0.001). Among women with moderate-severe depression, it was associated with an increased risk of having suicidal thoughts (OR = 4.99; CI 95% 1.11-27.9; P = 0.044) and age was found to be a protective variable (OR = 0.86; CI 95% 0.72-0.98; P = 0.053).ConclusionsThe COVID-19 pandemic represents a major mental health challenge for pregnant women. Despite the decrease in face-to-face visits, there are opportunities for health professionals to identify the existence of psycho-pathological alterations and suicidal ideation by asking the patient if she is seeing or considering seeing a mental health professional. Therefore, it is necessary to develop tools for early identification to ensure correct detection and care. SignificanceAmong women with moderate-severe depression, considered seeing a mental health professional during pregnancy was associated with an increased risk of having suicidal thoughts and age was found to be a protective variable. Despite the decrease in face-to-face visits during COVID-19 pandemic, there are opportunities for health professionals to identify the existence of psycho-pathological alterations and suicidal ideation by asking the patient if she is seeing or considering seeing a mental health professional. Asking about their intention to seek specialized assistance can help to identify those women at risk of suffering affective disorders such as depression and suicidal ideation and to individually plan future medical visits., Universidad de Granada/ CBUA

The early identification of the spreading patterns of an epidemic infectious disease is an important first step towards the adoption of effective interventions. We developed a simple regression-based method to estimate the directional speed of a disease’s spread, which can be easily applied with a limited dataset. We tested the method using simulation tools, then applied it on a real case study of an African Swine Fever (ASF) outbreak identified in late 2021 in northwestern Italy. Simulations showed that, when carcass detection rates were 0.1, the model produced asymptotically unbiased and progressively more predictable estimates. The model produced rather different estimates of ASF’s spreading speed in different directions of northern Italy, with the average speed ranging from 33 to 90 m/day. The resulting ASF-infected areas of the outbreak were estimated to be 2216 km2, about 80% bigger than the ones identified only thorough field-collected carcasses. Additionally, we estimated that the actual initial date of the ASF outbreak was 145 days earlier than the day of first notification. We recommend the use of this or similar inferential tools as a quick, initial way to assess an epidemic’s patterns in its early stages and inform quick and timely management actions.

Lung cancer is the leading cause of cancer-related mortality worldwide. Early detection is essential to achieving a better outcome and prognosis. Volatile organic compounds (VOCs) reflect alterations in the pathophysiology and body metabolism processes, as shown in various types of cancers. The biosensor platform (BSP) urine test uses animals’ unique, proficient, and accurate ability to scent lung cancer VOCs. The BSP is a testing platform for the binary (negative/positive) recognition of the signature VOCs of lung cancer by trained and qualified Long–Evans rats as biosensors (BSs). The results of the current double-blind study show high accuracy in lung cancer VOC recognition, with 93% sensitivity and 91% specificity. The BSP test is safe, rapid, objective and can be performed repetitively, enabling periodic cancer monitoring as well as an aid to existing diagnostic methods. The future implementation of such urine tests as routine screening and monitoring tools has the potential to significantly increase detection rate as well as curability rates with lower healthcare expenditure. This paper offers a first instructive clinical platform utilizing VOC’s in urine for detection of lung cancer using the innovative BSP to deal with the pressing need for an early lung cancer detection test tool.

The operation and maintenance of wind turbines benefit from reliable information on the wind turbine condition. Data-driven models use data from the supervisory data acquisition system. In particular, great performance is reported for artificial intelligence models. However, the lack of interpretability limits their effective industrial implementation. The present work introduces a new condition-monitoring approach for wind turbines featuring a built-in visualization tool that confers interpretability upon the model outcomes. The proposed approach is based on a supervised implementation of the variational autoencoder model, which allows the projection of the wind turbine system onto a low-dimensional representation space. Three outcomes follow from such representation: a health indicator for the early detection of abnormal conditions, a classifier providing the diagnosis status, and a visualization tool depicting the wind turbine condition as a trajectory in a 2D plot. The approach is implemented with a vast database. Two case studies demonstrate the potential of the proposed approach. The proposed health indicator detects the main bearing overtemperature 11 days before the control system alarm, one week earlier than a competing approach. Study cases illustrate that the built-in visualization tool enhances the interpretability and trust in the model outcomes, thus supporting wind turbine operation and maintenance.

