SEMA3: A free smartphone platform for daily life surveys.

Traditionally, behavioral, social, and health science researchers have relied on global/retrospective survey methods administered cross-sectionally (i.e., on a single occasion) or longitudinally (i.e., on several occasions separated by weeks, months, or years). More recently, social and health scientists have added daily life survey methods (also known as intensive longitudinal methods or ambulatory assessment) to their toolkit. These methods (e.g., daily diaries, experience sampling, ecological momentary assessment) involve dense repeated assessments in everyday settings. To facilitate research using daily life survey methods, we present SEMA3 ( http://www.SEMA3.com ), a platform for designing and administering intensive longitudinal daily life surveys via Android and iOS smartphones. SEMA3 fills an important gap by providing researchers with a free, intuitive, and flexible platform with basic and advanced functionality. In this article, we describe SEMA3's development history and system architecture, provide an overview of how to design a study using SEMA3 and outline its key features, and discuss the platform's limitations and propose directions for future development of SEMA3.

Screening of malaria infections in human blood samples with varying parasite densities and anaemic conditions using AI-Powered mid-infrared spectroscopy.

BACKGROUND: Effective testing for malaria, including the detection of infections at very low densities, is vital for the successful elimination of the disease. Unfortunately, existing methods are either inexpensive but poorly sensitive or sensitive but costly. Recent studies have shown that mid-infrared spectroscopy coupled with machine learning (MIRs-ML) has potential for rapidly detecting malaria infections but requires further evaluation on diverse samples representative of natural infections in endemic areas. The aim of this study was, therefore, to demonstrate a simple AI-powered, reagent-free, and user-friendly approach that uses mid-infrared spectra from dried blood spots to accurately detect malaria infections across varying parasite densities and anaemic conditions. METHODS: Plasmodium falciparum strains NF54 and FCR3 were cultured and mixed with blood from 70 malaria-free individuals to create various malaria parasitaemia and anaemic conditions. Blood dilutions produced three haematocrit ratios (50%, 25%, 12.5%) and five parasitaemia levels (6%, 0.1%, 0.002%, 0.00003%, 0%). Dried blood spots were prepared on Whatman™ filter papers and scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) for machine-learning analysis. Three classifiers were trained on an 80%/20% split of 4655 spectra: (I) high contrast (6% parasitaemia vs. negative), (II) low contrast (0.00003% vs. negative) and (III) all concentrations (all positive levels vs. negative). The classifiers were validated with unseen datasets to detect malaria at various parasitaemia levels and anaemic conditions. Additionally, these classifiers were tested on samples from a population survey in malaria-endemic villages of southeastern Tanzania. RESULTS: The AI classifiers attained over 90% accuracy in detecting malaria infections as low as one parasite per microlitre of blood, a sensitivity unattainable by conventional RDTs and microscopy. These laboratory-developed classifiers seamlessly transitioned to field applicability, achieving over 80% accuracy in predicting natural P. falciparum infections in blood samples collected during the field survey. Crucially, the performance remained unaffected by various levels of anaemia, a common complication in malaria patients. CONCLUSION: These findings suggest that the AI-driven mid-infrared spectroscopy approach holds promise as a simplified, sensitive and cost-effective method for malaria screening, consistently performing well despite variations in parasite densities and anaemic conditions. The technique simply involves scanning dried blood spots with a desktop mid-infrared scanner and analysing the spectra using pre-trained AI classifiers, making it readily adaptable to field conditions in low-resource settings. In this study, the approach was successfully adapted to field use, effectively predicting natural malaria infections in blood samples from a population-level survey in Tanzania. With additional field trials and validation, this technique could significantly enhance malaria surveillance and contribute to accelerating malaria elimination efforts.

Bridging the gap from medical to psychological safety assessment: consensus study in a digital mental health context.

