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Mindfulness-based stress reduction as perceived by individuals with pathological mental fatigue after an acquired brain injury

Abstract After acquired brain injury (ABI), some individuals suffer from long-term fatigue and emotional distress, which affects their work ability and daily life. Mindfulness-based stress reduction (MBSR) has shown promising results in quantitative studies as a complementary treatment for pathological mental fatigue (PMF) after ABI. This study aims to explore how people suffering from lasting PMF after ABI experience MBSR in relation to their PMF, with the intention of better meeting the rehabilitation needs of those affected. Seven individuals (mean age 42 years) who had suffered from long-lasting PMF after ABI took part in the study. None of them had resumed work due to their PMF, but all had recovered from neurological impairments. They were interviewed after completing an MBSR course. Thematic analysis of the participants’ perspectives suggested that the MBSR program provided them with coping techniques for living with PMF. They gained a deeper understanding of their condition becoming more self-compassionate, and the treatment provided them with a forum for meeting and sharing experiences with others with similar problems. The qualitative results strengthen the evidence for MBSR as a feasible psychoeducative complementary treatment for PMF after ABI.

Effectiveness of a school-based physical activity intervention on overweight and obesity among children and adolescents in Pakistan

Background Childhood obesity poses a significant public health challenge, yet effective school-based physical activity (PA) interventions remain scarce, especially in Pakistan. There is a lack of data assessing the impact of such interventions on obesity and related health outcomes in Pakistani school children. Methods This study aimed to design and implement a school-based intervention targeting multiple levels of the socio-ecological model to increase physical activity and reduce the prevalence of overweight and obesity in Pakistani youth. Conducted from October 2022 to January 2023 in Lahore, Pakistan, the 12-week, non-randomized controlled trial involved 1,200 students from eight schools, with four schools (n = 570) in the intervention group and four (n = 630) in the control group. Primary outcomes included changes in body mass index (BMI), waist circumference, and the prevalence of overweight/obesity, measured through anthropometric assessments. Secondary outcomes involved alterations in moderate-to-vigorous physical activity duration. Descriptive statistics, Chi-square tests, general linear mixed models, and repeated measures ANOVA were used for analysis. Results The intervention showed significant improvements across various socio-ecological levels. Intrapersonal factors saw a reduction in fast food consumption from 11.9% to 7.9% (F(1,1198) = 90.39, p < 0.001; η² = 0.074) and an increase in physical activity frequency from 11.9% to 39.6% (F(1,1198) = 465.25, p < 0.001; η² = 0.028). Screen time decreased from 27.0% to 7.4% (F(1,1198) = 219.83, p = 0.015; η² = 0.15), and normal sleep duration increased from 44.6% to 71.8% (F(1,1198) = 242.73, p < 0.001; η² = 0.16). At the interpersonal level, parental involvement in encouraging sports and providing financial support for sports activities significantly increased. School-level factors also showed positive changes, including improved sports facilities and equipment access. Community-level factors revealed increased opportunities for physical activity and a more supportive community environment. The intervention group’s BMI change (−0.06 ± 0.07 kg·m²) significantly differed from the control group’s (0.19 ± 0.09 kg·m²). Conclusions This study demonstrates the effectiveness of a multi-level intervention in boosting physical activity and addressing obesity among Pakistani school-aged children, supporting the implementation of similar school-based interventions.

Mesenchymal stem cells modulate breast cancer progression through their secretome by downregulating ten-eleven translocation 1

Optimizing depression detection in clinical doctor-patient interviews using a multi-instance learning framework

Abstract In recent years, the number of people suffering from depression has gradually increased, and early detection is of great significance for the well-being of the public. However, the current methods for detecting depression are relatively limited, typically relying on the self-rating depression scale (SDS) and interviews. These methods are influenced by subjective or environmental factors. To improve the objectivity and efficiency of diagnosis, deep learning techniques have been applied to the field of automatic depression detection (ADD), providing a more accurate and objective approach. During interviews, transcribed interview data is one of the most commonly used modalities in ADD. However, previous studies have only utilized response texts or selected question–answer pairs, resulting in information redundancy and loss. This paper is the first to apply the multiple instance learning (MIL) framework to the field of textual interview data, aiming to overcome issues of inadequate text representation and ineffective information extraction in long texts. In the MIL framework, each instance undergoes an independent feature extraction process, ensuring that the local features of each instance are fully captured. This not only enhances the overall text representation capability but also alleviates the issue of sample imbalance in the dataset. Additionally, this paper improves upon previous aggregation strategies by introducing two hyper-parameters to accommodate the uncertainties in the field of text sentiment. An ensemble model of MT5 and RoBERTa (referred to as multi-MTRB) was constructed to extract features from each instance and output confidence scores indicating the presence of depressive information in the instances. Due to the unique design of the MIL framework, the proposed method is highly interpretable and is able to identify specific sentences that identify people from depressed patients, while introducing LIME techniques to provide more in-depth interpretation of negative instance sentences. This provides a promising approach for depression detection in the context of text interview data patterns. We evaluated the proposed method on DAIC-WOZ and E-DAIC datasets with excellent results. The F1 score is 0.88 on the DAIC-WOZ dataset and 0.86 on the E-DAIC dataset.

