Personal Assessment Research Articles

Carbapenem-resistant Enterobacterales sepsis following endoscopic retrograde cholangiopancreatography: risk factors for 30-day all-cause mortality and the development of a nomogram based on a retrospective cohort

BackgroundEndoscopic retrograde cholangiopancreatography (ERCP) has become a routine endoscopic procedure that is essential for diagnosing and managing various conditions, including gallstone extraction and the treatment of bile duct and pancreatic tumors. Despite its efficacy, post-ERCP infections – particularly those caused by carbapenem-resistant Enterobacterales (CRE) – present significant risks. These risks highlight the need for accurate predictive models to enhance postprocedural care, reduce the mortality risk associated with post-ERCP CRE sepsis, and improve patient outcomes in the context of increasing antibiotic resistance.ObjectiveThis study aimed to examine the risk factors for 30-day mortality in patients with CRE sepsis following ERCP and to develop a nomogram for accurately predicting 30-day mortality risk.MethodsData from 195 patients who experienced post-ERCP CRE sepsis between January 2010 and December 2022 were analyzed. Variable selection was optimized via the least absolute shrinkage and selection operator (LASSO) regression model. Multivariate logistic regression analysis was then employed to develop a predictive model, which was evaluated in terms of discrimination, calibration, and clinical utility. Internal validation was achieved through bootstrapping.ResultsThe nomogram included the following predictors: age > 80 years (hazard ratio [HR] 2.61), intensive care unit (ICU) admission within 90 days prior to ERCP (HR 2.64), hypoproteinemia (HR 4.55), quick Pitt bacteremia score ≥ 2 (HR 2.61), post-ERCP pancreatitis (HR 2.52), inappropriate empirical therapy (HR 3.48), delayed definitive therapy (HR 2.64), and short treatment duration (< 10 days) (HR 5.03). The model demonstrated strong discrimination and calibration.ConclusionsThis study identified significant risk factors associated with 30-day mortality in patients with post-ERCP CRE sepsis and developed a nomogram to accurately predict this risk. This tool enables healthcare practitioners to provide personalized risk assessments and promptly administer appropriate therapies against CRE, thereby reducing mortality rates.

Heart Disease Prediction Using Machine Learning

Heart disease remains a leading cause of mortality worldwide, necessitating improved methods for early detection and risk assessment. This paper reviews and analyzes the application of machine learning techniques in heart disease prediction, focusing on five primary algorithms: Naïve Bayes, k-Nearest Neighbor (KNN), Decision Tree, Artificial Neural Network (ANN), and Random Forest. By examining existing studies and datasets, we evaluate the effectiveness of these algorithms in predicting heart disease risk. Our analysis demonstrates that machine learning models can significantly enhance the accuracy and efficiency of heart disease prediction compared to traditional diagnostic methods. The Random Forest algorithm exhibited the highest overall performance, with studies reporting accuracy rates up to 95% in identifying potential heart disease cases. This review highlights the potential of machine learning in revolutionizing cardiovascular healthcare by enabling more personalized risk assessments and facilitating early intervention strategies. The integration of these advanced predictive models into clinical practice could substantially improve patient outcomes and reduce the global burden of heart disease. Keywords: Cardiovascular Risk Prediction, Machine Learning Algorithms, Electronic Health Records, Random Forest, Artificial Neural Networks, Feature Importance, Clinical Decision Support, Personalized Medicine, Predictive Analytics in Healthcare, Early Disease Detection.