BACKGROUND: Digital Mental Health Interventions (DMHIs) that meet the definition of a medical device are regulated by the Medicines and Healthcare products Regulatory Agency (MHRA) in the UK. The MHRA uses procedures that were originally developed for pharmaceuticals to assess the safety of DMHIs. There is recognition that this may not be ideal, as is evident by an ongoing consultation for reform led by the MHRA and the National Institute for Health and Care Excellence. AIMS: The aim of this study was to generate an experts' consensus on how the medical regulatory method used for assessing safety could best be adapted for DMHIs. METHOD: An online Delphi study containing three rounds was conducted with an international panel of 20 experts with experience/knowledge in the field of UK digital mental health. RESULTS: Sixty-four items were generated, of which 41 achieved consensus (64%). Consensus emerged around ten recommendations, falling into five main themes: Enhancing the quality of adverse events data in DMHIs; Re-defining serious adverse events for DMHIs; Reassessing short-term symptom deterioration in psychological interventions as a therapeutic risk; Maximising the benefit of the Yellow Card Scheme; and Developing a harmonised approach for assessing the safety of psychological interventions in general. CONCLUSION: The implementation of the recommendations provided by this consensus could improve the assessment of safety of DMHIs, making them more effective in detecting and mitigating risk.

How mutation accumulation depends on the structure of the cell lineage tree.

All the cells of a multicellular organism are the product of cell divisions that trace out a single binary tree, the so-called cell lineage tree. Because cell divisions are accompanied by replication errors, the shape of the cell lineage tree is a key determinant of how somatic evolution, which can potentially lead to cancer, proceeds. Carcinogenesis requires the accumulation of a certain number of driver mutations. By mapping the accumulation of mutations into a graph theoretical problem, we present an exact numerical method to calculate the probability of collecting a given number of mutations and show that for low mutation rates it can be approximated with a simple analytical formula, which depends only on the distribution of the lineage lengths, and is dominated by the longest lineages. Our results are crucial in understanding how natural selection can shape the cell lineage trees of multicellular organisms and curtail somatic evolution.

Reagent-free detection of Plasmodium falciparum malaria infections in field-collected mosquitoes using mid-infrared spectroscopy and machine learning.

Field-derived metrics are critical for effective control of malaria, particularly in sub-Saharan Africa where the disease kills over half a million people yearly. One key metric is entomological inoculation rate, a direct measure of transmission intensities, computed as a product of human biting rates and prevalence of Plasmodium sporozoites in mosquitoes. Unfortunately, current methods for identifying infectious mosquitoes are laborious, time-consuming, and may require expensive reagents that are not always readily available. Here, we demonstrate the first field-application of mid-infrared spectroscopy and machine learning (MIRS-ML) to swiftly and accurately detect Plasmodium falciparum sporozoites in wild-caught Anopheles funestus, a major Afro-tropical malaria vector, without requiring any laboratory reagents. We collected 7178 female An. funestus from rural Tanzanian households using CDC-light traps, then desiccated and scanned their heads and thoraces using an FT-IR spectrometer. The sporozoite infections were confirmed using enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR), to establish references for training supervised algorithms. The XGBoost model was used to detect sporozoite-infectious specimen, accurately predicting ELISA and PCR outcomes with 92% and 93% accuracies respectively. These findings suggest that MIRS-ML can rapidly detect P. falciparum in field-collected mosquitoes, with potential for enhancing surveillance in malaria-endemic regions. The technique is both fast, scanning 60-100 mosquitoes per hour, and cost-efficient, requiring no biochemical reactions and therefore no reagents. Given its previously proven capability in monitoring key entomological indicators like mosquito age, human blood index, and identities of vector species, we conclude that MIRS-ML could constitute a low-cost multi-functional toolkit for monitoring malaria risk and evaluating interventions.

Development and validation of a predictive model for choledocholithiasis.

BACKGROUND: Gallstone-related conditions affect a significant portion of the population, with varying prevalence among different ethnic groups. Complications such as pancreatitis and cholangitis are associated with the presence of common bile duct (CBD) stones. Existing guidelines for diagnosing choledocholithiasis lack precision, leading to excessive use of invasive procedures like endoscopic retrograde cholangiopancreatography (ERCP). METHODS: A prospective study was conducted at Hospital Central "Dr. Ignacio Morones Prieto," involving 374 patients in the development cohort and 154 patients in the validation cohort. Patients meeting inclusion criteria underwent biochemical testing and ultrasonography. A predictive scoring system was developed using logistic regression and validated in an independent cohort. Clinical and laboratory variables were collected, and model performance was assessed using receiver-operator characteristic (ROC) curves. RESULTS: The predictive model incorporated variables such as age, pancreatitis, cholangitis, bilirubin levels, and CBD stone presence on ultrasound. The model demonstrated an area under the ROC curve (AUC) of 93.81% in the validation dataset. By adjusting the threshold defining high-risk probability to 40%, the model improved specificity and sensitivity compared to existing guidelines. Notably, the model reclassified patients, leading to a more accurate risk assessment. CONCLUSIONS: The developed algorithm accurately predicts choledocholithiasis non-invasively in patients with symptomatic gallstones. This tool has the potential to reduce reliance on costly or invasive procedures like magnetic resonance cholangiopancreatography and ERCP, offering a more efficient and cost-effective approach to patient management. The user-friendly calculator developed in this study could streamline diagnostic procedures, particularly in resource-limited healthcare settings, ultimately improving patient care.

