Sexual development is a complex mechanism activated by the hypothalamic-pituitary-gonadal axis. Over the last one hundred years there has been a decline in the age at puberty onset in industrialised countries. Some Italian studies showed an increase in diagnoses of Central Precocious Puberty (CPP) during the COVID-19 pandemic. It is thus supposed that in this period there was an increased impact of factors that can influence pubertal development. Our retrospective monocentric study aimed to confirm the existence of this phenomenon and analysed possible related factors. We retrospectively evaluated clinical, laboratory, radiological and ultrasound (US) data of 154 girls referred to our Tertiary Centre of Paediatric Endocrinology from January 2019 to April 2021 for different forms of Precocious Puberty. We subdivided the cases into subgroups according to the final diagnosis: CPP, Early Puberty (EP), isolated thelarche and isolated pubarche. The observation period was subdivided into: Period 1, before lockdown (1 January 2019 – 8 March 2020) and Period 2, lockdown and the following months (9 March 2020 – 30 April 2021). Period 2 was further divided into “restrictive lockdown period” (Period 2.1)  and “less restrictive lockdown period” (Period 2.2) (15 June 2020 – 30 April 2021). We analysed data regarding the use of electronic devices before and during lockdown in a group of girls with CPP diagnosed in Period 2 and we compared the data with that of a control group. Our data show an increase in the number of new diagnoses of CPP during lockdown and in the following months, compared with the previous period. We also detected a higher use of PCs and smartphones in girls with CPP diagnosed in Period 2, compared with the control group. The percentage of the presence of endometrial rhyme detected during the pelvic ultrasound was higher in girls with CPP in Period 2, compared with the previous period. Based on our data we assume there was an environmental effect on pubertal timing that calls our attention to factors such as food, use of electronic devices and stress. We will need further studies to better understand this data. The complex mechanism that leads to sexual development starts from the activation of the hypothalamic-pituitary-gonadal axis by an unknown primary input. Over the last one hundred years there has been a decrease in age at puberty onset in industrialised countries; menarche appears on average in girls about 12 and a half years old, and thelarche in girls between 9 and a half and ten years old  but alsoIn Italy, the restrictions due to the coronavirus pandemic led to deep changes in people’s habits. Italians had to stay at home for several months. The schools closure and the beginning of distance learning led to an increase in the use of electronic devices. These devices were also used more frequently for extracurricular reasons  that resulted in an accumulation of hours spent in front of screens.Forced home - staying at home also led a reduction in physical activities and an increase in consumption of junk food resulting in body weight gain.In addition, the pandemic situation led to a growth in stressors for many families .Some previous Italian studies reported an increase in the number of new cases of Central Precocious Puberty (CPP) in girls during the COVID-19 pandemic –11 It haThis retrospective monocentric study aimed to confirm the existence of this phenomenon and analysed possible related factors.We retrospectively evaluated clinical, laboratory, radiological and ultrasound (US) data on 154 girls referred to our Tertiary Centre of Paediatric Endocrinology from January 2019 to April 2021 for different forms of Precocious Puberty. We subdivided the cases into subgroups according to the final diagnosis: CPP, Early Puberty (EP), isolated thelarche and isolated pubarche. CPP was defined according to Consensus Guidelines . Early pThe observation period was subdivided into: Period 1, before lockdown (1 January 2019 – 8 March 2020), and Period 2, lockdown and the following months (9 March 2020 – 30 April 2021). Period 2 was further divided into “restrictive lockdown period” (Period 2.1)  and “less restrictive lockdown period” (Period 2.2) (15 June 2020 – 30 April 2021).We analysed data regarding the use of electronic devices obtained through a questionnaire Figure 1We excluded girls with CPP associated with hypothalamic–pituitary congenital malformations, neurological, neurosurgical and/or genetic diseases, psychomotor delay, oncological diseases. We also excluded adopted girls with CPP.Age, sex, ethnicity, family history of precocious and EP, mid-parental height, age at the onset of pubertal signs, and age at first observation were recovered from clinical records. Height, weight, birth weight and BMI were expressed as a standard deviation score (SDS) according to Italian standards . PubertaWhen available, we recorded levels at baseline of follicle-stimulating hormone [FSH], luteinizing hormone [LH], estradiol [E2]. A Gonadotropin-releasing hormone (GnRH) test was performed by i.v. administration of GnRH to 94 girls (37 in Period 1 and 57 in Period 2) with LH and FSH measurement at 0, + 30 and + 60 min.A basal level of LH > 0,3 IU/L and/or a peak response of LH after GnRH infusion > 5 IU/L, with or without serum estradiol levels > pg/ml, were considered suggestive of CPP –18.We estimated bone age (BA) from an X-ray of the left hand and wrist using the Greulich & Pyle atlas . Bone agPelvic ultrasound was trans-abdominally performed by a group of paediatric evaluation expert radiologists. It was used a Convex ecotomograph with B-mode ultrasound signal and a variable wavelength between 3.5 and 5.0 MHz (Philips). The subject was examined in a supine position with distended bladder and we’re detected uterine measurements  and ovarian measurements. It was also evaluated the visibility of the endometrial rhyme. The uterine volumes were obtained through the ellipsoid formula [(DL x DT x ADP x 0.52)/1000]. A uterine longitudinal diameters (ULD) > 34 mm and/or a uterine volume (UV) > 4 ml and/or the presence of endometrial rhyme were considered suggestive of estrogenic stimulation and advanced pubertal development .We performed a descriptive analysis of central trend measures (mean and median), with range maximum and minimum value and standard deviation of continuous variables .We analysed Tanner stage with frequency tables. We analysed data with Chi squared tests.We realised frequency tables for distribution analysis of descriptive variables .We used Student’s t-distribution tests to evaluate differences between means of the following variables, in the different groups defined on the basis of diagnosis: SDS weight, SDS height, SDS BMI, birth weight, rate of progression of pubertal development, age at diagnosis, basal and peak value of LH and FSH, bone age, uterine volume, longitudinal diameter uterus.We used non-parametric tests (Mann–Whitney) for data with non-normal distribution and two proportion zeta tests and chi squared tests for frequency analysis.All statistical tests were two-tailed and P < 0.05 was considered statistically significant.Statistical analyses were performed with the use of NCSS 2021  for statistical analyses.We found 3 girls with CPP and 14 girls with EP in sub-Period 2.1 and 14 girls with CPP and 34 girls with EP in sub-Period 2.2. The comparative analysis of sub-Period 2.1 and 2.2 did not show any differences in auxological, laboratory and radiologic data except for BMI SDS that showed a significant increase in the period after the restrictive lockdown in the EP group . We did not find any significance in the group with CPP, probably due to the sample’s reduced size (sub-Period 1 0.07 ± 0.79 vs sub-Period 2 0.79 ± 0.53).In regard to the results of the questionnaire on the use of electronic devices in girls with CPP, the percentage of girls with CPP that used PCs and tablets more than 2 hours a day was significantly higher during the lockdown period, compared with the preceding period . In addition, the percentage of girls that used PCs and smartphones more than 2 hours a day during lockdown was significantly higher in girls with CPP compared with the control group .CPP is a rare disease that involves mainly females and in most cases it is idiopathic.The results of our study, in line with other Italian studies recently published –11, showMoreover, in accordance with the results of Chioma et al , this inDuring the SARS-CoV-2 pandemic, people’s habits changed radically; we think that an environmental change in lifestyle may influence pubertal timing.We assumed that obesity and the increase of BMI in the paediatric population are possible contributing factors of the pubertal onset advance seen in the last few decades in industrialized countries. The increase in fat mass may lead to an augmented production of adipocyte hormones like leptin, which has a permissive role in HPG axis activation  22), an, an22), In our experience, we found no significant increase in SDS BMI in girls with CPP diagnosed in Period 2, but only a trend of increasing BMI in months after lockdown in girls with EP. Indeed only in Stagi’s study  was an iIn regard to a greater pubertal thrust that characterized the diagnosis of forms of CPP detected during lockdown, as compared with forms identified in the previous period, there is contrasting literature data.The studies of Stagi  and UmanIn Stagi’s  study thOn the other hand, as concerns US data, our study is the only one in literature that shows a higher percentage of girls with the presence of endometrial rhyme during lockdown, compared with previous period.It is well known that the presence of endometrial rhyme is a really specific parameter of estrogenic secretion and significant pubertal activation . It coulWe must however considerer that controversial results in the literature could depend on the difficulty of comparing studies having different numbers of patients, inclusion criteria and observation period.via genetic transcription  , 9. StreWe performed all the evaluations in the same Centre with homogeneous criteria of diagnosis and evaluation.We also analysed the frequency of other forms of precocious puberty without central involvement, in contrast to other published studies.We used a control group for the examination of the questionnaires regarding the use of electronic devices.The limitations of our study are largely related to the emergency situation due to the pandemic, which did not allow prospective, controlled studies.The sample small size and restrospective nature are also in the limitations of our study.The data are preliminary and other evaluations need to be carried out through multicentre analyses with wider case studies.The SARS-CoV-2 pandemic led to radical habit changes and we assume that these changes influenced the increase in frequency of CPP. Based on our data we assume there was an environmental effect on pubertal timing that calls our attention to factors such as food, use of electronic devices and stress. We will need other studies to better understand this data. By improving our knowledge of these factors, we could better understand some of the physiopathological aspects of puberty and implement preventive and corrective actions.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by UOC Ricerca e Innovazione - Scudeller Startup/Istruttoria  IRCCS Azienda Ospedaliero-Universitaria di Bologna Policlinico S.Orsola-Malpighi. