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Using augmented reality to enhance wax up training in dental education a feasibility study

Abstract Wax-up practice is an integral part of dental education, teaching students the morphology of teeth and correct handling of waxing tools. Currently wax-ups need to be presented and evaluated by a dentist involved in dental education. The aim of this study was the development of a smartphone application utilizing augmented reality to improve self-evaluation and learning effects of beginners doing wax-up practice. The application was developed for devices running the iOS operating-system using the Swift language. The Swift-libraries UIKit, ArKit and SceneKit were used. The application was test-run on an iPhone 14 Pro, using an iOS developer account. An integral component of the application is the use of 3D-printed wax-up templates, which were manufactured with FDM printers. The resulting smartphone application enables dental students and teaching dentists to inspect the wax-up template using the device’s camera to view the optimal wax-up overlaying the template. Different cusps can be displayed, hidden or altered in their transparency to further improve feedback on the students’ practice. The aim of creating a supportive application for wax-up practice as a proof of concept was fulfilled and even exceeded the initial expectations regarding the accuracy and consistency of object tracking in the augmented reality. The acceptance amongst students and teaching staff, as well as a comparison against traditional methods of teaching wax-up remain to be evaluated.

Pregnancies, intentions, and fertility behaviors during use of the Creighton Model FertilityCare System after initial intention to avoid pregnancy: Results from the Creighton Model effectiveness, intentions, behaviors assessment study

Background Knowledge of the fertile and infertile phases of the menstrual cycle can be applied to conceive or to avoid pregnancy. Fertility intentions and sexual behaviors during the fertile time may influence whether and when pregnancy occurs. The Creighton Model FertilityCare System (CrMS) is a specific system of fertility appreciation used to conceive or to avoid pregnancy. The objective of this paper is to report intentions, behaviors, and pregnancy rates during use of the CrMS among couples who initially intended to avoid pregnancy. Data and methods We analyzed a prospective cohort study conducted in 17 CrMS centers across the USA and Canada, following 296 couples for up to one year after onset of initial use of the CrMS to avoid pregnancy. Baseline data included demographics, motivations, and pregnancy intentions for each partner. Couples contributed 2894 menstrual cycles, most of which had data collected (by questionnaires and daily diary) on cycle-specific pregnancy intentions, days of potential fertility, and fertility behaviors. Pregnancies were prospectively actively ascertained. Results We found a high concordance (91%) in cycle pregnancy intentions between partners. However, 44% of cycles with strong intentions to avoid pregnancy included intercourse on potentially fertile days or days of undetermined fertility status. Across all sensitivity scenarios, cumulative 13-cycle pregnancy rates with cycle intention to conceive ranged from 88.0% to 89.8%, and cumulative 13-cycle pregnancy rates with cycle intention to avoid ranged from 29.1% to 35.3%. In multivariate analysis, baseline motivations and intentions for pregnancy within 2 years were strongly correlated with the likelihood of pregnancy, more so than cycle intentions. Conclusion The findings suggest that in some populations using natural family planning, baseline motivations and intentions may be more strongly related to pregnancy rates than cycle intentions. Our findings also highlight essential elements for evaluating correct use, including complete recording of intercourse and its timing.

A novel encrypted traffic detection model based on detachable convolutional GCN-LSTM

Abstract With the widespread adoption of network encryption technologies, traditional detection methods increasingly struggle to identify malicious encrypted traffic due to their limited ability to capture structural and behavioral characteristics. To address this issue, this paper proposes a Detachable Convolutional GCN-LSTM (DC-GL) model. The proposed model constructs graph-structured data by integrating protocol-layer features and traffic statistical features extracted from encrypted flows. A Graph Convolutional Network (GCN) is employed to capture structural dependencies among nodes, while a Long Short-Term Memory (LSTM) network models the temporal dynamics of traffic behavior. To improve computational efficiency and feature extraction performance, detachable convolution is introduced into the GCN layers. In addition, an attention mechanism is incorporated to enhance the representation of critical features. Experimental results demonstrate that the DC-GL model outperforms several mainstream approaches in terms of accuracy, recall, and other key metrics, while also exhibiting faster convergence and greater robustness. These results suggest that DC-GL offers an effective and promising approach for malicious encrypted traffic detection.