Socioeconomic deprivation, health and healthcare utilisation among millennials.

This study estimates and decomposes components of different measures of inequality in health and healthcare use among millennial adolescents, a sizeable cohort of individuals at a critical stage of life. Administrative data from the UK Hospital Episode Statistics are linked to Next Steps, a survey collecting information about millennials born between 1989 and 1990, providing a uniquely comprehensive source of health and socioeconomic variables. Socioeconomic inequalities in psychological distress, long-term illness and the use of emergency and outpatient hospital care are measured using a corrected concentration index. Shapley-Shorrocks decomposition techniques are employed to measure the relative contributions of childhood socioeconomic circumstances to adolescents' health and healthcare inequality of opportunity. Results show that income-related deprivation contributes to significant inequalities in mental and physical health among adolescents aged between 15 and 17 years old. There are also pro-rich inequalities in the use of specific outpatient hospital services (e.g., orthodontic and mental healthcare), while pro-poor disparities are found in the use of emergency care services. Regional and parental circumstances are leading factors in influencing inequality of opportunity in the use of hospital care among adolescents. These findings shed light on the main drivers of health inequalities during an important stage of human development and have potentially important implications on human capital formation across the life-cycle.

A Novel Blended Transdiagnostic Intervention (eOrygen) for Youth Psychosis and Borderline Personality Disorder: Uncontrolled Single-Group Pilot Study.

BACKGROUND: Integrating innovative digital mental health interventions within specialist services is a promising strategy to address the shortcomings of both face-to-face and web-based mental health services. However, despite young people's preferences and calls for integration of these services, current mental health services rarely offer blended models of care. OBJECTIVE: This pilot study tested an integrated digital and face-to-face transdiagnostic intervention (eOrygen) as a blended model of care for youth psychosis and borderline personality disorder. The primary aim was to evaluate the feasibility, acceptability, and safety of eOrygen. The secondary aim was to assess pre-post changes in key clinical and psychosocial outcomes. An exploratory aim was to explore the barriers and facilitators identified by young people and clinicians in implementing a blended model of care into practice. METHODS: A total of 33 young people (aged 15-25 years) and 18 clinicians were recruited over 4 months from two youth mental health services in Melbourne, Victoria, Australia: (1) the Early Psychosis Prevention and Intervention Centre, an early intervention service for first-episode psychosis; and (2) the Helping Young People Early Clinic, an early intervention service for borderline personality disorder. The feasibility, acceptability, and safety of eOrygen were evaluated via an uncontrolled single-group study. Repeated measures 2-tailed t tests assessed changes in clinical and psychosocial outcomes between before and after the intervention (3 months). Eight semistructured qualitative interviews were conducted with the young people, and 3 focus groups, attended by 15 (83%) of the 18 clinicians, were conducted after the intervention. RESULTS: eOrygen was found to be feasible, acceptable, and safe. Feasibility was established owing to a low refusal rate of 25% (15/59) and by exceeding our goal of young people recruited to the study per clinician. Acceptability was established because 93% (22/24) of the young people reported that they would recommend eOrygen to others, and safety was established because no adverse events or unlawful entries were recorded and there were no worsening of clinical and social outcome measures. Interviews with the young people identified facilitators to engagement such as peer support and personalized therapy content, as well as barriers such as low motivation, social anxiety, and privacy concerns. The clinician focus groups identified evidence-based content as an implementation facilitator, whereas a lack of familiarity with the platform was identified as a barrier owing to clinicians' competing priorities, such as concerns related to risk and handling acute presentations, as well as the challenge of being understaffed. CONCLUSIONS: eOrygen as a blended transdiagnostic intervention has the potential to increase therapeutic continuity, engagement, alliance, and intensity. Future research will need to establish the effectiveness of blended models of care for young people with complex mental health conditions and determine how to optimize the implementation of such models into specialized services.

