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Aims: The aims of this register study were: 1. To study whether the type of ownership of the dental practice was correlated with the type of dental care provided, that is public versus private ownership and professional (dentist or dental hygienist) versus non-professional ownership. 2. To study the extent of follow-up of patients who have undergone two types of treatments. Material and method: Two types of dental care were defined in the two groups studied, periodontitis/peri-implantitis and comprehensive restorative/rehabilitation. All relevant treatment codes that fall under these definitions are noted when they are performed. Also, the follow-up of each treatment code is noted. Differences in dental and socioeconomic status over time and between regions were adjusted for. A drop-out analysis was performed. Results: Dental practices owned by dentists or dental hygienists schedule follow-up appointments for patients who have undergone comprehensive restorative or rehabilitation dentistry more often than practices with other types of ownership. Dental practices owned by dentists or dental hygienists follow up patients with periodontitis and peri-implantitis less frequently. Conclusion: Type of ownership of a dental business influences the extent to which periodontal, and comprehensive restorative or rehabilitation dentistry were followed up.

There are prominent sex/gender differences in the prevalence, expression, and life span course of mental health and neurodiverse conditions. However, the underlying sex- and gender-related mechanisms and their interactions are still not fully understood. This lack of knowledge has harmful consequences for those with mental health problems. Therefore, we set up a cocreation session in a 1-week workshop with a multidisciplinary team of 25 researchers, clinicians, and policy makers to identify the main barriers in sex and gender research in the neuroscience of mental health. Based on this work, here we provide recommendations for methodologies, translational research, and stakeholder involvement. These include guidelines for recording, reporting, analysis beyond binary groups, and open science. Improved understanding of sex- and gender-related mechanisms in neuroscience may benefit public health because this is an important step toward precision medicine and may function as an archetype for studying diversity.

Abstract Background Internalizing symptoms are among the most common psychological symptoms in childhood and adolescence, are highly stable and can cause severe impairment. Current research discusses lower capacities of self-regulation (SR) as risk factors for the development of internalizing symptoms. The present study identifies trajectories of internalizing symptoms in the transition phase from middle childhood to adolescence and examines multiple SR facets as predictors of potentially unfavorable trajectories, also in the presence of other established risk factors. Methods The study utilized a community sample of N = 1453 (52.2% female) German children, who provided data at up to three measurement points (t1: 6–11 years, t2: 7–11 years, t3: 9–13 years). Trajectories of internalizing symptoms were based on parents’ ratings of the emotional problems scale of the Strengths and Difficulties Questionnaire. SR facets were assessed using multiple methods and informants. Two multinomial regression analyses were conducted to predict class membership by (1) SR facets and gender and (2) SR facets, gender, and other established risk factors (education status, family adversity, peer problems). Results Using growth mixture modelling, we identified three trajectory classes with stable low (n = 1200), increasing (n = 124), and early high decreasing internalizing symptoms (n = 129). In the regression analysis controlling for risk factors, membership in the increasing trajectory was significantly predicted by higher emotional reactivity (OR = 2.65, p

Cochlear implants (CIs) restore activity in the deafened auditory system via electrical stimulation of the auditory nerve. As the spread of electric current in biological tissues is rather broad, the spectral information provided by electrical CIs is limited. Optogenetic stimulation of the auditory nerve has been suggested for artificial sound coding with improved spectral selectivity, as light can be conveniently confined in space. Yet, the foundations for optogenetic sound coding strategies remain to be established. Here, we parametrized stimulus-response-relationships of the auditory pathway in gerbils for optogenetic stimulation. Upon activation of the auditory pathway by waveguide-based optogenetic stimulation of the spiral ganglion, we recorded neuronal activity of the auditory midbrain, in which neural representations of spectral, temporal, and intensity information can be found. Screening a wide range of optical stimuli and taking the properties of optical CI emitters into account, we aimed to optimize stimulus paradigms for potent and energy-efficient activation of the auditory pathway. We report that efficient optogenetic coding builds on neural integration of millisecond stimuli built from microsecond light pulses, which optimally accommodate power-efficient laser diode operation. Moreover, we performed an activity-level-dependent comparison of optogenetic and acoustic stimulation in order to estimate the dynamic range and the maximal stimulation intensity amenable to single channel optogenetic sound encoding, and indicate that it complies well with speech comprehension in a typical conversation (65 dB). Our results provide a first framework for the development of coding strategies for future optogenetic hearing restoration.