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Protein–ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures:  With increases in computing power, code scalability, and advancement of theoretical methods, physics-based computational tools such as molecular dynamics and molecular/quantum mechanics can be used for the reliable representation of PLI and prediction of accurate binding free energies4. However, these methods are computationally expensive and are limited to a number of protein–ligand complexes5. This limits their routine use in high-throughput virtual screening4 for the discovery of novel hit candidates and lead optimization6 for a given protein target7. Molecular docking has been used to predict binding affinity and estimate interactions with reasonable computational cost15; however, its accuracy is relatively low as it uses heuristic rules to evaluate the scoring function.Accurate prediction of protein–ligand interactions (PLI) is a critical step in therapeutic design and discovery. These interactions influence various molecular-level properties, such as substrate binding, product release, regio-selectivity, target protein function, and ability to facilitate potential hit identification, which is the first step in finding novel candidates for drug discovery16. There has been significant effort to develop deep learning models that predict PLI18, and other biophysical properties20 that are critical for therapeutic design but cannot be predicted through physics based modeling21. The greater understanding of PLI enabled by deep learning can help in the estimation of properties such as activity, potency and binding affinity22. However, several technical challenges limit the use of deep learning for modeling protein–ligand complexes and accurate prediction of properties. The first challenge relates to the limited availability of protein–ligand 3D data and the second challenge focuses on the appropriate representation of the data (domain knowledge), specifically in terms of the comprehensive 3D geometry representation. Structure-based methods have the advantage of producing results that can be interpreted but are limited by the number of available samples21. Circular fingerprints, generated by encoding localized structural and geometric information, have long been a cornerstone of cheminformatics23. The flexibility of fingerprints has created new avenues for molecular computational research, including the increased implementation of graph-based representations to include domain awareness25. Molecular graph representations provide a way to model and simulate the 3D chemical space while retaining a wider range of structural information. In that context, Ragoza et al. implemented a deep convolutional neural network (CNN) that operates directly on 3D molecular graph input, similar to the AtomNet model previously implemented by Walloch et al.27. Other approaches such as Graph-CNN developed by Torng et al. use unsupervised autoencoders to leverage sequence-based data that is more abundant but also costly in terms of structural accuracy28.The use of deep learning has revolutionized the healthcare system in recent years29. That approach has been generalized and improved through the formulation of graph attention (GAT) networks30. GAT architectures operate on the importance of a given node, leading to improved computational efficiency and accuracy31. Lim et al. showed the implementation of such an architecture to capture PLI, which provides a baseline for the development of more robust models32. Chen et al. proposed a bidirectional attention-driven, end-to-end GNN to predict PLI and enable biochemical insights through attention weight visualization1. Predicting the activity of a protein–ligand complex is a binary classification problem. Reshaping the problem to focus on affinity creates a regression problem of heightened complexity. The existing deep learning models that predict the binding affinity or other biophysical and biochemical properties such as 222133. However, most of the methods use sequence-based data for proteins and SMILES representations for interacting ligands. For example, DeepDTA22 and DeepAffinity21 use SMILES strings of the ligands and amino-acid sequences of the target proteins to predict the affinity. MONN33 is a multi-objective sequence-based neural network model that first predicts the non-covalent interaction between the ligand and the residues of the interacting target and then the binding affinities in terms of Graph-based representations extend the learning of chemical data to graph neural networks (GNNs). Monti et al. designed a mixture model network (MoNet) that enables non-Euclidean data, such as graphs, to be learned by CNNsIn this contribution, we formulated two GNNs based on the GAT architecture by incorporating domain-specific featurization of the protein and ligand atoms ; we consolidated these features into a single atom-level feature, hybridization, which describes the bonding properties of an atom.The distinct featurization of our GNNs reduces the feature size of the protein, which can contain a large number of atoms, and enhances the feature size of the ligand, which is typically a small molecule. This shift reduces redundancy in the protein representation and focuses computational resources on the improved atomic representation of the ligand. While the Graph-CNN developed by Torng et al. also involved a large number of ligand features, more than half were at the bond level34 and PDBbind36, which are described in further detail below. The datasets consist of protein–ligand complexes and their docking affinities, while some samples include experimentally derived binding affinities. Prediction is based on accessing ligands as active (active-interact molecules or positive) or inactive (set of decoys or negative) depending on whether the ligand is able to bind with the protein, as described in more detail below. Table Machine learning models for training PLI prediction require data on target proteins, ligands/compounds, and interactions between them. In this work, our goal is to improve the degree to which the graph-based model can be generalized while also maintaining accuracy. We accomplish this by enlarging the dataset used to train our model and including a variety of targets. We collected and curated protein–ligand complexes from two public datasets, DUD-ETo confirm that these two datasets offer diversity in terms of both target protein and different functionality of the ligand, we computed pair similarities between the targets and ligands, which can be found in the supporting information Figure 34. The dataset was originally designed to test benchmark molecular docking programs by providing challenging decoys, but some have noted that the dataset suffers from limited chemical space and biases38. In their analysis of the DUD-E dataset, Chen et al. generated a docked subset38, which we use here. Ligands from the experimentally verified ChEMBL dataset were designated as positive, while the generated decoys and their docked structures were considered negative. These files were parsed into individual standard database formats (SDF) files for each ligand and corresponding docking pose.The DUD-E dataset consists of pairs of experimentally verified active complexes and property-matched inactive pairs, called decoys39. The target proteins were pulled from the original DUD-E set, cleaned of water molecules, and matched with their designated ligands. Targets were screened with a homology test to understand the diversity of targets present in the dataset, and none had a similarity greater than 90%. Each complex was processed to crop the target protein and retain atoms within a distance of 8 Å from the ligand. Because the DUD-E dataset is biased towards inactive complexes due to the large number of decoys, equal counts of active and inactive samples were collected in regards of a target with no consideration of a specific complex. A complete 1:1 active-to-inactive ratio was not always achieved; however, the imbalance was found to be minimal and to have no affect on performance. Samples from the experimentally verified ChEMBL dataset were labeled as active, while decoy samples were denoted with the key word ZINC.Ligands with a molecular weight above 500 Da were removed, and the docked structures were converted to a machine-readable format using RDKit36. Binding poses for a refined set of the protein–ligand complexes were generated by docking calculations40. A 90% homology test was run on this set, which resulted in a small number of proteins being removed because of a high level of similarity. As with the DUD-E dataset, the docked structures were converted to a machine-readable format using RDKit. The root-mean-square distance (RMSD) was used to label ligands as positive if they maintained an RMSD less than or equal to 2Åcompared to the original crystal structure, and negative if the RMSD was greater than or equal to 4Å. Molecules with RMSDs between these thresholds were removed. The viable molecules and their target proteins were processed into a dataset of protein atoms cropped within a distance of 8Å. As PDBbind data were significantly limited, all available samples were used. This resulted in a greater inclusion of negative samples than positive samples.The PDBbind dataset contains experimentally verified protein–ligand complexes from the Protein Data Bank41 and manually curated antiviral data were used to generate the docked complexes with each target. Ten docking poses were calculated for each complex with the qvina docking program42 through a custom non-covalent pipeline. The docking data was parsed and cropped to the 8 Å threshold in the same manner as the DUD-E and PBBind datasets. Similar to PDBbind, positive and negative samples are determined with RMSD. Protease data were largely directed into the training set while the other targets, with the exception of on non-structural protein, were directed to the test set. This variety allows the inclusion of the critical targets of SARS-CoV-2 viral life cycle in both the training and the test sets.The SARS-CoV-2 dataset is composed of seven protein targets: Mwww.ibscreen.com). All ligands were fed as an input to the our GNN models paired against MThe IBS dataset is created using 486,232 synthetic compounds from the IB screening database  PDBbind2016, (2) PDBbind2018, and (3) PDBbind2019For the PDBbind2016 dataset, we prepared various train-test splits, which are summarized in Table We focus on the For the PDBbind2016 data, all crystal poses with an experimentally measured 2 is obtained as a result of non-covalent inhibition of MCULE-5948770040 compound with M2. When a protein–ligand complex had multiple measured We also considered an independent dataset associated with SARS-CoV-2 targets. To prepare this dataset, we used crystal structures of SARS-CoV-2 main protease  for the protein and ligand. To this end, we would like to understand the effects of the number of protein targets and the number of protein–ligand complexes per target; therefore, we train the In addition to making sure the model can be generally applied, we are interested in developing a model that does not require the docked structure to be known before inferences can be made. We performed the same experiment using varying numbers of DUD-E targets and complexes on our To assess the ability of our GNN models to be generally applied, we produced three test sets of varying similarity to the training set Table . Our basWe then assessed the model’s ability to identify top-scoring 3D poses in each protein–ligand complex from our PDBbind repository described in the dataset preparation section. The ’best’ docked pose is quantified as having an RMSD of less than 2 Å with respect to the crystal structure. In this analysis, we measure not only the ability of the model to identify an active protein–ligand pose but also its ability to identify the best pose among multiple docked poses. Figure In each rank, both the see Fig. .47.In addition, we tested our models on datasets that include molecules from ChEMBL for DUD-E targets and SARS-CoV-2-target-specific data. Additional ChEMBL data were collected for a small subset of DUD-E targets extracted from the initial test set and implemented without docking for the We next investigated the performance of the see Fig. . For thePredicting the binding affinity of a protein–ligand