CTDNN-Spoof: compact tiny deep learning architecture for detection and multi-label classification of GPS spoofing attacks in small UAVs

Machine learning-enabled multiscale modeling platform for damage sensing digital twin in piezoelectric composite structures

Burden of periodontal diseases in young adults

Gamma-radiation insulating performance of AlON-hardened Na2O–Bi2O3–SiO2–BaO–Fe2O3–ZrO2 glasses

Geoclimatic modeling and assessment of pesticide dynamics in Indian soil

Identification of splenic IRF7 as a nanotherapy target for tele-conditioning myocardial reperfusion injury

Pan-cancer analysis reveals SMARCAL1 expression is associated with immune cell infiltration and poor prognosis in various cancers

Abstract Although immune checkpoint inhibition in particular has shown promise in cancer immunotherapy, it is not always efficient. Recent studies suggest that SMARCAL1 may play a role in tumor immune evasion, yet its pan-cancer role is unclear. We conducted a comprehensive analysis of SMARCAL1 using TCGA, GTEx, and CCLE databases, evaluating its expression, genetic alterations, epigenetic modifications, and their clinical correlations across 33 cancer types. Our findings indicate that SMARCAL1 is overexpressed in several cancers, such as Glioma, LUAD, KIRC, and LIHC, impacting prognosis. Elevated SMARCAL1 is linked to poor outcomes in Glioma, LUAD, and LIHC but correlates with better survival in KIRC. We also found significant associations between SMARCAL1 expression and DNA methylation in 13 cancers. Furthermore, SMARCAL1 expression correlates with immune infiltration, suggesting it as a potential therapeutic target in cancer immunotherapy. This study underscores the need for further research on SMARCAL1 to enhance immunotherapeutic strategies.

Potent and selective SETDB1 covalent negative allosteric modulator reduces methyltransferase activity in cells

SARS-CoV-2 neutralizing antibody determination after vaccination using spectrophotometric measurement of lateral flow immunochromatography

Context-dependent change in the fitness effect of (in)organic phosphate antiporter glpT during Salmonella Typhimurium infection

Abstract Salmonella enterica is a frequent cause of foodborne diseases, which is attributed to its adaptability. Even within a single host, expressing a gene can be beneficial in certain infection stages but neutral or even detrimental in others as previously shown for flagellins. Mutants deficient for the conserved glycerol-3-phosphate and phosphate antiporter glpT have been shown to be positively selected in nature, clinical, and laboratory settings. This suggests that different selective pressures select for the presence or absence of GlpT in a context dependent fashion, a phenomenon known as antagonistic pleiotropy. Using mutant libraries and reporters, we investigated the fitness of glpT-deficient mutants during murine orogastric infection. While glpT-deficient mutants thrive during initial growth in the gut lumen, where GlpT’s capacity to import phosphate is disadvantageous, they are counter-selected by macrophages. The dichotomy showcases the need to study the spatial and temporal heterogeneity of enteric pathogens’ fitness across distinct lifestyles and niches. Insights into the differential adaptation during infection may reveal opportunities for therapeutic interventions.

Evaluation of different mathematical models on fitting the in vitro gas production parameters in beef cattle

Artificial intelligence for modeling and understanding extreme weather and climate events

Evaluation of blood-tumor barrier permeability and doxorubicin delivery in rat brain tumor models using additional focused ultrasound stimulation

Author Correction: Assessment of human leukocyte antigen-based neoantigen presentation to determine pan-cancer response to immunotherapy

POU2F2+ B cells enhance antitumor immunity and predict better survival in non small cell lung cancer

Cecelia: a multifunctional image analysis toolbox for decoding spatial cellular interactions and behaviour

Abstract With the ever-increasing complexity of microscopy modalities, it is imperative to have computational workflows that enable researchers to process and perform in-depth quantitative analysis of the resulting images. However, workflows that allow flexible, interactive and intuitive analysis from raw images to analysed data are lacking for many experimental use-cases. Notably, integrated software solutions for analysis of complex 3D and live cell images are sorely needed. To address this, we present Cecelia, a toolbox that integrates various open-source packages into a coherent data management suite to make quantitative multidimensional image analysis accessible for non-specialists. We describe the application of Cecelia to several immunologically relevant scenarios and the development of an unbiased approach to distinguish dynamic cell behaviours from live imaging data. Cecelia is available as a software package with a Shiny app interface ( https://github.com/schienstockd/cecelia ). We envision that this framework and its approaches will be of broad use for biological researchers.