Introduction: Accurate and actionable diagnosis of Acute Kidney Injury (AKI) ahead of time is important to prevent or mitigate renal insufficiency. The purpose of this study was to evaluate the performance of Kinetic estimated Glomerular Filtration Rate (KeGFR) in timely predicting AKI in critically ill septic patients. Methods: We conducted a retrospective analysis on septic ICU patients who developed AKI in AmsterdamUMCdb, the first freely available European ICU database. The reference standard for AKI was the Kidney Disease: Improving Global Outcomes (KDIGO) classification based on serum creatinine and urine output (UO). Prediction of AKI was based on stages defined by KeGFR and UO. Classifications were compared by length of ICU stay (LOS), need for renal replacement therapy and 28-day mortality. Predictive performance and time between prediction and diagnosis were calculated. Results: Of 2492 patients in the cohort, 1560 (62.0%) were diagnosed with AKI by KDIGO and 1706 (68.5%) by KeGFR criteria. Disease stages had agreement of kappa = 0.77, with KeGFR sensitivity 93.2%, specificity 73.0% and accuracy 85.7%. Median time to recognition of AKI Stage 1 was 13.2 h faster for KeGFR, and 7.5 h and 5.0 h for Stages 2 and 3. Outcomes revealed a slight difference in LOS and 28-day mortality for Stage 1. Conclusions: Predictive performance of KeGFR combined with UO criteria for diagnosing AKI is excellent. Compared to KDIGO, deterioration of renal function was identified earlier, most prominently for lower stages of AKI. This may shift the actionable window for preventing and mitigating renal insufficiency.

Reducing the age of first autism diagnosis facilitates access to critical early intervention services. A current "waitlist crisis" for autism diagnostic evaluation thus demands that we consider novel use of available clinical resources. Previous work has found that expert autism clinicians can identify autism in young children with high specificity after only a brief observation; rapid identification by non-experts remains untested. In the current study, 252 children ages 12-53 months presented for a comprehensive autism diagnostic evaluation. We found that junior clinicians in training to become autism specialists (n = 29) accurately determined whether or not a young child would be diagnosed with autism in the first five minutes of the clinic visit in 75% of cases. Specificity of brief observations was high (0.92), suggesting that brief observations may be an effective tool for triaging young children toward autism-specific interventions. In contrast, the lower negative predictive value (0.71) of brief observations, suggest that they should not be used to rule out autism. When trainees expressed more confidence in their initial impression, their impression was more likely to match the final diagnosis. These findings add to a body of literature showing that clinical observations of suspected autism should be taken seriously, but lack of clinician concern should not be used to rule out autism or overrule other indicators of likely autism, such as parent concern or a positive screening result.

Pre-eclampsia is a severe disorder that can affect the health of pregnant women and their unborn babies. Early detection and appropriate interventions are crucial to prevent severe complications. In this context, the role of midwives is essential in managing pre-eclampsia. This study aims to explore the role of midwives in the early detection and intervention of pre-eclampsia. The research method employed in this study is a Systematic Literature Review utilizing academic journal search engines from online databases such as PubMed, Web of Science, and Scopus, published between 2007 and 2022. The role of midwives in the early detection and intervention of pre-eclampsia is essential to ensure that the condition is identified and managed effectively to safeguard the health of both the mother and the baby. Research findings indicate that midwives possess good knowledge of the signs and symptoms of pre-eclampsia, as well as early detection methods such as blood pressure measurement and monitoring vital signs. Midwives are also recognized to be critical in managing pre-eclampsia, including using safe and effective medications, appropriate medical interventions, and monitoring and addressing potential complications. Enhancing knowledge, skills, and interprofessional collaboration is critical to effectively managing pre-eclampsia. Through these measures, it is hoped that complications associated with pre-eclampsia can be reduced and healthier pregnancy outcomes can be achieved. However, midwives face several challenges in the early detection of pre-eclampsia, such as limited resources and infrastructure. Therefore, efforts are needed to enhance the knowledge and skills of midwives in monitoring and interpreting laboratory test results, such as urine protein levels.

Ransomware is a type of malware that employs encryption to target user files, rendering them inaccessible without a decryption key. To combat ransomware, researchers have developed early detection models that seek to identify threats before encryption takes place, often by monitoring the initial calls to cryptographic APIs. However, because encryption is a standard computational activity involved in processes, such as packing, unpacking, and polymorphism, the presence of cryptographic APIs does not necessarily indicate an imminent ransomware attack. Hence, relying solely on cryptographic APIs is insufficient for accurately determining a ransomware pre-encryption boundary. To this end, this paper is devoted to addressing this issue by proposing a Temporal Data Correlation method that associates cryptographic APIs with the I/O Request Packets (IRPs) based on the timestamp for pre-encryption boundary delineation. The process extracts the various features from the pre-encryption dataset for use in early detection model training. Several machine and deep learning classifiers are used to evaluate the accuracy of the proposed solution. Preliminary results show that this newly proposed approach can achieve higher detection accuracy compared to those reported elsewhere.