Tailoring oral targeted therapies dosage in lung cancer: A systematic review of pharmacokinetics studies on renal and hepatic impairment

Background Lung cancer is the leading cause of cancer-related deaths worldwide, and stage IV lung cancer is frequently managed with targeted therapy. Renal and hepatic impairment frequently coexist with cancer, often requiring a reduction in targeted therapy dosage. This systematic review assesses the appropriateness of current targeted therapy dosage adjustments in individuals with hepatic and renal impairment by comparing package insert recommendations with available pharmacokinetic studies. Methods We reviewed the most recent guidelines from the National Comprehensive Cancer Network (NCCN) on the use of non-monoclonal antibody targeted therapy. We also examined all package inserts for information on dose adjustment in cases of hepatic and renal impairment. We then systematically searched for studies that involved pharmacokinetic analysis in populations with hepatic or renal impairment, as well as those undergoing hemodialysis and peritoneal dialysis. Results We identified 44 studies from 21 oral lung cancer therapies that met the inclusion criteria. We developed 13 new recommendations and updated 7 existing ones regarding targeted therapy dose adjustment in cases of hepatic and renal impairment compared to the information provided in the package insert. Several drugs have not published their pharmacokinetic results in a scientific journal, which limits access to their appropriateness. Moreover, there is a lack of research on pharmacokinetic analysis of targeted therapy in patients undergoing hemodialysis and peritoneal dialysis. Conclusions Adjusting the dosage of targeted therapy in hepatic and renal impairment based on pharmacokinetic analysis is essential to broaden the usage, improve effectiveness, and minimize side effects. Further pharmacokinetic research on the usage in unstudied populations is strongly advised. Prospero registration number CRD42024518123.

Understanding food loss patterns across developed and developing countries using a GDP, growth rate, and health expenditure-based typology

Abstract Food loss and waste (FLW) threaten progress toward Sustainable Development Goals (SDG) 12.3, yet their distribution by development stage remains under-quantified. We created a time-weighted K-means typology for 105 countries (2000–2022) using Gross Domestic Product (GDP) per capita, GDP growth, and per-capita health expenditure—indicators chosen to capture economic capacity, growth momentum, and institutional investment. The scheme classified nations as developed (n = 13), developing (n = 92), or hybrid, with > 98% membership stability across weighting parameters. Linking this typology with FAO’s FLW data, we modelled food loss percentages (FLP) across ten commodity groups and eight supply-chain stages using multilevel mixed-effects regression. Developed countries lost the most food at consumption (22.5%), dwarfing developing (6.8%) and hybrid cases (9.0–14.2%), whereas developing nations suffered greater upstream losses at harvest/on-farm (3.7%). FLP in developing economies was significantly lower for grains (β = − 8.02, p = 0.007), oilseeds (β = − 19.29, p = 0.016) and pulses (β = − 5.43, p = 0.021). From 2000 to 2022, oilseed and sugar losses rose (β = 0.26, p < 0.001), while roots/tubers and dairy/eggs declined (β = − 0.31, − 0.89; p < 0.01). Stage analyses revealed pronounced development gaps at consumption (β = − 16.06, p < 0.001) and processing (β = − 5.58, p = 0.014), alongside a rising trend in marketing/retail losses (β = 0.25, p = 0.005). Country-level random effects explained up to 90% of variance, underscoring the dominance of local conditions. The evidence supports consumer-behaviour interventions in high-income settings, upstream infrastructure investment in developing regions, and dual-track strategies in hybrids. Our typology provides a scalable, policy-ready lens for designing targeted FLW actions aligned with SDG 12.3.

The impact of maternal versus paternal imprisonment on their children’s health: A scoping review

Background Rates of imprisonment for both women and men are high in England and Wales yet no official records report the number of people in prisons who are parents. Reports suggested 54% of people in prison have children under the age of 18 years which is estimated to affect 312,000 children annually. Research has examined the impact of parental imprisonment on their children, but little is known about the health and wellbeing outcomes for children who experience maternal versus paternal imprisonment. The Prison Reform Trust reported only 9% of children live with their father at the time of their mother’s incarceration, whilst 75% of children live with their mother at their father’s incarceration. The aim of this scoping review was to review the published evidence about the health impacts of maternal versus paternal imprisonment to enable a better understanding of the differential impacts on affected children and to identify where gaps in the evidence remain. Methods The Arksey and O’Malley methodology for scoping review was used to address how do the physical, mental and behavioural health, along with healthcare service use differ between children who experience their mother being imprisoned, compared to those who experience their father being imprisoned. Databases searched included Medline, Embase, CINAHL, Cochrane, PyschINFO, Web of Science, Delphis and IBBS. The search yielded 9,773 results, which after screening and removal of duplicates, resulted in 20 papers being included. Results All included papers compared data relating to outcomes for children who had experienced maternal or paternal imprisonment to children with no parental imprisonment, and three compared maternal to paternal imprisonment. Eighteen used populations in the United States of America and of these, thirteen used data from two studies. Having experienced either parent being in prison results in considerable impacts on the health of children, as well as their support networks and the stigma they encounter. The findings comprised of four main categories of health: physical health, mental health, behavioural health and healthcare service use. Discussion This review highlighted how atomised the study designs and study populations were in addition to the varied findings about the impact of maternal and paternal imprisonment on children. The sparsity of literature resulted in challenges addressing the original study question about how health and wellbeing outcomes differ for children experiencing maternal versus paternal imprisonment and no clear conclusions can be drawn. Conclusion There is limited understanding about the impact of maternal or paternal imprisonment on their children’s health and behaviour, despite the substantial implications their imprisonment has and the stigma. It is important to consider that the absence of clear significant findings, does not negate the great health needs for this cohort. Further research is vital to ensure this population is identified, recognised and supported appropriately.

