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Abstract The mental health needs of young people in sub-Saharan African societies remain understudied. This study builds upon the everyday perspectives of young people in Gorongosa, a rural district in central Mozambique, to determine the frequency and severity of key mental health issues, identify significant risk and protective factors, and their associations with demographic factors and mental health predictors. This cross-sectional study gathered culturally and ecologically relevant variables, such as sociodemographic indicators and risk and protective factors. The Beck Depression Inventory-II (BDI-II), Self-Report Questionnaire (SRQ) and Nocturnal Intrusions after Traumatic Experiences Questionnaire (NITE) were used to determine the prevalence and severity of key mental health issues. A convenience sampling was used involving n = 794 young people of those (45.8%) were females and the mean age of all participants was 17.24 years (SD 2.9). To examine the results, univariate analysis, chi-square tests, binary logistic regression analyses and odds ratio were used. Risk factors were pervasive in households, communities, and schools. Mental health problems were commonly linked to severe depression (14.2%), anxiety-related disorders (39.3%), sleep disturbances (37.3%), and anxiety dreams (27.5%). Gender, age, and protective factors were associated with the frequency and severity of mental health outcomes, while living with both parents protected against the severity of anxiety-related disorders. Understanding the contextual mental health needs of young people in rural Mozambique is crucial for developing insights to craft and implement targeted public mental health interventions in resource-limited settings. Addressing mental health issues among young people necessitates a concentrated effort on understanding and managing the interplay of risks and protective factors within families, communities, and schools.

Abstract The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms’ performance. Unlike H&E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. Subsequently, MF detectors, including a novel dual-stain detector, were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models’ performance. We found that the annotators’ object-level agreement significantly increased when using PHH3-assisted labeling (F1: 0.53 to 0.74). However, this enhancement in label consistency did not translate to improved performance for H&E-based detectors, neither during the training phase nor the evaluation phase. Conversely, the dual-stain detector was able to benefit from the higher consistency. This reveals an information mismatch between the H&E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H&E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure.

Fibro-adipogenic progenitors (FAPs) are essential in supporting regeneration in skeletal muscle, but in muscle pathologies FAPs the are main source of excess extracellular matrix (ECM) resulting in fibrosis. Fibrotic ECM has altered mechanical and architectural properties, but the feedback onto FAPs of stiffness or ECM properties is largely unknown. In this study, FAPs' sensitivity to their ECM substrate was assessed using collagen coated polyacrylamide to control substrate stiffness and collagen hydrogels to engineer concentration, crosslinking, fibril size, and alignment. FAPs on substrates of fibrotic stiffnesses had increased myofibroblast activation, depicted by αSMA expression, compared to substrates mimicking healthy muscle, which correlated strongly YAP nuclear localization. Surprisingly, fibrosis associated collagen crosslinking and larger fibril size inhibited myofibroblast activation, which was independent of YAP localization. Additionally, collagen crosslinking and larger fibril diameters were associated with decreased remodeling of the collagenous substrate as measured by second harmonic generation imaging. Inhibition of YAP activity through verteporfin reduced myofibroblast activation on stiff substrates but not substrates with altered architecture. This study is the first to demonstrate that fibrotic muscle stiffness can elicit FAP activation to myofibroblasts through YAP signaling. However, fibrotic collagen architecture actually inhibits myofibroblast activation through a YAP independent mechanism. These data expand knowledge of FAPs sensitivity to ECM and illuminate targets to block FAP's from driving progression of muscle fibrosis.

The mental health needs of young people in sub-Saharan African societies remain understudied. This study builds upon the everyday perspectives of young people in Gorongosa, a rural district in central Mozambique, to determine the frequency and severity of key mental health issues, identify significant risk and protective factors, and their associations with demographic factors and mental health predictors. This cross-sectional study gathered culturally and ecologically relevant variables, such as sociodemographic indicators and risk and protective factors. The Beck Depression Inventory-II (BDI-II), Self-Report Questionnaire (SRQ) and Nocturnal Intrusions after Traumatic Experiences Questionnaire (NITE) were used to determine the prevalence and severity of key mental health issues. A convenience sampling was used involving n = 794 young people of those (45.8%) were females and the mean age of all participants was 17.24 years (SD 2.9). To examine the results, univariate analysis, chi-square tests, binary logistic regression analyses and odds ratio were used. Risk factors were pervasive in households, communities, and schools. Mental health problems were commonly linked to severe depression (14.2%), anxiety-related disorders (39.3%), sleep disturbances (37.3%), and anxiety dreams (27.5%). Gender, age, and protective factors were associated with the frequency and severity of mental health outcomes, while living with both parents protected against the severity of anxiety-related disorders. Understanding the contextual mental health needs of young people in rural Mozambique is crucial for developing insights to craft and implement targeted public mental health interventions in resource-limited settings. Addressing mental health issues among young people necessitates a concentrated effort on understanding and managing the interplay of risks and protective factors within families, communities, and schools. [ABSTRACT FROM AUTHOR]

The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms' performance. Unlike H&E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. Subsequently, MF detectors, including a novel dual-stain detector, were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models' performance. We found that the annotators' object-level agreement significantly increased when using PHH3-assisted labeling (F1: 0.53 to 0.74). However, this enhancement in label consistency did not translate to improved performance for H&E-based detectors, neither during the training phase nor the evaluation phase. Conversely, the dual-stain detector was able to benefit from the higher consistency. This reveals an information mismatch between the H&E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H&E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure. [ABSTRACT FROM AUTHOR]