Purpose:: Alzheimer’s disease (AD) has the highest worldwide prevalence of all neurodegenerative disorders, no cure, and low ratios of diagnosis accuracy at its early stage where treatments have some effect and can give some years of life quality to patients. This work aims to develop an automatic method to detect AD in 3 different stages, namely, control (CN), mild-cognitive impairment (MCI), and AD itself, using structural magnetic resonance imaging (sMRI). Methods:: A set of co-occurrence matrix and texture statistical measures (contrast, correlation, energy, homogeneity, entropy, variance, and standard deviation) were extracted from a two-level discrete wavelet transform decomposition of sMRI images. The discriminant capacity of the measures was analyzed and the most discriminant ones were selected to be used as features for feeding classical machine learning (cML) algorithms and a convolution neural network (CNN). Results:: The cML algorithms achieved the following classification accuracies: 93.3% for AD vs CN, 87.7% for AD vs MCI, 88.2% for CN vs MCI, and 75.3% for All vs All. The CNN achieved the following classification accuracies: 82.2% for AD vs CN, 75.4% for AD vs MCI, 83.8% for CN vs MCI, and 64% for All vs All. Conclusion:: In the evaluated cases, cML provided higher discrimination results than CNN. For the All vs All comparison, the proposedmethod surpasses by 4% the discrimination accuracy of the state-of-the-art methods that use structural MRI.

Introduction: A pre-experimental study was undertaken with one group pretest posttest design to find the effectiveness of Information booklet on knowledge of female school teachers regarding prevention and early detection of cervical cancer in selected secondary schools, Mairang Town, Meghalaya State. Objectives: The main objectives of the study were to assess the knowledge of female school teachers on prevention and early detection of cervical cancer before and after administering the information booklet and to evaluate the effectiveness of information booklet on prevention and early detection of cervical cancer in terms of gain in knowledge. Material and Methods: The conceptual frame work adopted for the study was based on Health Belief Model. The sample size is 30, non-probability purposive sampling is the sampling technique adopted. The data was collected by using the structured knowledge questionnaire. Result: The findings of the study revealed that the mean posttest knowledge scores (23.17) of the teachers was higher than the pretest knowledge scores (12.83). The obtained mean difference was found to be statistically significant at 0.05 level of significance, as evident from calculated ‘t’(29)= 20.09, there by indicating the effectiveness of the information booklet regarding prevention and early detection of cervical cancer. Conclusion : The information Booklet on prevention and early detection of cervical cancer was effective for increasing the knowledge

Objective: Globally, cardiovascular diseases (CVDs) are one of the most leading causes of death. In medical screening and diagnostic procedures of CVDs, electrocardiogram (ECG) signals are widely used. Early detection of CVDs requires acquisition of longer ECG signals. It has triggered the development of personal healthcare systems which can be used by cardio-patients to manage the disease. These healthcare systems continuously record, store, and transmit the ECG data via wired/wireless communication channels. There are many issues with these systems such as data storage limitation, bandwidth limitation and limited battery life. Involvement of ECG data compression techniques can resolve all these issues. Method: In the past, numerous ECG data compression techniques have been proposed. This paper presents a methodological review of different ECG data compression techniques based on their experimental performance on ECG records of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Results: It is observed that experimental performance of different compression techniques depends on several parameters. The existing compression techniques are validated using different distortion measures. Conclusion: This study elaborates advantages and disadvantages of different ECG data compression techniques. It also includes different validation methods of ECG compression techniques. Although compression techniques have been developed very widely but the validation of compression methods is still a prospective research area to accomplish an efficient and reliable performance.

The most effective treatment for lung cancer is complete lung resection, although recurrences reach up to 10% and the appearance of second neoplasms, up to 6%. Therefore, the follow-up of these patients will be essential for the early detection and treatment of these events; however, there is no definition of the form, time and cadence of these follow-ups. In this consensus document, we try to define them based on the available scientific evidence. A critical review of the literature is carried out (meta-analysis, systematic reviews, reviews, consensus recommendations of scientific societies, randomized controlled studies, non-randomized controlled studies, observational studies and case series studies) and communications to the main congresses on oncology and thoracic surgery in Spanish, English and French. The evidences found are classified following the GRADE system. It is defined according to the existing evidence that the patient resected for lung cancer should be followed up, as well as that this follow-up should be close during the first years and with CT (not being necessary to follow up with PET-CT, biomarkers or bronchoscopy). Cessation of smoking is also recommended in this follow-up.

Digital technology is now an integral part of medicine. Tools for detecting, screening, diagnosis, and monitoring health-related parameters have improved patient care and enabled individuals to identify issues leading to better management of their own health. Wearable technologies have integrated sensors and can measure physical activity, heart rate and rhythm, and glucose and electrolytes. For individuals at risk, wearables or other devices may be useful for early detection of atrial fibrillation or sub-clinical states of cardiovascular disease, disease management of cardiovascular diseases such as hypertension and heart failure, and lifestyle modification. Health data are available from a multitude of sources, namely clinical, laboratory and imaging data, genetic profiles, wearables, implantable devices, patient-generated measurements, and social and environmental data. Artificial intelligence is needed to efficiently extract value from this constantly increasing volume and variety of data and to help in its interpretation. Indeed, it is not the acquisition of digital information, but rather the smart handling and analysis that is challenging. There are multiple stakeholder groups involved in the development and effective implementation of digital tools. While the needs of these groups may vary, they also have many commonalities, including the following: a desire for data privacy and security; the need for understandable, trustworthy, and transparent systems; standardized processes for regulatory and reimbursement assessments; and better ways of rapidly assessing value.