complex plays a critical role in identifying a lead molecule that binds with the protein target; however, the experimental measurement of protein–ligand binding affinity is laborious and time-consuming, which is one of the greatest bottlenecks in drug discovery. On the other hand, half maximal inhibitory concentration  PDBbind2018-docked, (2) PDBbind2018 crystal, and (3) PDBbind2016 crystal structure dataset. Since most of the deep learning models that we compare in this section are trained on complexes from the PDBbind2016 database, training our models on the same dataset helps to obtain a comparison with the previous models49, which is a 3D CNN for protein–ligand affinity prediction that combines the 3D voxelization with atom-level features. Among all the results, the best performance is achieved by the First, we assessed the performance of the GNN models trained using PDBbind2018 data docked and crystal-only data as shown in Table 50 and FAST48 models. Our analysis on the PDBbind2016 core set shows that the 48, while 48. Out of all the combinations of the general and refined sets, we achieved the best performance with the In addition, for non-docking experiments, we used the PDBbind2016 general and refined datasets for training and the PDBbind2016 core set for testing. We assessed the performance of our methods for predicting the affinity through comparison with the Our results on the PDBbind2018 and PDBbind2016 datasets suggests that with our proposed GNN frameworks, we achieve top performance compared with prior deep learning methods for binding affinity prediction while preserving the spatial orientation of the protein and ligand. While To investigate the generalizability of our GNN models in predicting the binding affinity of unseen and novel targets, we compare the performance of our GNN and various models on the PDBbind2019 structure-based evaluation dataset Table . The PDBFinally, to expand the scope of our experiment, we also trained our model on a physics-based docking affinity score. We refer to these models as Docking Binding Affinity (DBA) models. We compare our model’s performance against the physics-based docking on the two DBA datasets. Our results suggest that, with a correlation score of 0.79, the 21, DeepDTA22 and MONN33. While these methods were trained purely on the protein sequence and 2D SMILES representation of the ligands, our model is novel in that it considers the 3D structures of the protein and ligand to predict As a next step, we tailored our GNN models to predicting 21, DeepDTA22, and MONN33 were trained on BindingDB data, which has nearly 10 times the amount of data than that available for our dataset. In addition, we observed improvement in the Pearson correlation coefficients in predicting The overall performance of the GNN model is relatively low compared to the existing methods listed in the Table To assess how critical the learned protein and ligand representations are for predicting 2. Interestingly, Our extensive analysis on the SARS-CoV-2 MIn this work, we devised graph-based deep learning models, These implementations enable us to leverage the vast amount of 3D structural data of both the target protein and ligands, and interactions between them which is crucial for activity, potency, and affinity prediction to accelerate in silico hit identification during early stages of drug design. The goal of our extensive study is to generalize the graph-based models by incorporating domain-aware information, features, and biophysical properties and by utilizing a large amount of data including a variety of targets. The test accuracy for 50 with highest prediction correlation coefficients. Using PDBbind2016 data, we achieved Pearson correlation coefficients of 0.66 and 0.65 on experimental affinity prediction and 0.50 and 0.51 on 51, the GNN model performance can be further improved.Our GNN models were further modified for regression tasks to predict binding affinities and Our model is unique and novel in that it considers the 3D structures of the protein and ligand to predict affinity and https://github.com/PNNL-CompBio/pf-gnn_pli.The Supporting information is available with Table (S1–S8) and Figures (S1–S7)) detailing data, models, hyperparameter optimization, Performance Metrics for Classification and Regression GNN Models, IBS molecule properties, and Docking Binding affinity Data and Results used in this study. Description about hyper-parameters and the trained Supplementary Information.


What are the long-term outcomes among men who have prostate cancer (PC) that is managed with active surveillance?In this cohort study of 23 655 men in Sweden, those who were diagnosed with intermediate-risk PC at younger than 60 years had a high risk of PC death and little benefit in terms of time without active PC treatment. In contrast, men older than 65 years with low-risk PC had a lower risk of PC death and longer time without active PC treatment.These findings suggest that active surveillance may be a safe disease management strategy among men older than 65 years with low-risk PC. This cohort study applies a state transition model to assess 30-year trajectories of prostate cancer (PC) among men in Sweden with low-risk PC or intermediate-risk PC managed with active surveillance. The long-term outcomes among men with prostate cancer (PC) whose disease is managed with active surveillance (AS) remains unknown.To develop a simulation model with a 30-year follow-up for men with PC managed with AS.In this cohort study, a state transition model was created using data from Prostate Cancer data Base Sweden (PCBaSe) on 23 655 men diagnosed with PC and managed with deferred treatment to estimate treatment trajectories. A simulation was performed with 100 000 men in each combination of age at diagnosis, Charlson Comorbidity Index, and PC risk with a follow-up of 30 years.Death from PC and death from other causes were estimated, and the proportion of time without active PC treatment was assessed until date of death or age 85 years.This study included 23 655 men from PCBaSe with a median age at diagnosis of 69 years . Of these, 16 177 men underwent active surveillance for PC and 7478 underwent watchful waiting. The proportion of men who were diagnosed at age 55 years and died of PC before age 85 years was 9% for very low-risk PC, 13% for low-risk PC, and 15% for intermediate-risk PC. Among men with a Charlson Comorbidity Index of 0 who were diagnosed at age 70 years, the corresponding percentages were 3%, 6%, and 7%, respectively. The mean proportion of remaining life-years without active PC treatment for men diagnosed at age 55 years was 12 of 25 years (48%) for very low-risk PC, 9 of 25 years (36%) for low-risk PC, and 7 of 25 (29%) for intermediate-risk PC. For men aged 70 years, the corresponding numbers were 10 of 13 years (77%), 9 of 13 years (66%), and 8 of 13 years (60%), respectively. Men with intermediate-risk PC who were younger than 60 years at diagnosis had a high risk of PC death (12%-15%) and fewer remaining life-years without active PC treatment (29%-33%). In contrast, men with low-risk PC who were older than 65 years at diagnosis had a lower risk of PC death (3%-5%) and more remaining life-years without active PC treatment (62%-77%).The findings of this Swedish cohort study suggest that active surveillance may be a safe strategy for disease management among men with PC who were older than 65 years at diagnosis. Previous studies have reported on the transition from AS to curative treatment and on the risk of PC death for men on AS up to 15 years after starting AS.11Active surveillance (AS) is a management strategy for men with low-risk prostate cancer (PC) that entails conversion to active treatment if disease progression occurs, and it is used increasingly worldwide.12 Because such data are currently not directly available, we created a state transition model that estimated PC disease trajectory, including changes in treatment strategy and outcomes up to 30 years after start of AS.13 The aim of this study was to apply this model to assess 30-year PC trajectories in men with low- or intermediate-risk PC managed with AS. Specifically, we sought to identify men who would benefit the most from AS in terms of low risk of PC death and of treatment-free years.To fully assess long-term outcomes among men with PC who are undergoing AS, an extensive PC disease trajectory needs to be investigated given that this trajectory may include several subsequent treatments due to disease progression at several time points.STROBE) reporting guideline.This cohort study was approved by the Regional Ethical Review Board of Uppsala University. As in all Swedish clinical cancer registers and other quality registers, all men with PC are informed at time of diagnosis and treatment that they will be included in Sweden’s National Prostate Cancer Register (NPCR) unless they opt out; therefore, written informed consent was not required. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology , so this lack of data was mitigated by using information on dates of prostate biopsy obtained from the National Patient Registry in combination with recordings of watchful waiting and AS in the NPCR as described previously.9 In brief, regular use of prostate biopsies was used to model the probability of having received a biopsy, which served as an indication of adherence to AS.We identified men in Prostate Cancer data Base Sweden (PCBaSe) who had been diagnosed at age 40 to 75 years with very low-risk, low-risk, and intermediate-risk PC between 1992 and 2014 according to a modified version of National Comprehensive Cancer Network risk categorization  or accompanied by a new risk category .13 To visualize the timing and occurrence of state transitions in accordance with The analytical steps in the state transition model have been described previously in detail.21 To compensate for this grade migration, the model was allowed to select a group of men with very low-risk PC with the least favorable prognosis and to upgrade these men to a more advanced risk category. To this end, we identified men in the very low-risk category who had the highest risk of a transition to androgen deprivation therapy and upgraded 5%, 10%, 15%, and 20% of them to a less favorable risk category.Because of the modification in the International Society of Urological Pathology classification of Gleason scoring in 2005, a substantial number of cancers that were graded as Gleason score 6 prior to 2005 would have been graded as Gleason score 7 after 2005.There were 23 655 men from PCBaSe included in the study with a median age at diagnosis of 69 years . Of these, 16 177 underwent AS and 7478 underwent watchful waiting . The medWe estimated the remaining life-years spent without active PC treatment as the ratio between the proportion of the area under the curve for AS and watchful waiting  and a considerably higher risk of PC death . Overall, men older than 65 years with low-risk PC had a high proportion of treatment-free years (53%-70%) and a low risk of PC death (6%-8%). At an earlier time point (15 years), PC death was 2.6% for low-risk PC.The results of the sensitivity analysis of different risk categories on AS were consistent with the primary results , they differ from our study in several important ways. First, our simulation was based on population-based data with a capture of 99% of all men diagnosed with PC in the Swedish Cancer Registry to which reporting is mandated by law, whereas the existing series with long-term data began as experimental programs at tertiary referral centers.23 This means that a proportion of men in our cohort would not have been selected for AS at a center of excellence. Moreover, because the follow-up schedule in our study was at the discretion of the treating physician, there is also the issue of difficult standardization of follow-up schemes, and eventually lack of adherence. We believe