Bridges Between Spiking Neural Membrane Systems and Virus Machines.

Spiking Neural P Systems (SNP) are well-established computing models that take inspiration from spikes between biological neurons; these models have been widely used for both theoretical studies and practical applications. Virus machines (VMs) are an emerging computing paradigm inspired by viral transmission and replication. In this work, a novel extension of VMs inspired by SNPs is presented, called Virus Machines with Host Excitation (VMHEs). In addition, the universality and explicit results between SNPs and VMHEs are compared in both generating and computing mode. The VMHEs defined in this work are shown to be more efficient than SNPs, requiring fewer memory units (hosts in VMHEs and neurons in SNPs) in several tasks, such as a universal machine, which was constructed with 18 hosts less than the 84 neurons in SNPs, and less than other spiking models discussed in the work.

Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus.

BACKGROUND: Accurately determining the age and survival probabilities of adult mosquitoes is crucial for understanding parasite transmission, evaluating the effectiveness of control interventions and assessing disease risk in communities. This study was aimed at demonstrating the rapid identification of epidemiologically relevant age categories of Anopheles funestus, a major Afro-tropical malaria vector, through the innovative combination of infrared spectroscopy and machine learning, instead of the cumbersome practice of dissecting mosquito ovaries to estimate age based on parity status. METHODS: Anopheles funestus larvae were collected in rural south-eastern Tanzania and reared in an insectary. Emerging adult females were sorted by age (1-16 days old) and preserved using silica gel. Polymerase chain reaction (PCR) confirmation was conducted using DNA extracted from mosquito legs to verify the presence of An. funestus and to eliminate undesired mosquitoes. Mid-infrared spectra were obtained by scanning the heads and thoraces of the mosquitoes using an attenuated total reflection-Fourier transform infrared (ATR-FT-IR) spectrometer. The spectra (N = 2084) were divided into two epidemiologically relevant age groups: 1-9 days (young, non-infectious) and 10-16 days (old, potentially infectious). The dimensionality of the spectra was reduced using principal component analysis, and then a set of machine learning and multi-layer perceptron (MLP) models were trained using the spectra to predict the mosquito age categories. RESULTS: The best-performing model, XGBoost, achieved overall accuracy of 87%, with classification accuracy of 89% for young and 84% for old An. funestus. When the most important spectral features influencing the model performance were selected to train a new model, the overall accuracy increased slightly to 89%. The MLP model, utilizing the significant spectral features, achieved higher classification accuracy of 95% and 94% for the young and old An. funestus, respectively. After dimensionality reduction, the MLP achieved 93% accuracy for both age categories. CONCLUSIONS: This study shows how machine learning can quickly classify epidemiologically relevant age groups of An. funestus based on their mid-infrared spectra. Having been previously applied to An. gambiae, An. arabiensis and An. coluzzii, this demonstration on An. funestus underscores the potential of this low-cost, reagent-free technique for widespread use on all the major Afro-tropical malaria vectors. Future research should demonstrate how such machine-derived age classifications in field-collected mosquitoes correlate with malaria in human populations.

Rapid assessment of the blood-feeding histories of wild-caught malaria mosquitoes using mid-infrared spectroscopy and machine learning.