Transfer learning prediction of type 2 diabetes with unpaired clinical and genetic data

Editorial Note: Sanitation facilities, hygienic conditions, and prevalence of acute diarrhea among under-five children in slums of Addis Ababa, Ethiopia: Baseline survey of a longitudinal study

The significance of adding posterior decompression to spine stabilization in metastatic spinal surgery: a multicenter prospective study

Viral etiology of severe acute respiratory infections in hospitalized patients, Shandong, China

Background Severe Acute Respiratory Infection (SARI) represents a critical global public health challenge, accounting for substantial severe morbidity and hospitalization burdens with distinct geographical patterns in etiological profiles. This study systematically characterizes the epidemiological and clinical phenotypes of SARI patients in Shouguang county, Shandong Province, China. Methods A prospective observational study was conducted at Shouguang People’s Hospital between August 28, 2023 and April 30, 2024, enrolling 1,730 hospitalized patients with SARI from the Departments of Infectious Diseases and Respiratory and Critical Care Medicine. Standardized electronic case report forms were used to systematically collect the demographic characteristics, clinical manifestations and laboratory testing results. Oropharyngeal swab specimens were collected within 24 hours of admission for each patient and stored at −80°C. Multiplex real-time quantitative PCR (RT-qPCR) was performed using the ABI 7500 system to detect 11 respiratory viruses infection, including influenza A virus (IFA), influenza B virus (IFB), respiratory syncytial virus (RSV), parainfluenza virus (HPIV), human coronaviruses (HCoV), human metapneumovirus (HMPV), rhinovirus (HRV), enterovirus (EV), human bocavirus (HBoV), human adenovirus (HAdV), and SARS-CoV-2 (COVID-19). Results 501 samples (28.96%) were tested positive for at least one virus. The most frequently detected viruses and their infection rates were as follows: IFA (11.33%), COVID-19 (6.53%), HPIV (2.31%), HCoV (2.20%), RSV (1.79%), IFB (1.68%), HMPV (1.56%), EV (0.64%), HADV (0.52%), and HBoV (0.06%). Among patients aged 0–14 years, IFA and EV had the highest infection rates, both at 9.46% (7/74). In the 15–24 age group, IFA exhibited the highest infection rate at 19.70% (26/132). In patients aged ≥70 years, COVID-19 was the most frequently detected virus, with a infection rate of 10.69% (65/608). The overall virus infection rate peaked at 60.00% (30/50) in epidemiological week 48 of 2023. During weeks 46–50 of 2023, the overall infection rate remained consistently high (range: 28.42–60.00%). Significant differences in infection rates were observed across hospital departments (χ² = 5.52, P < 0.05), The Department of Infectious Diseases demonstrated a higher infection rate of 34.91% (162/464) compared to 29.07% (368/1266) in the Department of Respiratory Medicine. Conclusion Viral etiological analysis of SARI patients in Eastern China identified IFA, COVID-19, and HPIV as the three predominant virus, with influenza virus exhibiting the highest frequency of co-infection with other respiratory viruses. Our study further revealed significant heterogeneity in virus distribution across different hospital departments, age groups, and admission periods. The most common clinical manifestations were cough and fever, with distinct symptomatic profiles observed among infections caused by different viruss. These findings provide scientific evidence to inform government strategies for optimizing the prevention and management of respiratory infectious diseases.