that the lack of selection and the lack of optimal follow-up are 2 reasons why the observed outcomes on which we base our models are different from those from selected cohorts. Johns Hopkins University School of Medicine, the AS program with the most stringent enrollment criteria,22 reports a 15-year cancer-specific survival that is greater than 99%. In this program, most of the enrolled men (>70%) had very low-risk PC and underwent strict follow-up. A scenario more similar to our study has been reported by Godtman et al11 and Klotz et al4; both series reported a 94% 15-year cancer-specific survival among men with low-risk PC , which is consistent with our model   .24 However, in these early studies, the crude classification of cancer stage, tumor grade, and PSA levels makes it difficult to extrapolate these results to men diagnosed and classified with current workup methods.The long-term natural history of currently diagnosed low-risk PC is unknown. Previous studies have shown that risk of PC death persists up to 30 years after diagnosis in men with low-risk PC who undergo watchful waiting.18 This model has previously been shown to accurately predict state transitions .13 In our model, we updated age, PC characteristics, and comorbidity, which allowed us to estimate transition to watchful waiting as well as the proportion of remaining life-years without treatment until either death or end of follow-up.12 In comparison, conventional survival analysis merely provides data on a single end point, neglecting both the path to that specific outcome and time-updated cancer characteristics and comorbidity.24The strengths of our study include the high-quality, population-based data on PC characteristics, treatments, updated information on comorbidity, and a virtually complete nationwide capture that allowed us to model a 30-year follow-up based on a population-based cohort.21 However, our sensitivity analyses confirmed that such limitations did not interfere with our model estimates. There are Swedish guidelines with detailed recommendations for inclusion criteria for AS and instructions for follow-up, and adherence to these guidelines was recently shown to be quite high,1 with 92% of Swedish men with very low-risk PC being managed with AS in 2020.3 The definitions of AS and watchful waiting in the NPCR are consistent with international guidelines.27 Compliance with repeat biopsy has not been very high in Sweden,28 although it has improved considerably since the introduction and diffusion of multiparametric prostate magnetic resonance imaging.29Our study has some limitations. Before 2006, registration in the NPCR did not distinguish between AS and watchful waiting, and prostate volume was not registered in the NPCR until 2008, so we were unable to calculate PSA density for men diagnosed before that time point. Furthermore, the modification of Gleason scoring by the International Society of Urological Pathology in 2005 resulted in tumor grade inflation.To the best of our knowledge, this study is the first to compare the benefit of increased life-years without active treatment  with the adverse effects of treatment. As expected, we found a greater benefit among older men with low-risk PC vs younger men with intermediate-risk PC. Our results are likely to depict a worst-case scenario given that our model was based on nationwide, population-based quality of care registry data rather than data from tertiary referral centers, and our inclusion period overlapped with the aforementioned limitations in disease classification and indication for deferred treatment. Despite these limitations, our findings could help clinicians and patients with PC select optimal treatment strategies. Additionally, the recent introduction of new diagnostic tools not specifically considered in this study  may improve the selection process when assigning men to AS, which may lead to better long-term outcomes.The findings of this Swedish cohort study suggest that men older than 65 years with low-risk PC had a high proportion of treatment-free years (53%-70%) and a low risk of PC death (6%-8%), hence AS was indicated among men in this subgroup. In contrast, in men younger than 65 years, AS appeared to be indicated only in those with very low-risk PC. Our state transition model based on data for men who were diagnosed between 1992 and 2014 likely provides a worst-case scenario that may improve in the future owing to enhanced diagnostic technology.


Concomitant schwannomas and benign meningothelial proliferations, including meningothelial hyperplasia or meningioma, rarely occur at the same location outside the setting of neurofibromatosis. Herein, we present a rare case of concurrent schwannoma and benign meningothelial hyperplasia concomitantly occurring in the cervical spine of a 69-year-old male patient with no history of any genetic disorder. Schwannomas and meningiomas are the two most common spinal tumors, representing 30% and 25% of spinal cord tumors, respectively . In the Meningothelial hyperplasia is a poorly characterized entity that is thought to be reactive in nature and is often seen in association with advanced age, chronic kidney disease, trauma, hemorrhage, and neoplasia , 8. In tHerein, we present the case of concomitant schwannoma and a benign meningothelial hyperplasia arising in the cervical spine of a 69-year-old man.A 69-year-old male patient presents with a chief complaint of neck pain of several weeks, radiating to the head and right shoulder. The pain is reported to be intermittent, burning in quality, associated with weakness and numbness especially in the right hand. The patient has a past medical history of hypertension, hyperlipidemia, benign prostatic hyperplasia, DVT/pulmonary embolus in the past year for which he is on anticoagulant therapy, and a surgical history of bilateral knee replacement within the past two years. The patient has no history of neurofibromatosis or stigmata of another genetic disorder. Upon physical examination, the patient has right hemiparesis (-4/5) that has mildly progressed compared to prior examination. MRI of the cervical spine (performed at an outside institution) reveals a T1 hypointense and T2 heterogeneously hyperintense extramedullary mass causing impingement upon the right side of the spinal cord which is flattened and displayed to the left at the C2-C3 level. The mass measures approximately 35 mm in transverse diameter by 13 mm in AP diameter by 18 mm in craniocaudal diameter and laterally extends through the right C2-C3 neural foramen. The radiologic impression is that of a nerve sheath tumor of either benign or malignant nature (no images available). Surgery is recommended, and the patient undergoes a C2-C3 laminectomy at an outside institution with resection of the tumor. Intraoperatively, the tumor is seen on the right side of the canal with the cord. Dorsolaterally, the tumor appears to blend in with the dura at its base. The junction is coagulated with bipolar cautery. Then, some of the tumor is debulked along the base, allowing it to shrink away from the cord laterally. Working around the superior pole of the tumor then reveals rootlets that appear to be joining the tumor to the right superior pole along its posterior aspect. Attempting to dissect to release it reveals that the rootlets were not just adherent to the capsule but rather growing into the tumor. The rootlets are hence cauterized and amputated at base. The tumor is removed in piecemeal fashion, and the tissue is sent to pathology. Grossly, the specimen consists of multiple pieces of light tan, firm tissue measuring in aggregate (2.0 × 1.6 × 0.3 cm).The slides are sent to our institution for review. Microscopic examination reveals fragments of a benign nerve sheath tumor comprised of Schwannian cells arranged in loose fascicles set in an eosinophilic stroma with occasional hyalinized vessels and Verocay bodies, compatible with Schwannoma Figures . Also prThe overall histopathological and immunohistochemical findings are compatible with Schwannoma with a small focus of benign meningothelial proliferation compatible with benign meningothelial hyperplasia. MRI with and without contrast performed three months after the surgery shows postoperative changes of subtotal resection of the enhancing mass centered about the right C2-C3 foramen, with a residual 21 × 10 × 16 mm  right neural foraminal mass and significant improvement in the spinal cord compression. At six month postsurgery, MRI demonstrates improved postsurgical changes with redemonstration of the residual spinal enhancing mass at the right C2-C3 neural foramen extending into the spinal canal with minimal mass effect on the spinal cord on the right aspect of the cord at this level. No further available clinical data could be obtained due to the patient being lost to follow-up.While previous cases of simultaneous schwannoma and meningiomas arising at the same level of cervical spine have been reported, concomitant schwannoma and meningothelial hyperplasia have not been previously reported at the same location in any site. In the present case, a 69-year-old man presents with an intradural extramedullary mass at C2-C3. Pathology shows a schwannoma with a separate fragment of meningothelial proliferation, most compatible with meningothelial hyperplasia. The previously reported seven cases of concurrent schwannoma and meningioma occurring at the same spinal level consisted of a predominantly extradural component of the mass which was a schwannoma in addition to a smaller intradural extramedullary component which represented a meningioma –3, 5, 6,In contrast to the previously reported cases, in our case, only one mass was discernible macroscopically, thus favoring meningothelial hyperplasia over a concomitant meningioma. Among the eleven reported cases of meningothelial hyperplasia, 64% overlapped immunohistochemically with meningiomas in terms of progesterone receptor (PR) expression and two cases showed chromosomal gains, in contrast to normal arachnoidal cap cells that were negative for PR and showed no chromosomal abnormalities . FurtherWhen it comes to cases of concomitant schwannoma and meningioma of the cervical spine without associated syndromes, based on the available literature, clinical follow-up was reported on two cases with no tumor recurrence 24 and 6 months after surgery , 6. No fFor cervical schwannomas arising at the high cervical levels (C1-C3), microneurosurgical management using a standard midline posterior approach, which was suggested to be the most suitable type of approach for these tumors, in combination with proper neuroradiological observation and pathological appreciations can give satisfying neurological outcomes without extensive bone removal or soft tissue manipulation . CompletSpinal meningiomas are usually benign slow-growing tumors that respond favorably to surgical excision, especially complete resection, with low complication rates and a good long-term functional outcome , 14. A lCervical spine meningiomas constitute a very small proportion of spinal schwannomas. The largest case series consists of twenty-two cases of cervical spine meningiomas . The funPathogenetically, schwannomas are believed to arise from Schwann cells of the dorsal root nerves while meningiomas are thought to arise from the arachnoid membrane/cap cells , 6. TherRegarding the latter theory, reactive meningothelial hyperplasia caused by a preexisting tumor is a consideration in our case. In fact, Geddes et al. proposed it as a plausible mechanism for reactive meningothelial hyperplasia occurring in the vicinity of a schwannoma . Even thWe report the case of a concomitant schwannoma and meningothelial hyperplasia present within the high cervical spine of a 69-year-old man with no history of any genetic disorder. This is a rare phenomenon of uncertain biologic nature for which the underlying mechanism remains to be fully elucidated.