For early detection of surgical site infection (SSI) following spinal decompression surgery, we compared temporal changes in the values of laboratory markers that are not affected by operative parameters.The study included 302 patients, which were divided into an SSI group (patients who developed deep SSI) and a non-SSI group for analysis. We reviewed data on C-reactive protein level, total white blood cell (WBC) count, and WBC differential percentage and count before spinal decompression, on postoperative day 1, and on postoperative day 4. We identified laboratory markers that are not affected by operative parameters (operating time, intraoperative blood loss, and number of operative segments). Laboratory markers with a significant difference observed between the peak or nadir value and the value in the subsequent survey day were considered as an indicator of SSI. We examined the utility of each indicator by calculating sensitivity and specificity. Furthermore, we investigated the utility of the combination of all five indicators (wherein the recognition of one marker was considered positive).Temporal changes in five laboratory markers were considered indicators of SSI. The changes from postoperative day 1 to postoperative day 4 were as follows: (1) increased WBC count (42% sensitivity, 88% specificity), (2) increased neutrophil percentage (25% sensitivity, 96% specificity), (3) increased neutrophil count (25% sensitivity, 94% specificity), (4) decreased lymphocyte percentage (25% sensitivity, 95% specificity), and (5) decreased lymphocyte count (25% sensitivity, 85% specificity). The combination of these five markers showed a 50% sensitivity, 81% specificity, and 0.65 AUC.Five markers were found to be reliable indicators of SSI following spinal decompression surgery because they were not affected by operative parameters. The combination of all five indicators had moderate sensitivity and high specificity. Therefore, this may be reliable and useful for the early detection of SSI.

application/pdf, 本稿は，ヤングケアラーが直面している困難について，今までの生徒調査や学校調査で明らかになった実態及び研究報告を文献的かつ批判的に咀嚼しつつ，具体的施策を構想するときの視点を提起することを目的として論究したものである．ヤングケアラーは教育現場での早期発見が難しいため深刻化して判明するケースが少なくない．その背景には多様・複合したケアに苦しむ家族が存在していること，老老介護や8050問題と連続していること，若者の将来に影響を及ぼすこと等が導き出された．ヤングケアラーの生活する家族は，介護対象者の状態，世帯人数と子ども数，介護対象者の人数，介護内容，世帯の生活状態など，多くの属性が組み合わされた家庭であるため，その困難はときに多様・複合的である．厚生労働省が打ち出した重層的支援体制整備事業は，市町村による任意事業である点や財源等にも多くの課題があり，支援の充実を早急にはかっていく必要がある．

Los problemas en salud mental infantil son frecuentes, complejos y pueden conllevar un riesgo en el desarrollo. La prevención en salud mental es un compromiso colectivo, especialmente por parte de las instituciones que atienden la primera infancia: la escuela y la consulta pediátrica. El objetivo de esta investigación es validar un instrumento diseñado ad hoc de detección precoz en salud mental para la primera infancia. En el diseño del instrumento, denominado Detectar para Prevenir-Salud Mental Infantil-25 (DxP-SMI-25), se tuvo en cuenta la literatura previa y las limitaciones actuales en la detección precoz en salud mental. El contenido del cuestionario se validó mediante un juicio de expertos y se aplicó el cuestionario, junto al Ages & Statges Questionnaires: Social-Emotional-2, con el fin de obtener la validez de criterio. El instrumento diseñado muestra una buena predisposición y permite una evaluación global del desarrollo. Child mental health problems are frequent, complex and can lead to developmental risk. Prevention in mental health is a collective commitment, especially on the part of the institutions that care for early childhood: the school and the pediatric office. The aim of this paper is to validate an ad hoc instrument designed for early mental health screening in early childhood. The design of the instrument, called Detectar para Prevenir- Salud Mental Infantil-25 (DxP-SMI-25, Detect to Prevent- Children’s Mental Health-25), took into account previous literature and current limitations in early mental health screening. The content of the questionnaire was validated by means of expert judgment and the questionnaire was applied, together with the Ages & Statges Questionnaires: Social-Emotional-2, in order to obtain criterion validity. The designed instrument shows a good predisposition and allows a global assessment of development.