BACKGROUND: The degree to which Anopheles mosquitoes prefer biting humans over other vertebrate hosts, i.e. the human blood index (HBI), is a crucial parameter for assessing malaria transmission risk. However, existing techniques for identifying mosquito blood meals are demanding in terms of time and effort, involve costly reagents, and are prone to inaccuracies due to factors such as cross-reactivity with other antigens or partially digested blood meals in the mosquito gut. This study demonstrates the first field application of mid-infrared spectroscopy and machine learning (MIRS-ML), to rapidly assess the blood-feeding histories of malaria vectors, with direct comparison to PCR assays. METHODS AND RESULTS: Female Anopheles funestus mosquitoes (N = 1854) were collected from rural Tanzania and desiccated then scanned with an attenuated total reflectance Fourier-transform Infrared (ATR-FTIR) spectrometer. Blood meals were confirmed by PCR, establishing the 'ground truth' for machine learning algorithms. Logistic regression and multi-layer perceptron classifiers were employed to identify blood meal sources, achieving accuracies of 88%-90%, respectively, as well as HBI estimates aligning well with the PCR-based standard HBI. CONCLUSIONS: This research provides evidence of MIRS-ML effectiveness in classifying blood meals in wild Anopheles funestus, as a potential complementary surveillance tool in settings where conventional molecular techniques are impractical. The cost-effectiveness, simplicity, and scalability of MIRS-ML, along with its generalizability, outweigh minor gaps in HBI estimation. Since this approach has already been demonstrated for measuring other entomological and parasitological indicators of malaria, the validation in this study broadens its range of use cases, positioning it as an integrated system for estimating pathogen transmission risk and evaluating the impact of interventions.

Corrigendum: Usability, feasibility, and effect of a biocueing intervention in addition to a moderated digital social therapy-platform in young people with emerging mental health problems: a mixed-method approach.

[This corrects the article DOI: 10.3389/fpsyt.2022.871813.].

The Cost-Effectiveness of a Novel Online Social Therapy to Maintain Treatment Effects From First-Episode Psychosis Services: Results From the Horyzons Randomized Controlled Trial.

BACKGROUND: Digital interventions have potential applications in promoting long-term recovery and improving outcomes in first-episode psychosis (FEP). This study aimed to evaluate the cost-effectiveness of Horyzons, a novel online social therapy to support young people aged 16-27 years following discharge from FEP services, compared with treatment as usual (TAU) from a healthcare sector and a societal perspective. STUDY DESIGN: A cost-effectiveness analysis (CEA), based on the change in social functioning, and a cost-utility analysis (CUA) using quality-adjusted life years were undertaken alongside a randomized controlled trial. Intervention costs were determined from study records; resources used by patients were collected from a resource-use questionnaire and administrative data. Mean costs and outcomes were compared at 18 months and incremental cost-effectiveness ratios were calculated. Uncertainty analysis using bootstrapping and sensitivity analyses was conducted. STUDY RESULTS: The sample included 170 participants: Horyzons intervention group (n = 86) and TAU (n = 84). Total costs were significantly lower in the Horyzons group compared with TAU from both the healthcare sector (-AU$4789.59; P < .001) and the societal perspective (-AU$5131.14; P < .001). In the CEA, Horyzons was dominant, meaning it was less costly and resulted in better social functioning. In the CUA, the Horyzons intervention resulted in fewer costs but also yielded fewer QALYs. However, group differences in outcomes were not statistically significant. When young people engaged more with the platform, costs were shown to decrease and outcomes improved. CONCLUSIONS: The Horyzons intervention offers a cost-effective approach for improving social functioning in young people with FEP after discharge from early intervention services.

Measures of Social and Occupational Function in Early Psychosis: A Systematic Review and Meta-analysis.

Deficits in social and occupational function are widely reported in psychosis, yet no one measure of function is currently agreed upon as a gold standard in psychosis research. The aim of this study was to carry out a systematic review and meta-analysis of functioning measures to determine what measures were associated with largest effect sizes when measuring between-group differences, changes over time, or response to treatment. Literature searches were conducted based on PsycINFO and PubMed to identify studies for inclusion. Cross-sectional and longitudinal observational and intervention studies of early psychosis (≤5 years since diagnosis) that included social and occupational functioning as an outcome measure were considered. A series of meta-analyses were conducted to determine effect size differences for between-group differences, changes over time, or response to treatment. Subgroup analyses and meta-regression were carried out to account for variability in study and participant characteristics. One hundred and sixteen studies were included, 46 studies provided data (N = 13 261) relevant to our meta-analysis. Smallest effect sizes for changes in function over time and in response to treatment were observed for global measures, while more specific measures of social and occupational function showed the largest effect sizes. Differences in effect sizes between functioning measures remained significant after variability in study and participant characteristics were accounted for. Findings suggest that more specific measures of social function are better able to detect changes in function over time and in response to treatment.

Nonlinear Spiking Neural Systems With Autapses for Predicting Chaotic Time Series.