There is a strong association between increased mobile device use and worse dietary habits, worse sleep outcomes, and poor academic performance in children. Self-report or parent-proxy report of children’s screen time has been the most common method of measuring screen time, which may be imprecise or biased.The objective of this study was to assess the feasibility of measuring the screen time of children on mobile devices using the Family Level Assessment of Screen Use (FLASH)–mobile approach, an innovative method that leverages the existing features of the Android platform.This pilot study consisted of 2 laboratory-based observational feasibility studies and 2 home-based feasibility studies in the United States. A total of 48 parent-child dyads consisting of a parent and child aged 6 to 11 years participated in the pilot study. The children had to have their own or shared Android device. The laboratory-based studies included a standardized series of tasks while using the mobile device or watching television, which were video recorded. Video recordings were coded by staff for a gold standard comparison. The home-based studies instructed the parent-child dyads to use their mobile device as they typically use it over 3 days. Parents received a copy of the use logs at the end of the study and completed an exit interview in which they were asked to review their logs and share their perceptions and suggestions for the improvement of the FLASH-mobile approach.The final version of the FLASH-mobile approach resulted in user identification compliance rates of >90% for smartphones and >80% for tablets. For laboratory-based studies, a mean agreement of 73.6% (SD 16.15%) was achieved compared with the gold standard (human coding of video recordings) in capturing the target child’s mobile use. Qualitative feedback from parents and children revealed that parents found the FLASH-mobile approach useful for tracking how much time their child spends using the mobile device as well as tracking the apps they used. Some parents revealed concerns over privacy and provided suggestions for improving the FLASH-mobile approach.The FLASH-mobile approach offers an important new research approach to measure children’s use of mobile devices more accurately across several days, even when the child shares the device with other family members. With additional enhancement and validation studies, this approach can significantly advance the measurement of mobile device use among young children. Screen use includes mobile device use, television watching, playing video games, and computer use. Strong associations have been demonstrated between increased screen time and worse sleep outcomes, locomotive skills, and dietary habits in children -4. ScreeThe most common approach to measuring screen use is through self-report or parent-proxy report of children’s screen use by questionnaires, which introduces errors and biases , affectiThe difficulties in accurately measuring children’s mobile device use demonstrate an acute need for new measurement tools to accomplish this goal. We have already developed a novel approach for measuring children’s television viewing . There aIn this study, our goal was to develop an innovative approach that leverages the capabilities of smartphone apps to develop a less intrusive yet accurate method for assessing children’s mobile screen use. This study describes the Family Level Assessment of Screen use (FLASH) for mobile devices. The FLASH-mobile approach makes use of (1) an app to track device and app use logging, (2) notifications to identify the user of the device, (3) a system to provide feedback on compliance with the user identification notification to improve compliance, and (4) a minimum threshold of user identification to be included in the analysis. In this study, we used the HealthSense platform  for implA total of 4 feasibility studies tested multiple iterations of the FLASH-mobile approach on mobile phones and tablets. Participants included a parent and at least one child aged 6 to 11 years. Data were collected from December 2019 to August 2021 by installing HealthSense, an app that tracks the use of the device and corresponding apps, on the participants’ mobile phone or tablet.This research was approved by the Baylor College of Medicine Institutional Review Board (H-40556), with a reciprocal authorized agreement with Rice University. Parents provided written, informed consent for themselves and their child, and the children provided assent to participate.Participants were recruited from the community and had to live in the United States, speak English, and be a parent of a child aged between 6 and 11 years, and the child had to have their own or share another family member’s Android device (smartphone or tablet). Participants were excluded if the parent or child had a developmental, medical, mental, or physical condition that would prevent them from following the study protocol. Families with mobile devices with other operating systems (OSs), such as Apple iOS or Amazon Fire OS, were excluded because other OSs do not support the HealthSense platform. For laboratory-based protocols, a study-owned backup device was available in case of technical difficulties with the participant’s device. Families were compensated US $40 for their participation.The FLASH-mobile approach was developed to estimate the total time a target child spent using a mobile device. Children often share a mobile device with their siblings or a parent. Therefore, 4 steps were developed to assess the mobile device use of a child from a shared device: (1) an app to track the Android OS app use statistics on the device to identify mobile device use time, duration, and which apps were used; (2) identification of the user of the mobile device via device notifications when unlocked; (3) a system to provide feedback on compliance with the user identification notifications to improve compliance; and (4) a minimum threshold of user identification compliance to be included in the analysis. Several iterative versions of the user identification procedure were tested based on feedback from the participants on earlier versions.The FLASH-mobile approach was implemented using the HealthSense app , developchild or a gray button with the word other , a pop-up prompt appeared when the phone was unlocked, which had to be answered to use the device; version 2 (V2), a notification banner appeared when the phone was unlocked and after every 15 minutes of use; and version 3 (V3), a notification banner that appeared only when the phone was unlocked. In each case, the user had to identify themselves by selecting 1 of 2 options, an orange button with the word rd other . There wWe conducted 4 feasibility studies. Parents were instructed to download the HealthSense app. The type of Android device used, model number of the device, OS number, whether the HealthSense app was successfully downloaded, and whether the user identification feature worked as expected were tracked. Two laboratory-based protocols  or using the screen while multitasking . This is similar to how others have defined screen use, including as background television . A totalr person . In all,r person -29 , streaming video services , gaming, social media, browsing, Android OS, and others . Parents were asked to verify whether the apps listed were correct; whether any apps were missing or added incorrectly; and whether the timing, duration, and user identified were correct. All the interviews were audio recorded, transcribed, and coded by 3 study staff members , using NVivo  software. A codebook was created based on an inductive coding of themes [All the parents were asked to share their perceptions of using the FLASH-mobile approach and provide suggestions for its improvement. The semistructured interviews followed a standardized script, which included probes and prompts to clarify responses  child directly using mobile screen alone, (2) using mobile screen with someone else, and (3) multitasking mobile screen use with other activities . The agreement between the HealthSense output and staff coding for mobile screen use in feasibility studies A (in laboratory) and B (in laboratory) was tested using the percentage of agreement, prevalence- and bias-adjusted Cohen κ statistic, and intraclass correlation coefficient.A total of 48 parent-child dyads participated in the study . Each dyad participated in only 1 study. Of all the 48 enrolled participants, 33 (68%) were able to download the app on their own device and have all the components function. For the observational laboratory studies, a study device was available for 2 families whose device did not work, resulting in 73% (35/48) of participants with complete data from the HealthSense app. The demographics of the sample are shown in Problems with the installation of the app on participants’ mobile devices included the nonappearance of user identification, lack of tracking app use, inability to install HealthSense on the device, problems due to phones and tablets with older Android OS systems (<8), and incompatibility of certain mobile device models with the HealthSense app. In some cases, the reason for malfunction was unknown. A full description of installation issues is provided in Compliance with the user identification procedure for the at-home feasibility tests increased over the iterations of the HealthSense app from versions V1 to V3 . HealthSchild or other’s use. Hence, a threshold of ≥70% of identified use in the overall use of the mobile device was selected to identify quality assessments for reporting target child’s mobile device use. Those with lower identified mobile device use were screened out because their inclusion would lead to discrepancies resulting from user behavior (low compliance) rather than HealthSense app behavior.The accuracy of the FLASH-mobile approach in