BackgroundIn this paper, we argue for Gender as a Sociocultural Variable (GASV) as a complement to Sex as a Biological Variable (SABV). Sex (biology) and gender (sociocultural behaviors and attitudes) interact to influence health and disease processes across the lifespan-which is currently playing out in the COVID-19 pandemic. This study develops a gender assessment tool-the Stanford Gender-Related Variables for Health Research-for use in clinical and population research, including large-scale health surveys involving diverse Western populations. While analyzing sex as a biological variable is widely mandated, gender as a sociocultural variable is not, largely because the field lacks quantitative tools for analyzing the influence of gender on health outcomes.MethodsWe conducted a comprehensive review of English-language measures of gender from 1975 to 2015 to identify variables across three domains: gender norms, gender-related traits, and gender relations. This yielded 11 variables tested with 44 items in three US cross-sectional survey populations: two internet-based (N = 2051; N = 2135) and a patient-research registry (N = 489), conducted between May 2017 and January 2018.ResultsExploratory and confirmatory factor analyses reduced 11 constructs to 7 gender-related variables: caregiver strain, work strain, independence, risk-taking, emotional intelligence, social support, and discrimination. Regression analyses, adjusted for age, ethnicity, income, education, sex assigned at birth, and self-reported gender identity, identified associations between these gender-related variables and self-rated general health, physical and mental health, and health-risk behaviors.ConclusionOur new instrument represents an important step toward developing more comprehensive and precise survey-based measures of gender in relation to health. Our questionnaire is designed to shed light on how specific gender-related behaviors and attitudes contribute to health and disease processes, irrespective of-or in addition to-biological sex and self-reported gender identity. Use of these gender-related variables in experimental studies, such as clinical trials, may also help us understand if gender factors play an important role as treatment-effect modifiers and would thus need to be further considered in treatment decision-making.

Type 2 diabetes mellitus (T2DM) is one of the most common metabolic diseases in the world and poses a significant public health challenge. Early detection and management of this metabolic disorder is crucial to prevent complications and improve outcomes. This paper aims to find core differences in male and female markers to detect T2DM by their clinic and anthropometric features, seeking out ranges in potential biomarkers identified to provide useful information as a pre-diagnostic tool whie excluding glucose-related biomarkers using machine learning (ML) models. We used a dataset containing clinical and anthropometric variables from patients diagnosed with T2DM and patients without TD2M as control. We applied feature selection with three different techniques to identify relevant biomarker models: an improved recursive feature elimination (RFE) evaluating each set from all the features to one feature with the Akaike information criterion (AIC) to find optimal outputs; Least Absolute Shrinkage and Selection Operator (LASSO) with glmnet; and Genetic Algorithms (GA) with GALGO and forward selection (FS) applied to GALGO output. We then used these for comparison with the AIC to measure the performance of each technique and collect the optimal set of global features. Then, an implementation and comparison of five different ML models was carried out to identify the most accurate and interpretable one, considering the following models: logistic regression (LR), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbors (KNN), and nearest centroid (Nearcent). The models were then combined in an ensemble to provide a more robust approximation. The results showed that potential biomarkers such as systolic blood pressure (SBP) and triglycerides are together significantly associated with T2DM. This approach also identified triglycerides, cholesterol, and diastolic blood pressure as biomarkers with differences between male and female actors that have not been previously reported in the literature. The most accurate ML model was selection with RFE and random forest (RF) as the estimator improved with the AIC, which achieved an accuracy of 0.8820. In conclusion, this study demonstrates the potential of ML models in identifying potential biomarkers for early detection of T2DM, excluding glucose-related biomarkers as well as differences between male and female anthropometric and clinic profiles. These findings may help to improve early detection and management of the T2DM by accounting for differences between male and female subjects in terms of anthropometric and clinic profiles, potentially reducing healthcare costs and improving personalized patient attention. Further research is needed to validate these potential biomarkers ranges in other populations and clinical settings.