Spiking neural P (SNP) systems are a class of distributed and parallel neural-like computing models that are inspired by the mechanism of spiking neurons and are 3rd-generation neural networks. Chaotic time series forecasting is one of the most challenging problems for machine learning models. To address this challenge, we first propose a nonlinear version of SNP systems, called nonlinear SNP systems with autapses (NSNP-AU systems). In addition to the nonlinear consumption and generation of spikes, the NSNP-AU systems have three nonlinear gate functions, which are related to the states and outputs of the neurons. Inspired by the spiking mechanisms of NSNP-AU systems, we develop a recurrent-type prediction model for chaotic time series, called the NSNP-AU model. As a new variant of recurrent neural networks (RNNs), the NSNP-AU model is implemented in a popular deep learning framework. Four datasets of chaotic time series are investigated using the proposed NSNP-AU model, five state-of-the-art models, and 28 baseline prediction models. The experimental results demonstrate the advantage of the proposed NSNP-AU model for chaotic time series forecasting.

Reservoir computing models based on spiking neural P systems for time series classification.

Nonlinear spiking neural P (NSNP) systems are neural-like membrane computing models with nonlinear spiking mechanisms. Because of this nonlinear spiking mechanism, NSNP systems can show rich nonlinear dynamics. Reservoir computing (RC) is a novel recurrent neural network (RNN) and can overcome some shortcomings of traditional RNNs. Based on NSNP systems, we developed two RC variants for time series classification, RC-SNP and RC-RMS-SNP, which are without and integrated with reservoir model space (RMS), respectively. The two RC variants use NSNP systems as the reservoirs and can be easily implemented in the RC framework. The proposed two RC variants were evaluated on 17 benchmark time series classification datasets and compared with 16 state-of-the-art or baseline classification models. The comparison results demonstrate the effectiveness of the proposed two RC variants for time series classification tasks.

Lifting Hofmeister's Curse: Impact of Cations on Diffusion, Hydrogen Bonding, and Clustering of Water.

Water plays a role in the stability, reactivity, and dynamics of the solutes that it contains. The presence of ions alters this capacity by changing the dynamics and structure of water. However, our understanding of how and to what extent this occurs is still incomplete. Here, a study of the low-frequency Raman spectra of aqueous solutions of various cations by using optical Kerr-effect spectroscopy is presented. This technique allows for the measurement of the changes that ions cause in both the diffusive dynamics and the vibrations of the hydrogen-bond structure of water. It is found that when salts are added, some of the water molecules become part of the ion solvation layers, while the rest retain the same diffusional properties as those of pure water. The slowing of the dynamics of the water molecules in the solvation shell of each ion was found to depend on its charge density at infinite dilution conditions and on its position in the Hofmeister series at higher concentrations. It is also observed that all cations weaken the hydrogen-bond structure of the solution and that this weakening depends only on the size of the cation. Finally, evidence is found that ions tend to form amorphous aggregates, even at very dilute concentrations. This work provides a novel approach to water dynamics that can be used to better study the mechanisms of solute nucleation and crystallization, the structural stability of biomolecules, and the dynamic properties of complex solutions, such as water-in-salt electrolytes.

A Personalized, Transdiagnostic Smartphone Intervention (Mello) Targeting Repetitive Negative Thinking in Young People With Depression and Anxiety: Pilot Randomized Controlled Trial.

BACKGROUND: Repetitive negative thinking (RNT) is a key transdiagnostic mechanism underpinning depression and anxiety. Using "just-in-time adaptive interventions" via smartphones may disrupt RNT in real time, providing targeted and personalized intervention. OBJECTIVE: This pilot randomized controlled trial evaluates the feasibility, acceptability, and preliminary clinical outcomes and mechanisms of Mello-a fully automated, personalized, transdiagnostic, and mechanistic smartphone intervention targeting RNT in young people with depression and anxiety. METHODS: Participants with heightened depression, anxiety, and RNT were recruited via social media and randomized to receive Mello or a nonactive control over a 6-week intervention period. Assessments were completed via Zoom sessions at baseline and at 3 and 6 weeks after baseline. RESULTS: The findings supported feasibility and acceptability, with high rates of recruitment (N=55), uptake (55/64, 86% of eligible participants), and retention (52/55, 95% at 6 weeks). Engagement was high, with 90% (26/29) and 59% (17/29) of the participants in the Mello condition still using the app during the third and sixth weeks, respectively. Greater reductions in depression (Cohen d=0.50), anxiety (Cohen d=0.61), and RNT (Cohen d=0.87) were observed for Mello users versus controls. Mediation analyses suggested that changes in depression and anxiety were accounted for by changes in RNT. CONCLUSIONS: The results indicate that mechanistic, targeted, and real-time technology-based solutions may provide scalable and effective interventions that advance the treatment of youth mental ill health. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12621001701819; http://tinyurl.com/4d3jfj9f.