Analysis of the decomposition of an anhydride-cured epoxy resin by subcritical hydrolysis

Abstract The decomposition of an anhydride-cured epoxy resin by subcritical hydrolysis is studied under variation of reaction temperature, decomposition duration and water volume using a batch reactor. Within the framework of a design of experiment, the process is evaluated by the gravimetric decomposition of the epoxy resin clearly showing that temperature is the most important factor. Duration and water volume have only smaller positive effects. The mass loss mechanism of the cube-shaped specimens corresponds to a heterogeneous surface degradation where material is only lost from the outer areas. The determination of glass transition temperatures after the experiments demonstrates that the water hardly affects the center of the epoxy cubes. Therefore, a core-shrinking model is successfully applied for analysis of the decomposition kinetics. The product distribution in the aqueous phase reveals a large number of organic compounds. Higher temperatures, longer durations and lower water volumes lead to an increase in decomposition product complexity and concentrations, but the degradation pathways are apparently not affected.

Differentiation of COVID-19 from other types of viral pneumonia and severity scoring on baseline chest radiographs: Comparison of deep learning with multi-reader evaluation

Chest X-ray (CXR) imaging plays a pivotal role in the diagnosis and prognosis of viral pneumonia. However, distinguishing COVID-19 CXRs from other viral infections remains challenging due to highly similar radiographic features. Most existing deep learning (DL) models focus on differentiating COVID-19 from community-acquired pneumonia (CAP) rather than other viral pneumonias and often overlook baseline CXRs, missing the critical window for early detection and intervention. Moreover, manual severity scoring of COVID-19 CXRs by radiologists is subjective and time-intensive, highlighting the need for automated systems. This study introduces a DL system for distinguishing COVID-19 from other viral pneumonias on baseline CXRs acquired within three days of PCR testing, and for automated severity scoring of COVID-19 CXRs. The system was developed using a dataset of 2,547 patients (808 COVID-19, 936 non-COVID viral pneumonia, and 803 normal cases) and validated externally on several publicly accessible datasets. Compared to four experienced radiologists, the model achieved higher diagnostic accuracy (76.4% vs. 71.8%) and enhanced COVID-19 identification (F1-score: 74.1% vs. 61.3%), with an AUC of 93% for distinguishing between viral pneumonia and normal cases, and 89.8% for differentiating COVID-19 from other viral pneumonias. The severity-scoring module exhibited a high Pearson correlation of 93% and a low mean absolute error (MAE) of 2.35 compared to the radiologists’ consensus. External validation on independent public datasets confirmed the model’s generalizability. Subgroup analyses stratified by patient age, sex, and severity levels further demonstrated consistent performance, supporting the system’s robustness across diverse clinical populations. These findings suggest that the proposed DL system could assist radiologists in the early diagnosis and severity assessment of COVID-19 from baseline CXRs, particularly in resource-limited settings.

Comprehensive analysis of pachyvessel morphology in central serous chorioretinopathy using multimodal imaging

Mapping the path to domestic surrogacy: Identifying key facilitators and barriers in the Netherlands

Background Surrogacy involves a woman who consents, before conception, to carry and deliver a child for individuals or couples unable to do so due to biological or medical limitations. This complex process encompasses medical, ethical, legal and financial considerations, resulting in varied legislation worldwide, with countries either prohibiting, restricting or legalising it. Recently, several nations have revised their legislation to encourage domestic surrogacy over international options, driven by ethical considerations and legal concerns. However, these revisions are still pending enactment. Despite the extensive literature addressing the legal, ethical, societal and medical challenges and benefits of surrogacy, no study has comprehensively analysed these factors together to fully capture the complexity of surrogacy implementation. This study aims to identify the key elements that currently facilitate the implementation of domestic surrogacy in the Netherlands and those essential elements needed for its successful continuation. Methods A qualitative case study was conducted, employing both interviews and document analysis. The selection targeted individuals who were directly involved in or had an informed perspective on handling surrogacy in the Netherlands, including healthcare professionals, healthcare system leaders, policymakers, non-governmental organisations (NGOs), academics, lawyers and counsellors and 14 experts were purposively selected. The data were analysed both inductively and deductively, using the Context and Implementation of Complex Interventions (CICI) framework to assess the contextual factors influencing the implementation of domestic surrogacy. Results Four CICI domains were identified as most influential on the implementation of surrogacy: legal (allowance of altruistic gestational surrogacy but missing legal framework on legal parentage, advertisement and payment), political (political shifts and experts’ influence, gatekeepers, intersectional collaborations), ethical (professionals’ influence on patient’s choice) and socio-cultural (donation culture and public opinion). The absence of a legal framework that secures legal parenthood, the limited availability of fertility services and the shortage of surrogate candidates represent key barriers to the implementation of domestic surrogacy in the Netherlands. Conversely, significant facilitators include extensive, well-organised collaboration between professionals and non-governmental organisations (NGOs), invited by the political system to share expert knowledge and support comprehensive legislation. Conclusion In conclusion, despite the progress achieved, domestic surrogacy remains largely inaccessible to most infertile individuals and is yet to be fully adopted. Without legal reforms, the situation of surrogacy in the Netherlands is likely to remain unchanged, mirroring the experiences of other countries with pending surrogacy legislation.