BACKGROUND: Autistic Spectrum Disorder (ASD) is a neurodevelopment condition that is normally linked with substantial healthcare costs. Typical ASD screening techniques are time consuming, so the early detection of ASD could reduce such costs and help limit the development of the condition. OBJECTIVE: We propose an automated approach to detect autistic traits that replaces the scoring function used in current ASD screening with a more intelligent and less subjective approach. METHODS: The proposed approach employs deep neural networks (DNNs) to detect hidden patterns from previously labelled cases and controls, then applies the knowledge derived to classify the individual being screened. Specificity, sensitivity, and accuracy of the proposed approach are evaluated using ten-fold cross-validation. A comparative analysis has also been conducted to compare the DNNs’ performance with other prominent machine learning algorithms. RESULTS: Results indicate that deep learning technologies can be embedded within existing ASD screening to assist the stakeholders in the early identification of ASD traits. CONCLUSION: The proposed system will facilitate access to needed support for the social, physical, and educational well-being of the patient and family by making ASD screening more intelligent and accurate.

Non-motor manifestations of Parkinson’s disease (PD) appear early and have a significant impact on the quality of life of patients, but few studies have evaluated their predictive potential with machine learning algorithms. We evaluated 9 algorithms for discriminating PD patients from controls using a wide collection of non-motor clinical PD features from two databases: Biocruces (96 subjects) and PPMI (687 subjects). In addition, we evaluated whether the combination of both databases could improve the individual results. For each database 2 versions with different granularity were created and a feature selection process was performed. We observed that most of the algorithms were able to detect PD patients with high accuracy (>80%). Support Vector Machine and Multi-Layer Perceptron obtained the best performance, with an accuracy of 86.3% and 84.7%, respectively. Likewise, feature selection led to a significant reduction in the number of variables and to better performance. Besides, the enrichment of Biocruces database with data from PPMI moderately benefited the performance of the classification algorithms, especially the recall and to a lesser extent the accuracy, while the precision worsened slightly. The use of interpretable rules obtained by the RIPPER algorithm showed that simply using two variables (autonomic manifestations and olfactory dysfunction), it was possible to achieve an accuracy of 84.4%. Our study demonstrates that the analysis of non-motor parameters of PD through machine learning techniques can detect PD patients with high accuracy and recall, and allows us to select the most discriminative non-motor variables to create potential tools for PD screening. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was partially funded by the Department of Education, Universities and Research of the Basque Government (ADIAN, IT-980-16); by the Spanish Ministry of Science, Innovation and Universities - National Research Agency and the European Regional Development Fund - ERDF (PhysComp, TIN2017-85409-P), and from the State Research Agency (AEI, Spain) under grant agreement No RED2018-102312-T (IA-Biomed); by Michael J. Fox Foundation [RRIA 2014 (Rapid Response Innovation Awards) Program (Grant ID: 10189)]; by the Instituto de Salud Carlos III through the project “PI14/00679” and “PI16/00005”, the Juan Rodes grant “JR15/00008” (IG) (Co-funded by European Regional Development Fund/European Social Fund - “Investing in your future”); and by the Department of Health of the Basque Government through the projects “2016111009” and “2019111100”.

Crypto ransomware is a type of malware that locks its victim’s file for ransom using an encryption algorithm. Its popularity has risen at an alarming rate among the cyber security community due to several successful worldwide attacks. The encryption employed had caused irreversible damage to the victim’s digital files, even when the victim chooses to pay the ransom. Therefore, this research proposes the Pre-Encryption Detection Algorithm (PEDA) that can detect crypto-ransomware at the pre-encryption stage, when no encryption has been done. PEDA provides two levels of detection; the first level of detection was before the ransomware can be activated using a signature comparison with a known crypto-ransomware’s signature. The signature was generated using SHA-256 (Secure Hashing Algorithm) that allowed fast and accurate comparison of the file content. The second level of detection used Learning Algorithm (LA) that can detect crypto-ransomware based on pre-encryption application program interface (API). The LA produced a 100% recall rate based on 80:20 ratios of training and testing, and 99.9% recall rate with a 10-fold cross-verification test. In addition, this research had also successfully identified fourteen important APIs that can differentiate between ransomware and goodware. Three APIs were present in most ransomware, but less in goodware; these APIs were NtProtectVirtualMemory, NtResumeThread, and NtTerminateProcess. Eleven APIs, on the other hand, were mostly present in goodware, but less in ransomware; these APIs were NtWriteVirtualMemory, UuidCreate, NtDelayExecution, NtSetInformationFile, NtWriteFile, CreateThread, NtReadVirtualMemory, VirtualFreeEx, CreateDirectoryW, VirtualProtectEx, and SetFilePointer.