capturing children’s mobile device use was assessed in the laboratory observation, and it was found that most users  had high compliance with the log-in notification, with >80% of use identified, whereas a smaller proportion  had very low compliance, with >70% of use unidentified. When the user compliance was low, HealthSense identified app use but could not identify who used the app, resulting in an unidentified use label and disagreement with staff coding between For the users with high compliance (≥70% of app use identified), HealthSense identified the target child’s mean use as 21.76 (SD 20.71) minutes, whereas the gold standard was 18.30 (SD 10.77) minutes. Among the 12 families with high compliance with user identification, HealthSense achieved a 73.6% (range 44.7%-96.14%) mean agreement with human labelers in capturing the target child’s mobile use, 78.9% (range 3.55%-97.5%) in capturing other’s mobile use, 2.9% (range 0%-17.75%) agreement in capturing unidentified mobile use, and 85.2% (range 38.37%-98.37%) agreement in capturing no mobile use. Among the 3 families with low compliance (<70% of app use identified), mean agreement between human labelers and target child’s mobile use was 37% (range 0%-78.29%), 2.1% (range 0%-6.33%) agreement in capturing other’s mobile use, 82.7% (range 73.6%-97.17%) agreement in capturing unidentified mobile use, and 95.9% (range 93.82%-98.33%) agreement in capturing no mobile use. The prevalence- and bias-adjusted Cohen κ value for the agreement between HealthSense and the gold standard was 0.711 (SD 0.301), and the intraclass correlation coefficient was 0.714 for the high-compliance users, indicating high agreement between the gold standard and HealthSense estimate -35.other use or no user was identified. The former happened when either parent or sibling logged in as the user and the child started using the device or the target child started coviewing the mobile device along with their parent or sibling, which staff labeled as child use. The latter case happened when the user of the device ignored the log-in prompt, failing to identify the user.The times HealthSense and gold standard disagreed were primarily due to HealthSense either identifying child use as After qualitatively reviewing the 3-day app and user log, among 23, only 2 (9%) parents reported minor discrepancies in the mobile use reports of their child . The remaining 91% (21/23) of parents agreed with the HealthSense log of their child’s mobile device use.The parents from the observation laboratory studies tended to have positive or neutral feelings toward the FLASH-mobile approach, stating that it seemed fine, did not impact the child’s enjoyment while using the phone, and could allow them to track their child’s screen time. Although most parents had no issues with HealthSense and did not think that it was intrusive, there were a few concerns about privacy, for instance, regarding who would have access to the data and what was actually being recorded.Most  parents from the at-home studies found the app useful for determining how much time their child spent using the mobile device and to review the apps they used. More than half  of participants who tested the versions with a user identification pop-up and 15-minute reminders (V1 and V2) disliked the pop-up button. They found the pop-up annoying and too frequent. Those who tested V3, with an unlock user identification notification, had mixed reactions. Approximately, half  of the participants liked the notification and found no issues with it, but 13% (2/15) of participants stated that the notification was annoying. The remaining parents  had a positive reaction to the notification but added that it was difficult to notice. Moreover, 13% (2/15) of participants reported that the app slowed down their phones and did not allow some calls to come through.Suggestions for the improvement of the FLASH-mobile approach on phones included simplifying the user identification feature; making it available for other devices such as tablets, televisions, and Apple iOS devices; adding a time-restriction function; adding games or an educational component; having a unique personal identification number or password per user; and blocking phone use until user identification is selected. Others suggested including the user identification requirement only on shared devices. A few parents using mobile phones expressed privacy concerns over their information being sold or stolen and other security risks, and some felt uncomfortable with HealthSense recording not just the child’s but also the parent’s mobile phone use. None of the parents using tablets expressed similar concerns about privacy.The FLASH-mobile approach, a mobile device tracking app on an Android device, included four features: (1) user identification prompts, (2) app use tracking ability, (3) feedback on compliance with the user identification notification via text or email, and (4) a minimum threshold of user identification compliance to best capture mobile device use by a child. This approach was found to be feasible to measure children’s mobile device use. The FLASH-mobile approach addresses differentiating users of shared devices by sending a notification banner for the user to identify themselves every time the mobile device is unlocked along with intermittent communication with participants to praise or remind participants to be compliant. This provided >90% compliance rates with user identification for smartphones and >80% for tablets. In this small feasibility study, the threshold was 70% compliance with user identification, but further studies need to verify whether this threshold continues to correspond to high agreement between the FLASH-mobile approach and gold standard observation. When compliance with user identification reached this threshold, FLASH-mobile approach achieved a 73.6% (SD 16.15%) mean agreement with human labelers in capturing the target child’s mobile use . There is no agreed-upon definition in the literature for screen use by children. Others have conceptualized screen use of mobile devices as any time a device is used by its owner, regardless of whether the person is viewing the screen ,36. The During the course of this study, modifications were made to the FLASH-mobile approach to optimize it and adapt to the continually changing aspects of the Android OS. For example, the full-screen pop-up prompts used in the initial version (V1) of the FLASH-mobile approach are no longer supported by the latest Android OS versions because of new security constraints. Therefore, notification banners were used in the second version (V2), which appeared when the phone was unlocked. In addition, to better identify the user of the device, notification banners were programmed to appear after every 15 minutes of device use. The results from study B (at home) demonstrated that the 15-minute notification was not user-friendly, thus reducing user compliance. Therefore, the final version (V3) of the FLASH-mobile approach included only the unlock notification banner. Such a notification banner is supported by past and current versions of the Android OS. Although a few parents disliked the notification banner, it was well received by most parents and resulted in the lowest proportion of unidentified mobile device use.Although FLASH-mobile is not publicly available, it could be reproduced using specific Android methods -21. MoreOnly a few other digital tracking approaches have been used to measure children’s mobile device use, such as similar tracking apps on Android devices , screensSimilar to Radesky et al , the FLAThis study has several important limitations. The sample was small, recruited through convenience sampling, and thus not representative. However, the sample does reflect a racially and economically diverse group, which is important for demonstrating usefulness for studies targeting diverse children. The small sample with a limited number of days of data collection (3 days) was also not meant to establish typical mobile device use among children. Because the FLASH-mobile approach requires an app, such as HealthSense, Chronicle , or MinuThe low compliance of some participants with the log-in notifications was a challenge, as it resulted in high unidentified app use on the mobile device in earlier versions of testing. In the future, user identification prompts should be made more child-friendly, or they may be turned off when the child does not share the mobile device with others. In addition, future versions of the approach could include automated reminders and encouraging messages for participants to comply with user identification instead of relying on staff. We also found that having a uniform auto screen lock time  increased the app's ability to accurately identify the user (data not presented). However, the problem of user identification arises only when the mobile device is shared. Many young children now have their own mobile device, including 46% of children aged 2 to 4 years and 67% of children aged 5 to 8 years , in whicThe FLASH-mobile approach can be implemented using a mobile device app on an Android device with 4 features: user identification prompts, device and app use tracking ability, feedback on compliance with user identification, and thresholds of compliance to identify valid data. It offers an important new research approach to more accurately measure children’s use of mobile devices across one or several days, even when the child shares the device with others. By providing time-stamped data of app use for a specific user, the FLASH-mobile approach will allow for better causal assessments of how the duration, timing, and type of mobile device use by children can affect their developmental and health outcomes. Other researchers can use this approach to further advance the measurement of mobile device use among children.