Anacardic acid (AnAc) inhibits the growth of estrogen receptor α (ERα)-positive MCF-7 breast cancer (BC) cells and MDA-MB-231 triple-negative BC (TNBC) cells, without affecting primary breast epithelial cells. RNA sequencing (seq) and network analysis of AnAc-treated MCF-7 and MDA-MB-231 cells suggested that AnAc inhibited lipid biosynthesis and increased endoplasmic reticulum stress. To investigate the impact of AnAc on cellular metabolism, a comprehensive untargeted metabolomics analysis was performed in five independent replicates of control versus AnAc-treated MCF-7 and MDA-MB-231 cells and additional TNBC cell lines: MDA-MB-468, BT-20, and HCC1806. An analysis of the global metabolome identified key metabolic differences between control and AnAc-treated within each BC cell line and between MCF-7 and the TNBC cell lines as well as metabolic diversity among the four TNBC cell lines, reflecting TNBC heterogeneity. AnAc-regulated metabolites were involved in alanine, aspartate, glutamate, and glutathione metabolism; the pentose phosphate pathway; and the citric acid cycle. Integration of the transcriptome and metabolome data for MCF-7 and MDA-MB-231 identified Signal transduction: mTORC1 downstream signaling in both cell lines and additional cell-specific pathways. Together, these data suggest that AnAc treatment differentially alters multiple pools of cellular building blocks, nutrients, and transcripts resulting in reduced BC cell viability.

Abstract Background Children with the most severe parental mental health conditions have an elevated risk of numerous adversities including somatic morbidity. However, there is no knowledge concerning physical health in most children affected by parental mental health conditions. Therefore, the aim was to examine the association between different severities of parental mental health conditions and somatic morbidity in children of different age-groups and further explore the combinations of maternal and paternal mental health conditions on child somatic morbidity. Methods In this register-based cohort study, we included all children born in Denmark between 2000–2016 and linked parents. Parental mental health conditions were categorised into four severity groups (no, minor, moderate, and severe). Somatic morbidity in offspring was categorised into broad disease categories corresponding to the International Classification of Diseases. We estimated the risk ratio (RR) using Poisson regression, of the first registered diagnosis in different age-groups. Results Of the around 1 million children in the study > 14.5% were exposed to minor parental mental health conditions and

To mitigate ongoing insect biodiversity declines, there is a need for efficient yet accurate monitoring methods. The use of traditional catch-based survey methods is constrained both by costs and need for expertise for manual taxonomic identification. Emerging methods, such as eDNA and robotic sorting, have the potential to reduce workload but still require resource-intensive sample collection in the field. Recently, remote sensing methods such as photonic sensors have shown promise for recording large numbers of insect observations. However, accurately determining species composition in collected data remains a challenge.In this study, we investigated the potential of photonic sensors for quantifying species richness of flying insects in the field and at five sites and compared the results with estimates based on conventional Malaise traps. Firstly, we evaluated two unsupervised clustering methods using a library of measured insect signals from 42 known species. Secondly, we correlated estimated number of clusters in data recorded at five sites with species richness assessment of catches from Malaise traps. This study is based on 84,770 library- and 238,584 field individual insect recordings. Our results demonstrate that both clustering methods perform well and reflect estimates obtained by Malaise traps, indicating the potential of automated insect biodiversity monitoring. This offers the possibility of more efficient but still accurate methods for studying insect biodiversity with broader temporal and spatial coverage.

Abstract A series of epidemiological studies have shown the limited life expectancy of patients suffering from idiopathic normal pressure hydrocephalus (iNPH). In most cases, comorbid medical conditions are the cause of death, rather than iNPH. Though it has also been shown that shunting improves both life quality and lifetime. We sought to investigate the utility of the Charlson comorbidity index (CCI) for improved preoperative risk–benefit assessment of shunt surgery in individual iNPH cases. 208 shunted iNPH cases were prospectively investigated. Two in-person follow up visits at 3 and 12 months assessed postoperative clinical status. The correlation of the age adjusted CCI with survival was investigated over the median observation time of 2.37 years (IQR 1.16–4.15). Kaplan Meier statistics revealed that patients with a CCI score of 0–5 have a 5-year survival rate of 87%, compared to only 55% in patients with CCI > 5. Cox multivariate statistics revealed that the CCI was an independent predictor of survival, while common preoperative iNPH scores (modified Rankin Scale (mRS), gait score, and continence score) are not. As expected, mRS, gait, and continence scores improved during the postoperative follow up period, though relative improvement on any of these was not predicted by baseline CCI. The CCI is an easily applicable preoperative predictor of survival time in shunted iNPH patients. The lack of a correlation between the CCI and functional outcome means that even patients with multiple comorbidities and limited remaining lifetime may appreciate benefit from shunt surgery.