Resumo Introducao: A insuficiencia cardiaca (IC) descompensada esta associada a mau prognostico a curto e longo prazo. A monitorizacao remota invasiva da pressao na arteria pulmonar (PAP) possibilita a detecao precoce da descompensacao da IC, previamente ao inicio dos sintomas, podendo melhorar os resultados clinicos. Descrevemos a experiencia inicial com a utilizacao do sistema de monitorizacao remota CardioMEMS™ em doentes com IC, incluindo a sua seguranca e eficacia. Metodos e resultados: Foram incluidos prospectivamente, 5 doentes com IC em classe III da New York Heart Association, com pelo menos uma hospitalizacao por IC descompensada nos ultimos 12 meses, submetidos a implantacao de sistema de monitorizacao remota invasiva da PAP. A mediana de idades foi de 66.0 anos (intervalo interquartil [IIQ] 50.5–77.5 anos), 80.0% eram do sexo masculino e todos apresentavam IC com fracao de ejecao reduzida. O sensor de PAP foi colocado num ramo da arteria pulmonar esquerda em todos os doentes, nao tendo ocorrido complicacoes major. Durante o follow-up mediano de 40 dias (IIQ 40-61 dias), foram transmitidas um total de 271 leituras, verificando-se uma adesao dos doentes de 100% e taxa de transmissao bem sucedida de 98.1%. A PAP manteve-se dentro do objetivo em tres doentes durante o follow-up. Apesar de continuarem assintomaticos, dois doentes apresentaram valores das PAP acima do alvo. Foi realizado ajuste dietetico e da dose de diuretico, sem necessidade de visitas clinicas presenciais, objetivando-se uma reducao efetiva das PAP. Durante o seguimento, nao se registaram hospitalizacoes por IC ou obitos. Conclusao: A monitorizacao da IC guiada pela hemodinâmica demonstrou ser segura e eficaz, podendo assumir-se como uma ferramenta util no manejo de doentes com IC, particularmente no contexto da pandemia COVID-19, quando e desejavel uma reducao do numero de consultas presenciais hospitalares. Background: Decompensated heart failure (HF) is associated with poor short- and long-term prognosis. Remote invasive monitoring of pulmonary artery pressures (PAP) enables early detection of HF decompensation, before symptoms occur, and may improve clinical outcomes. We aimed to describe our initial experience with the use of the CardioMEMS™ remote monitoring system in patients with HF, including its safety and effectiveness. Methods and results: Five patients with HF in New York Heart Association class III and at least one hospitalization due to decompensated HF in last 12 months, who underwent invasive remote monitoring of PAP, were included in this prospective registry. The median age was 66.0 years (interquartile range [IQR] 50.5–77.5 years), 80.0% were men and all had HF with reduced ejection fraction. The pulmonary artery (PA) sensor was placed in a left PA branch in all patients and no major procedural complications occurred. In median follow-up of 40 days (IQR 40–61 days), a total of 271 pressure readings were transmitted, patient compliance was 100% and freedom from sensor failure 98.1%. In three patients, PAP remained within the goal during follow-up. Two patients presented an increase in PAP to values above the targets, despite the absence of symptoms worsening. These required dietary and diuretic dose adjustment, without the need for outpatient clinic visits, which reduced PAP. No hospitalizations for HF or deaths occurred during follow-up. Conclusion: Hemodynamic-guided HF monitoring was safe and effective and may be a useful adjunctive tool to the standard-of-care management in selected HF patients, particularly in the context of the COVID-19 pandemic, where a reduction in the number of health care visits may be desirable.

Blast furnaces are the most crucial equipment in ironmaking processes. Stable operation of the blast furnace is a prerequisite for personnel safety and production efficiency. Therefore, early detection of abnormalities in blast furnaces is an important task for ironmaking processes. However, owing to the large fluctuations in the quality of raw materials, dynamic operating conditions, as well as the impact of the hot blast stoves switches, the measurements of blast furnace show severe nonstationary characteristics. All these factors make monitoring the blast furnace a challenging task. In this article, a nonstationary process monitoring method called consistent trend feature analysis (CTFA) is proposed, which can extract the trend-related features and discard perturbations in process data. The directions and amplitudes of the extracted trends are used for abnormality detection, and a local-learning-based method is proposed for determining a time-varying control limit. The detection performance of the proposed method is analyzed, with a sufficient condition and a necessary condition for the detectability given. The effectiveness of the proposed method is validated by the practical data collected from a large-scale blast furnace located in Liuzhou, China.