Slow dynamics of human balance control

Abstract When standing on a tilting surface, humans’ sway behavior at frequencies below 0.1 Hz indicates the contribution of a slow feedback component. We suggest this may reflect a self-calibration mechanism of the balance control system, constantly referencing orientation estimates based on kinematic sensory cues to a reference based on force cues. However, attempts to identify this mechanism have been limited by insufficient experimental trial durations and small sample sizes. This study aimed to assess the properties of the mechanism that reduces body sway at very low frequencies in upright standing. Anteroposterior body sway responses to short- and long-duration surface tilts were measured and interpreted using balance control models. Four feedback control model variants, with different mechanisms to account for the slow dynamics, were fit to experimental data. Furthermore, we tested how estimates of the slow component are affected by stimulus period duration. We hypothesized that the model variants containing force cues would provide the best fit to experimental sway responses, particularly in response to long-duration surface tilts. Our results confirm this hypothesis and suggest that humans use integrated force afferents from the feet and legs in a slow, positive feedback mechanism during standing to remain upright. Despite stimulus period durations of ~ 180 s, some properties of this mechanism were difficult to estimate. The positive torque feedback mechanism aligns with the notion of self-calibration.

Relative importance of socioecological domains to predicting opioid-involved mortality

Background The opioid crisis in the United States is a complex issue with interconnected factors that lead to opioid misuse and opioid-involved mortality. This study assessed the relative importance of different risk factor domains in predicting fatal opioid-involved mortality that occurred after hospital encounters involving opioids. Methods A machine learning model was developed by integrating multiple data sources, including hospital records, death records, and societal data. The model allowed simultaneous examination of risk factors across individual drug and non-drug related factors, hospital factors, and societal factors. Results 429,005 patients with opioid-related encounters in 2014 were assessed, where 56.6% were female and the mean age was 44.98. Among deaths that had specific drugs listed for both the hospital encounter and the death, 51.7% of hospital encounters progressed to a more potent opioid at death. Community factors cumulatively had similar importance as individual drug-related factors in predicting opioid-involved deaths and were relatively more important in predicting opioid-involved mortality compared to non-drug involved mortality. In predicting opioid-involved mortality, non-drug related individual-level predictors accounted for 45.1% of the importance. Community factors accounted for 27.9% of the importance and drug-related individual factors accounted for 22.5%. In contrast, community factors accounted for only 16.5% of the importance when predicting non-opioid-involved mortality. Practice Implications Rather than suggesting community factors outweigh individual factors, our results highlight individual vulnerability may be amplified or mitigated by broader environmental factors. Interventions targeting larger social determinants of health may be strongly influential in reducing drug-involved mortality. This study demonstrated a quantitative evaluation of the different domains of risk factors and highlighted the importance of considering societal and community factors in a holistic approach to preventing opioid-involved mortality.

Cytogenetic landscape aberrations in paediatric acute lymphoblastic leukaemia — a polish paediatric population treated according to ALL-IC BFM 2009 protocol

Expression of Concern: Determinants of change in timely first antenatal booking among pregnant women in Ethiopia: A decomposition analysis

Shape optimization and mechanical properties analysis of the free-form surface

Communication-efficient decentralized clustering for dynamical multi-agent systems

The paper presents a decentralized, real-time clustering method designed for large-scale, distributed environments such as the Internet of Things (IoT). The approach combines compressed sensing for dimensionality reduction with a consensus protocol for distributed aggregation, enabling each node to generate compact, consistent summaries of the system’s clustering structure with minimal communication overhead. These representations are processed by a pre-trained neural network to reconstruct the global clustering state entirely without centralized coordination. Unlike traditional methods that depend on static topologies and centralized computation, this system adapts to dynamic network changes and supports on-the-fly processing. The system suits IoT applications where data must be processed locally, and immediate results are essential. Experiments on both synthetic and real-world datasets show that the method significantly outperforms baseline approaches in clustering accuracy, making it highly suitable for resource-limited, decentralized IoT scenarios.