Training physicians to provide effective behavior change counseling using approaches such as motivational communication (MC) is an important aspect of noncommunicable chronic disease prevention and management. However, existing evaluation tools for MC skills are complex, invasive, time consuming, and impractical for use within the medical context.The objective of this study is to develop and validate a short web-based tool for evaluating health care provider (HCP) skills in MC—the Motivational Communication Competency Assessment Test (MC-CAT).base case) and translating the cases into French, integrating the cases into the web-based MC-CAT platform, and conducting initial internal validity assessments with university health students.Between 2016 and 2021, starting with a set of 11 previously identified core MC competencies and using a 5-step, mixed methods, integrated knowledge translation approach, the MC-CAT was created by developing a series of 4 base cases and a scoring scheme, validating the base cases and scoring scheme with international experts, creating 3 alternative versions of the 4 base cases  and ranking scores (P=.24 to P=.89). The initial tests of internal consistency for rank order among the 24 student participants were in the acceptable range (α=.78).The MC-CAT assesses MC competency in 20 minutes by presenting HCPs with 4 out of a possible 16 cases (randomly selected and ordered) addressing various behavioral targets . Individual and global competency scores were calculated automatically for the 11 competency items across the 4 cases, providing automatic scores out of 100. From the factorial analysis of variance for the difference in competency and ranking scores, no significant differences were identified between the different case versions across individual and global competency (The results suggest that MC-CAT is an internally valid tool to facilitate the evaluation of MC competencies among HCPs and is ready to undergo comprehensive psychometric property analyses with a national sample of health care providers. Once psychometric property assessments have been completed, this tool is expected to facilitate the assessment of MC skills among HCPs, skills that will better support patients in adopting healthier lifestyles, which will significantly reduce the personal, social, and economic burdens of noncommunicable chronic diseases. The World Health Organization estimates that >71% of deaths worldwide result from noncommunicable diseases (NCDs), including cardiovascular disease, cancer, chronic lung disease, diabetes, and obesity . This ispersuasive information and advice [told what to do [As part of offering comprehensive care, health care providers (HCPs) are often responsible for providing some form of behavior change counseling (BCC) to patients who exhibit health risk behaviors. At present, this typically takes the form of offering d advice -7, whichat to do ,9. When at to do . Howeverat to do ,12.change-talk or evocation, (not) being argumentative or confrontational, setting goals, providing information neutrally, and being collaborative [To address the limitations of motivational interviewing, we codeveloped (with behavior change experts and HCPs) a new BCC approach called motivational communication (MC), which is based on motivational interviewing and theoretical models of behavior change , we also developed an accompanying MC competency assessment tool to evaluate skill acquisition among HCPs receiving training in this approach. A recent review of the literature on the quality of existing communication assessment tools among HCPs revealed a great deal of heterogeneity over the 45 different assessment tools that were identified. This review also indicated that few tools were developed using appropriate theoretical models (49%), and many failed to clearly define or describe the communication competencies they were designed to evaluate (19%) [real life. Existing competency assessment tools are hence complex, invasive, time-consuming, and impractical for use in many medical contexts.After defining MC as well as developing the content of the MC training program to be delivered to HCPs (the te (19%) . In addite (19%) , potentiEffective, feasible, and user-friendly competency evaluation tools are important not only for assessing the quality and efficacy of training programs but also for ensuring that patients benefit from the BCC methods used by HCPs. Using an integrated knowledge translation (iKT) approach, which is a collaborative model of knowledge production between stakeholders and researchers ,19, the The concept of the web-based MC-CAT assessment tool is to present HCPs with a series of patient cases with a specific behavioral target . Each case comprises a simulated interaction between a virtual patient and the provider, which focuses on engaging the patient in a discussion about changing their health behavior. Patient information  is accessible by clicking on the icon in the top right-hand corner . Each MCCentre Intégré Universitaires de Santé et de Service Sociaux du Nord-de-l’île-de-Montréal (number 2016-1206), and all participants provided informed consent electronically.Ethics approval was provided by the We have previously defined the 11 core communication competencies of MC , which r.The goal of this step was to create 4 base cases  to assess all 11 MC competencies through simulated medical consultations. One of the primary aims was to develop an assessment tool that would accurately reflect real-life medical consultations targeting health behavior changes in the context of NCD management. To ensure that the behavioral targets of our cases were relevant to clinical practice, we sent an open invitation to Canadian physicians from 4 target specialties known to treat a high volume of patients with NCDs  to complete a brief (10-minute) web-based survey (LimeSurvey GmbH), which was available in English and French. Invitations were emailed to physicians throughout January 2018 through relevant medical associations  and networks . In this survey, physicians were asked to identify what they believed to be the most important health risk behaviors that they encountered with patients in their efforts to prevent or treat NCDs (checklist with the possibility of adding behaviors) and rank them in order of their relative importance (from most important to least important). This resulted in the identification of the 4 most common health risk behaviors encountered in the context of NCD prevention and management, which would form the behavioral targets of the 4 base cases of MC-CATThe research team then proceeded to develop 4 base cases around the 4 most important health risk behaviors as identified by physicians, which acted as templates to develop alternate case versions to expand the tool to 16 cases . Base cases were designed to simulate a consultation between an HCP and a patient, which focused on engaging the patient in a conversation about changing their health behavior. Between October 2018 and March 2020, the research team worked on developing 4 base case scripts. The goal was to create a conversation flow beginning with the patient providing an opening statement of concern, after which the HCP was prompted to reply by selecting 1 of 5 multiple-choice responses, reflecting the most MC-consistent responses to the least MC-inconsistent responses (scored from 1 to 5). Each base case included 6 to 7 levels of exchange, each providing the HCP the opportunity to demonstrate ≥1 of the 11 core MC competencies. This resulted in a scoring scheme that indicated the extent to which responses were MC consistent or inconsistent on a 5-point scale . The scoring format was constructed with a range from +2 to −2 to reflect the nonneutrality of responding in an MC-inconsistent manner (which may be counterproductive for behavior change). Care was taken to provide HCPs with multiple opportunities to demonstrate each of the 11 MC competencies across the 4 cases to ensure that >1 interaction could be used to calculate an individual competency score. MC competency scores were calculated automatically by averaging the individual competency scores across the 4 cases, which were then summed and converted to a global score out of 100, reflecting overall MC competency .good if the agreement with the experts was perfect, acceptable if the expert rank was a +1-point or –1-point deviation from the rank the research team had indicated , and poor if the expert order was a +2-point –2-point deviation from the research team rank . These results were used to make minor modifications to some response items based on the criteria summarized in After creating the 4 base cases , we proceeded to validate the presence of the different MC competencies reflected in each case, as well as their rank order from most to least MC consistent, using a multi-round survey among a sample of international experts  in the demographic variables of the virtual patients , NCDs, and contextual variables  across cases. To achieve this, we adapted the 4 base cases to expand the test bank  without altering the core cases’ original structure . As such, every MC-CAT assessment of 4 randomly selected cases should maintain the integrity and psychometric properties by including a variant of each of the 4 original cases. This resulted in the creation of 16 unique patient cases  reflecting a variety of cases that would be appropriate for multiple types of NCD management situations and relevant across different medical specialties. These 16 cases were then formally translated into French  [One of the aims was to design the MC-CAT to allow for the variation   to permiThe MC-CAT provides a subscale score for each of the 11 MC competencies, as well as a global score summarizing overall MC competency. Individual competency scores were calculated by considering the number of times the person had chosen responses that included a certain competency  across the 4 cases, divided by the total number of times the competency could have been chosen across the evaluation, and multiplied by the relative proportion of opportunities to demonstrate that competency over all the competencies evaluated.change-talk or evocation; setting goals; providing information neutrally; and being collaborative) are aggregated together, and the negative competencies  are subtracted from the sum, as reflected in the following equation:To obtain scores for global competency, the scores for the positive competencies , including 32 virtual patient cases  and a demographic questionnaire  was designed and created in collaboration with 42 Comets Inc, a software developer with expertise in the creation of electronic education and training programs. To test the user interface of the MC-CAT, we conducted user experience research with 27 volunteer graduate students, HCPs, and behavior change experts also involved in step 2. The goal was to determine (1) the clarity of the instructions and tasks, (2) the navigability of the platform, (3) the synchronicity between audio and video information, and (4) the acceptability of the duration of the assessment. The responses were used to refine the aspects of the web-based interface to optimize the functionality of the program.We refined the aspects of the web-based interface based on user experience testing. The final step in the development process was to collect preliminary psychometric properties of the MC-CAT from a sample of MC-naïve undergraduate allied HCP students who were recruited via email and invited to complete 2 MC-CAT assessments approximately 12 weeks apart. Each assessment involved completing a basic sociodemographic questionnaire followed by the MC-CAT .To evaluate the consistency of the ranking and competency scores between the base cases  and the modified cases , a factorial ANOVA for the difference in competency and ranking scores was used to determine differences between case variations  across individual competency scores, global competency scores, and ranking scores.The internal consistency of the MC-CAT was obtained by calculating the Cronbach α coefficient . The ranWe received 154 surveys, of which 80 (52%) physicians had complete data  and were included in the analyses. The mean age was 49 (SD 12.9) years. Of the 80 physicians, 44 (55%) were male, and 50 (63%) identified French as the first language. The mean duration of practice of the physicians was 18 (SD 11.9) years, and 69% (55/80) of physicians were working in a university hospital setting and had a mean of 38 (SD 24.2) weekly NCD consultations , smoking , medication nonadherence , and unhealthy diet . Physicians ranked smoking first, medication nonadherence second, physical inactivity third, and unhealthy diet fourth in the list of most important health risk behaviors to address in the context of NCD management. The following health risk behaviors were perceived as the most prevalent among their patients: (1) physical inactivity , (2) unhealthy diet , and (3) difficulty in managing stress . On the basis of these results, we designed 4 core cases of the MC-CAT to feature cases with health risk behaviors—smoking, physical inactivity, nonadherence to medication, and poor diet—representing a range of NCDs  . The aveThe initial percentage of agreement between our classification and the experts’ for the rank order of responses across all 4 base cases was 60.9% . The competency identification agreement across all 4 base cases was 44.9% . In response to these results and considering the specific