Increasing evidence has linked the humoral immune response with the development of various cancers. Therefore, there is growing interest in investigating the predictive value of antibodies to assess overall and tissue site-specific cancer risk. Given the large amount of antibody types and the broad scope of the search (i.e. cancer risk), the primary aim of this systematic review was to present an overview of the most researched antibodies (i.e. immunoglobulin (Ig) isotypes (IgG, IgM, IgA, and IgE), tumour and self-antigen-reactive antibodies, infection-related antibodies) in relation to overall and site-specific cancer risk. We identified various antibody types that have been associated with the risk of cancer. While no significant associations were found for IgM serum levels, studies found an inconsistent association among IgE, IgA, and IgG serum levels in relation to cancer risk. When evaluating antibodies against infectious agents, most studies reported a positive link with specific cancers known to be associated with the specific agent recognized by serum antibodies (i.e. helicobacter pylori and gastric cancer, hepatitis B virus and hepatocellular carcinoma, and human papillomavirus and cervical cancer). Several reports identified autoantibodies, as single biomarkers (e.g. anti-p53, anti-MUC1, and anti-CA125) but especially in panels of multiple autoantibodies, to have potential as diagnostic biomarkers for specific cancer types. Overall, there is emerging evidence associating certain antibodies to cancer risk, especially immunoglobulin isotypes, tumour-associated antigen-specific, and self-reactive antibodies. Further experimental studies are necessary to assess the efficacy of specific antibodies as markers for the early diagnosis of cancer.

Aim of the work: Measuring serum endocan level to determine its potential role in detecting subclinical cardiovascular involvement in psoriatic patients with or without arthritis. Patients and methods: This work included 14 psoriatic arthritis (PsA) patients, 14 psoriasis only (PsO) patients, and 14 age and sex matched controls. The psoriasis area severity index was evaluated. Serum endocan level was measured, subclinical atherosclerosis was assessed using brachial artery flow-mediated vasodilation (FMD), and echocardiography: standard and tissue Doppler imaging (TDI) was performed. Results: The mean age of PsA patients was 38.3 ± 9.9 years and for PsO was 37.9 ± 8 years. They were 3 males and 11 females in both groups with a comparable psoriasis duration (11 ± 4.9 vs 8 ± 6.3 years; p = 0.17). PsA patients had significantly increased endocan level (618 ± 227.8 ng/L) compared to those with PsO or controls (359 ± 185.7 and 130.6 ± 38.2 ng/L respectively; p

During the past few years, the traffic volume of legitimate traffic and attack traffic has increased manifolds up to Terabytes per second (Tbps). Because of the processing of such a huge traffic volume, it has become implausible to detect high rate attacks in time using conventional DDoS defense architectures. At present, the majority of the DDoS defense systems are deployed predominantly at the victim-end domain But these victim-end defense systems themselves are vulnerable to HR-DDoS attacks as the mammoth volume of attack traffic is generated by such type of attacks. The insufficient computational resources further make the problem more crucial at the victim-end. This paper proposed a distributed and collaborative architecture called E-Had that is capable of efficiently processing a large amount of data by distributing it among a number of mappers and reducers in a Hadoop based cluster. The proposed E-Had system has been comprehensively validated using various publicly available benchmarked datasets and real datasets generated in HA-DDoS testbed in terms of various detection system evaluation metrics. The experimental results clearly show that the proposed detection system is capable of early detection of different scenarios of DDoS attacks along with differentiating them from flash crowds.

The massive and growing burden imposed on modern society by depression has motivated investigations into early detection through automated, scalable and non-invasive methods, including those based on speech. However, speech-based methods that capture articulatory information effectively across different recording devices and in naturalistic environments are still needed. This article proposes two feature sets associated with speech articulation events based on counts and durations of sequential landmark groups or n-grams. Statistical analysis of the duration-based features reveals that durations from several consecutive landmark bigrams and onset-offset landmark pairs are significant in discriminating depressed from non-depressed speakers. In addition to investigating different normalization approaches and values of n for landmark n-gram features, experiments across different elicitation tasks suggest that the features can be tailored to capture different articulatory aspects of depressed voices. Evaluations of both landmark duration features and landmark n-gram features on the DAIC-WOZ and SH2 datasets show that they are highly effective, either alone or fused, relative to existing approaches.

Dermatologists often diagnose or rule out early melanoma by evaluating the follow-up dermoscopic images of skin lesions. However, existing algorithms for early melanoma diagnosis are developed using single time-point images of lesions. Ignoring the temporal, morphological changes of lesions can lead to misdiagnosis in borderline cases. In this study, we propose a framework for automated early melanoma diagnosis using sequential dermoscopic images. To this end, we construct our method in three steps. First, we align sequential dermoscopic images of skin lesions using estimated Euclidean transformations, extract the lesion growth region by computing image differences among the consecutive images, and then propose a spatio-temporal network to capture the dermoscopic changes from aligned lesion images and the corresponding difference images. Finally, we develop an early diagnosis module to compute probability scores of malignancy for lesion images over time. We collected 179 serial dermoscopic imaging data from 122 patients to verify our method. Extensive experiments show that the proposed model outperforms other commonly used sequence models. We also compared the diagnostic results of our model with those of seven experienced dermatologists and five registrars. Our model achieved higher diagnostic accuracy than clinicians (63.69% vs. 54.33%, respectively) and provided an earlier diagnosis of melanoma (60.7% vs. 32.7% of melanoma correctly diagnosed on the first follow-up images). These results demonstrate that our model can be used to identify melanocytic lesions that are at high-risk of malignant transformation earlier in the disease process and thereby redefine what is possible in the early detection of melanoma.