The Effects of a Digital, Transdiagnostic, Clinically and Peer-Moderated Treatment Platform for Young People With Emerging Mental Health Complaints: Repeated Measures Within-Subjects Study.

BACKGROUND: To address the growing prevalence of youth mental health problems, early intervention is crucial to minimize individual, societal, and economic impacts. Indicative prevention aims to target emerging mental health complaints before the onset of a full-blown disorder. When intervening at this early stage, individuals are more responsive to treatment, resulting in cost-effective outcomes. The Moderated Online Social Therapy platform, which was successfully implemented and proven effective in Australia, is a digital, peer- and clinically moderated treatment platform designed for young people. The Netherlands was the first country outside Australia to implement this platform, under the name Engage Young People Early (ENYOY). It has the potential to reduce the likelihood of young people developing serious mental health disorders. OBJECTIVE: This study aims to investigate the effects on young people using the ENYOY-platform in relation to psychological distress, psychosocial functioning, and positive health parameters. METHODS: Dutch-speaking young people with emerging mental health complaints (N=131) participated in the ENYOY-platform for 6 months in a repeated measures within-subjects study. Psychological distress, psychosocial functioning, and positive health parameters were assessed at baseline and 3, 6, and 12 months. Repeated measures ANOVA was conducted and adjusted for age, sex, therapy, and community activity. The Reliable Change Index and Clinically Significant Index were computed to compare the baseline with the 6- and 12-month measurements. The missing data rate was 22.54% and the dropout rate 62.6% (82/131). RESULTS: The primary analysis (77/131, 58.8%) showed that psychological distress decreased and psychosocial functioning improved over time with large effect sizes (P<.001 in both cases; ηp2=0.239 and 0.318, respectively) independent of age (P=.76 for psychological distress and P=.48 for psychosocial functioning), sex (P=.24 and P=.88, respectively), therapy activity (P=.49 and P=.80, respectively), or community activity (P=.59 and P=.48, respectively). Similarly, secondary analyses (51/131, 38.9%) showed significant effects of time on the quality of life, well-being, and meaningfulness positive health parameters (P<.05; ηp2=0.062, 0.140, and 0.121, respectively). Improvements in all outcome measures were found between baseline and 3 and 6 months (P≤.001-.01; d=0.23-0.62) and sustained at follow-up (P=.18-.97; d=0.01-0.16). The Reliable Change Index indicated psychological distress improvements in 38% (39/102) of cases, no change in 54.9% (56/102) of cases, and worsening in 5.9% (6/102) of cases. Regarding psychosocial functioning, the percentages were 50% (51/102), 43.1% (44/102), and 6.9% (7/102), respectively. The Clinically Significant Index demonstrated clinically significant changes in 75.5% (77/102) of cases for distress and 89.2% (91/102) for functioning. CONCLUSIONS: This trial demonstrated that the ENYOY-platform holds promise as a transdiagnostic intervention for addressing emerging mental health complaints among young people in the Netherlands and laid the groundwork for further clinical research. It would be of great relevance to expand the population on and service delivery of the platform. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12888-021-03315-x.

Attacking cryptosystems by means of virus machines.

The security that resides in the public-key cryptosystems relies on the presumed computational hardness of mathematical problems behind the systems themselves (e.g. the semiprime factorization problem in the RSA cryptosystem), that is because there is not known any polynomial time (classical) algorithm to solve them. The paper focuses on the computing paradigm of virus machines within the area of Unconventional Computing and Natural Computing. Virus machines, which incorporate concepts of virology and computer science, are considered as number computing devices with the environment. The paper designs a virus machine that solves a generalization of the semiprime factorization problem and verifies it formally.