feedback provided by our international experts (74 comments over the 4 cases), we made 8 modifications to the rank ordering of statements and 23 modifications to aspects of the dialog  in a sessment . The phyOf the 27 responses received during user experience testing, 10 (37%) comments reflected audio and visual elements , and 19 (70%) comments reflected instruction elements . On the basis of this, the 29 comments were addressed by the research team and 42 Comets Inc when creating a new version of the MC-CAT platform (version 2.0).We received 24 MC-CAT responses from undergraduate allied HCP students. All participants had completed both assessments .To identify possible differences between the different versions of the base cases , ANOVAs were performed by comparing the competency and ranking scores. No significant differences were identified between the different versions of the case across the 2 measurement times .The MC-CAT tool showed acceptable values of internal consistency for global scores (25 items) at both time 1 (α=.78) and time 2 (α=.80).user-friendly tool for assessing communication competencies among HCPs in the context of changing health behaviors among patients with NCDs, conduct an initial internal validity assessment, and evaluate score consistency between the base and modified cases. The MC-CAT was designed to simulate clinical interactions with virtual patients to provide both global and specific scores for 11 core communication skills [The objectives of this study were to develop the MC-CAT, a new, web-based, n skills . It was The results of this 5-step mixed methods study indicate that the MC-CAT demonstrates acceptable levels of internal consistency for the global competency score (α=.78-.80), and little variance was found across different versions of the 4 base cases. This level of internal consistency is higher than the levels observed in many existing communication assessment tools such as the Pediatric Consultation Assessment Tool and the Four Habits Coding Scheme, which had Cronbach α values between .52  and .66 evocative questions, reflective listening, and expressing empathy) are given greater weight in the final scoring. This is an important strength of the tool, as we are aware of no existing tools that take into account the real-world frequency with which certain skills are used in their scoring algorithms. We also ensured that the different versions of our 4 base cases  were comparable in terms of competency and ranking scores. This means that our adaptations were consistent with the original scoring algorithm and that we can use them to create further adaptations of the 4 base cases to further extend the case bank of the tool.To our knowledge, this is the first study to describe the development of a web-based interactive communication skills assessment tool for HCPs . The MC-One of the most useful and attractive features of the MC-CAT is that it is scored automatically based on a preprogrammed algorithm, which eliminates the need for external raters to conduct assessments . The need for external, trained raters is a feature of all existing assessment tools . AlthougFirst, this study may be limited by the fact that we did not specifically include patients with NCDs as part of our stakeholder groups. The rationale was that our target users were HCPs; hence, our focus was on engaging various physicians, HCPs, and health administrator stakeholders. There is already an evidence base demonstrating that patients whose physicians use MC-type approaches feel more understood, have more trust in their providers, are more adherent to treatment, are more satisfied with their care, and have better outcomes ,34-38. Auser-friendly and intuitive. Another strength of our development process is that we created multiple versions of our base cases and tested whether they were comparable using a factorial analysis, which revealed no significant differences between cases on competency and ranking scores. This increases our confidence that the MC-CAT is now ready for comprehensive psychometric property analyses among target users , which is the next step in the development process.Despite these limitations, this study also has several notable strengths. Critical to successfully developing a valid and reliable assessment tool, we integrated several key stakeholder groups in all steps of the development process  using the knowledge transfer cycle as the basis of our iKT strategy . This isuser-friendliness are expected to increase the uptake and improve the quality of MC training programs designed to improve HCPs’ ability to effectively motivate and support patients to adopt healthy behaviors in the context of NCD prevention and management.The MC-CAT is a new web-based, interactive, user-friendly MC assessment tool that was codeveloped with a range of relevant stakeholders. The results demonstrated acceptable internal consistency for global competency scores, which indicates that it is ready for more comprehensive psychometric property analyses, including both internal and external validity tests  in a national sample of HCPs across disciplines. We will continue to use an iterative approach during subsequent phases of development, and we are prepared to further refine the tool and its scoring algorithm as needed. Once developed, the MC-CAT will be the first web-based MC assessment tool that can be easily and widely accessed by a variety of HCPs and can be used not only as an evaluation tool but also as an adjunct to the MC training programs. Its accessibility, convenience, and


As observed in the outbreaks of SARS and swine flu, as well as many other infectious diseases, the huge volume of human traffic across numerous enclosed public venues has posed immense challenges to preventing the spread of communicable diseases. There is an urgent need for effective disease surveillance management in public areas under pandemic outbreaks. The physicochemical properties associated with ionic liquids make them particularly suited for molecular communications in sensing networks where low throughput is quite adequate for pathogen detection. This paper presents a self-cognizant system for rapid diagnosis of infectious disease using a bionic sensor such that testing can be supported without collecting a fluid sample from a subject through any invasive methods. The system is implemented for testing the performance of the proposed bionic liquid sensing network. In recent years, the development of bionic sensing systems has led to the surge in application for infectious disease management . MoleculThe use of triazole synthesis from a combination of azides and carbonyl compounds has been used in ionic sensing . An earlThis paper is organized as follows: The preparation of ionic liquids commences with the Brønsted basicity principle across an air-water interface. This yields a thermally stable ionic liquid basic site with the cation as well as a basic anion . SensingAn alternative ionic liquid is [DBU][Ac] , and aromatic/aliphatic amines are a catalyst that can undergo chemical reaction under solvent-free condition at room temperature . One of The process of synthesizing organic moieties that contain heterocycles is highly dependent on the specific moieties' chemical properties . These oThe development of a noninvasive rapid diagnosis mechanism will entail new biological active molecules with appropriate therapeutic properties that are largely depending on the type of pathogen to be detected. More importantly, it must exhibit no toxicity to be used in any sensing system for diagnosis. Variants of heterocycles as well as 1,2,3-triazoles exhibit such potent biological activities for use as a base for ionic liquid sensing making them well suited to be used as therapeutic agents . Moreove1,2,3-Triazoles exhibit pharmacophore biological properties against allergy being particularly appropriate for the detection of pathogens like bacteria and viruses  with suc1,2,3-Triazoles are antiretroviral protease synthetic molecule inhibitors used in combination for diagnosis as well as treatment as triazole with moieties possesses potent biological activities like antiviral, antiepileptic, antiallergic, antimicrobial, anticancer, and antituberculosis . The parOne of the main challenges of utilizing 1,2,3-triazoles as a base is that these molecules do not occur naturally such that they need to be synthesized . The claChemical reactions carried out in water are an important requirement for diagnosis in the context of safety of test subjects.  proposesTo use water as solvent, carbohydrate azide and acetylenes are used to react under the catalyst of copper iodide (CuI), and different bionic liquids are screened for their reactions in order to carry out diagnosis tests as shown in Basic sensing is supported by copper-catalyzed azide-alkyne cycloaddition reaction (CuAAC) through click reactions in a facile through a plausible mechanism that yields the copper acetylide and dinuclear complex as shown in For the synthesis of 1,4-disubstituted triazoles from CuAAC reaction, the molecular sensing network relies on ionic liquid [Bmim]OH (1-methyl-3-butyl-imidazolium hydroxide) used in conjunction with CuI only, thereby eliminating the use of any base or reducing agent. Other additives are needed to serve two main purposes, namely, a reduction to take place within the reaction time duration and production of a cleaner liquid to enter the test subject's digestive system, since it is likely that a small amount of the substance will be swallowed. With both aromatic azide and aromatic terminal alkyne, it typically takes approximately 0.5 h reaction time for the product yield to reach 90% as shown in One of the major problems of relying on regioselective synthesis of 1,2,3-triazoles as a molecular sensing medium is due to copper's cytotoxic property being a limiting factor for its broader medical applications . We ther3N or K2CO3. The reaction commences with initial formation of 1,3-dipolar cycloaddition of enolate ion under the presence of [bmim]OH. This reaction yields 1,4,5-trisubstituted 1,2,3-triazole through the elimination of water.Based on recent development by Gaetke and Chow , they re4NOH as a hydrated ionic liquid catalyst for its ease of availability and nonmetallic, nontoxic, and nonvolatile properties so that it can be used both efficiently and safely [The regioselectivity is resulted by the azide's selective approach of positive terminal to the electron-rich part of the 1,3-dipolar cycloaddition's enolate ions . We seled safely .Through utilizing the molecular sensing mechanism described above, rapid diagnosis can be supported by the use of a simple self-cognizant set of biosensor nodes that returns a very simple binary test result of either positive (presence of pathogen under test) or negative (absence of pathogen under test). Further, measurement of the location of each sensor node can provide insights into the concentration of pathogen, which can be particularly useful in the detection of asymptomatic carriers . Each noEo.Initially, a simple sensing network is set up with a cluster controller being selected in a distributed manner . The netThe node that is selected as the cluster controller broadcasts a message across the entire network indicating that it is the controller of a specific cluster. Other nodes within the cluster will then make direct connection to this particular cluster controller. Clusters are formed on the basis of received signal strength of the received broadcast message from the cluster controller. An active node initiates a request to the nearest cluster controller when being grouped as within the same cluster. This effectively works as the Carrier Sense Multiple Access (CSMA) MAC protocol for collision avoidance . The cluEo = 50 J. We base our evaluation on the Packet Transmission Ratio (PTR) that measures the ratio of redundant packets sent to the cluster controller from active node+to sink via the cluster controller so long as the network is active, i.e., as controlled by chemical reaction. The network active time performance is shown in To evaluate the bionic sensing system performance, we carry out simulation for up to 100 nodes with an equal initial energy level of Eo, is the highest among the three possible implementations. CuAAC offers an improved overhead. The 1,4-disubstited 1,2,3-triazoles are obtained by reacting alkyl bromide, sodium azide, and terminal alkyne with a comparable 90% yield. The active methylene compound was used while the number of supported cluster controllers was reduced. The use of an appropriate clustering algorithm can save energy among individual nodes that consequently support the highest number of rounds. Cu(I) supports a higher maximum number of nodes acting as cluster controllers due to its longer response time. Consequently, the total energy, namely, the sum of Finally, we test the prototype using the BioSD architecture proposed by Hussain et al.  as the cThis paper discusses the design of a molecular bionic sensor based on the synthesis of 1,4,5-trisubstituted-1,2,3-triazoles using various ionic liquids as catalyst as well as reaction medium without the need of an external base. The use of azide-alkyne cycloaddition and cycloaddition of azides with active methylene compounds is particularly suited to serve as a bionic sensing system for rapid diagnosis in the presence of selected pathogens, owing to its nonmetallic and nontoxic base properties with short reaction time and adequate yields.