Background: Alzheimer’s Disease (AD) is the most common cause of dementia. Genetics, excessive exposure to environmental pollutants, as well as unhealthy lifestyle practices are often linked to the development of AD. No therapeutic approach has achieved complete success in treating AD; however, early detection and management with appropriate drugs are key to improving prognosis. Interventions: The pathogenesis of AD was extensively discussed in order to understand the reasons for the interventions suggested. The interventions reviewed include the use of different therapeutic agents and approaches, gene therapy, adherence to healthy dietary plans (Mediterranean diet, Okinawan diet and MIND diet), as well as the use of medicinal plants. The potential of nanotechnology as a multidisciplinary and interdisciplinary approach in the design of nano-formulations of AD drugs and the use of Superparamagnetic Iron Oxide Nanoparticles (SPIONs) as theranostic tools for early detection of Alzheimer’s disease were also discussed.

BackgroundCancer screening provides the opportunity to detect cancer early, ideally before symptom onset and metastasis, and offers an increased opportunity for a better prognosis. The ideal biomarkers for cancer screening should discriminate individuals who have not developed invasive cancer yet but are destined to do so from healthy subjects. However, most cancers lack effective screening recommendations. Therefore, further studies on novel screening strategies are urgently required.MethodsWe used a simple suboptimal inoculation melanoma mouse model to obtain ‘pre-diagnostic samples’ of mice with macroscopic melanomas. High-throughput sequencing and bioinformatic analysis were employed to identify differentially expressed RNAs in platelet signatures of mice injected with a suboptimal number of melanoma cells (eDEGs) compared with mice with macroscopic melanomas and negative controls. Moreover, 36 genes selected from the eDEGs via bioinformatics analysis were verified in a mouse validation cohort via quantitative real-time PCR. LASSO regression was utilized to generate the prediction models with gene expression signatures as the best predictors for occult tumor progression in mice.ResultsThese RNAs identified from eDEGs of mice injected with a suboptimal number of cancer cells were strongly enriched in pathways related to immune response and regulation. The prediction models generated by 36 gene qPCR verification data showed great diagnostic efficacy and predictive value in our murine validation cohort, and could discriminate mice with occult tumors from control group (area under curve (AUC) of 0.935 (training data) and 0.912 (testing data)) (gene signature includingCd19,Cdkn1a,S100a9,Tap1, andTnfrsf1b) and also from macroscopic tumor group (AUC of 0.920 (training data) and 0.936 (testing data)) (gene signature includingCcr7,Cd4,Kmt2d, andLy6e).ConclusionsOur proof-of-concept study provides evidence for potential clinical relevance of blood platelets as a platform for liquid biopsy-based early detection of cancer.

Background: The purpose of this study was to investigate the feasibility and effectiveness of an innovative telemedicine community-based intervention to increase detection of previously undiagnosed...

Objectives We propose a risk-tailored approach for management of lung cancer screening results. This approach incorporates individual risk factors and low-dose computed tomography (LDCT) image features into calculations of immediate and next-screen (1-y) risks of lung cancer detection, which in turn can recommend short-interval imaging or 1-year or 2-year screening intervals. Methods We first extended the "LCRAT+CT" individualized risk calculator to predict lung cancer risk after either a negative or abnormal LDCT screen result. To develop the abnormal screen portion, we analyzed 18,129 abnormal LDCT results in the National Lung Screening Trial (NLST), including lung cancers detected immediately (n = 649) or at the next screen (n = 235). We estimated the potential impact of this approach among NLST participants with any screen result (negative or abnormal). Results Applying the draft National Health Service (NHS) England protocol for lung screening to NLST participants referred 76% of participants to a 2-year interval, but delayed diagnosis for 40% of detectable cancers. The Lung Cancer Risk Assessment Tool+Computed Tomography (LCRAT+CT) risk model, with a threshold of less than 0.95% cumulative lung cancer risk, would also refer 76% of participants to a 2-year interval, but would delay diagnosis for only 30% of cancers, a 25% reduction versus the NHS protocol. Alternatively, LCRAT+CT, with a threshold of less than 1.7% cumulative lung cancer risk, would also delay diagnosis for 40% of cancers, but would refer 85% of participants for a 2-year interval, a 38% further reduction in the number of required 1-year screens beyond the NHS protocol. Conclusions Using individualized risk models to determine management in lung cancer screening could substantially reduce the number of screens or increase early detection.