Predicting progression-free survival in patients with epithelial ovarian cancer using an interpretable random forest model

Prognostic models play a crucial role in providing personalised risk assessment, guiding treatment decisions, and facilitating the counselling of patients with cancer. However, previous imaging-based artificial intelligence models of epithelial ovarian cancer lacked interpretability. In this study, we aimed to develop an interpretable machine-learning model to predict progression-free survival in patients with epithelial ovarian cancer using clinical variables and radiomics features. A total of 102 patients with epithelial ovarian cancer who underwent contrast-enhanced computed tomography scans were enrolled in this retrospective study. Pre-surgery clinical data, including age, performance status, body mass index, tumour stage, venous blood cancer antigen-125 (CA125) level, white blood cell count, neutrophil count, red blood cell count, haemoglobin level, and platelet count, were obtained from medical records. The volume of interest for each tumour was manually delineated slice-by-slice along the boundary. A total of 2074 radiomic features were extracted from the pre- and post-contrast computed tomography images. Optimal radiomic features were selected using the Least Absolute Shrinkage and Selection Operator logistic regression. Multivariate Cox analysis was performed to identify independent predictors of three-year progression-free survival. The random forest algorithm developed radiomic and combined models using four-fold cross-validation. Finally, the Shapley additive explanation algorithm was applied to interpret the predictions of the combined model. Multivariate Cox analysis identified CA-125 levels (P = 0.015), tumour stage (P = 0.019), and Radscore (P < 0.001) as independent predictors of progression-free survival. The combined model based on these factors achieved an area under the curve of 0.812 (95 % confidence interval: 0.802–0.822) in the training cohort and 0.772 (95 % confidence interval: 0.727–0.817) in the validation cohort. The most impactful features on the model output were Radscore, followed by tumour stage and CA-125. In conclusion, the Shapley additive explanation-based interpretation of the prognostic model enables clinicians to understand the reasoning behind predictions better.

An exploration of factors affecting hotel consumers' health from an ecological systems theory perspective: A meta-analytical study

Consumers are placing increasing importance on their health experiences during their hotel stays. Previous studies have explored the various factors influencing consumer health experiences from multiple independent micro-perspectives, such as hotel environment and service type. In addition to the lack of generalization of influencing factors and outcomes from an overall perspective, existing research also ignores the impact of some important macro variables, such as culture and time. To fill these gaps, this study adopted a meta-analytic approach, guided by the Ecological Systems Theory, and explored based on 44 independent studies obtained from 41 empirical papers with 19,625 participants. The findings of the study indicate that individual factors (e.g. consumer personal assessment, motivation, and personality), microsystem factors (e.g. hotel environment and hotel service), macrosystem factors (e.g. culture), and chronosystem factors (e.g. critical events) all have an impact on hotel consumer health. In addition, this study builds on the findings of existing research to summarise the gaps and shortcomings of this research topic. These findings can help hotels enhance consumer health experiences and promote related research.

Applications of Artificial Intelligence in Psychiatric Nursing: A Scope Review.

Rapid advances in artificial intelligence (AI) have reshaped healthcare, including psychiatric nursing, to address the limitations of traditional approaches and meet escalating mental health challenges. A scoping review analyzed 48 articles examining the application of AI in psychiatric nursing across different technologies and topics, noting trends in publications and countries involved. The articles covered different aspects of mental health using AI technologies such as machine learning and robotics, and primarily explored AI applications in mental health, specifically dementia, autism and schizophrenia. These studies highlighted the role of AI in personalized care plans, symptom monitoring and risk assessment. AI is promising, but faces challenges such as data bias and ethical concerns. Future research needs to focus on long-term studies, diverse populations, patient interaction and personalized treatments for practical integration into psychiatric nursing.

The Role of Artificial Intelligence in Dental Diagnosis

This paper discusses the potential role of artificial intelligence in enhancing dental diagnosis and care delivery. It describes how AI shows promise in automatic detection of abnormalities in dental images, personalised disease risk assessment through predictive modelling, and generation of comprehensive examination reports. The ability of AI to analyse patterns across medical imaging, genetics, lifestyle factors and health records enables more holistic understanding of oral health in relation to systemic conditions. Clinical decision support through differential diagnoses and evidence-based treatment recommendations aims to augment dentists' decision making. Seamless integration of AI into clinical workflows through interfaces is emphasised as important for adoption. Challenges around data and model validation are also addressed. Continued development of AI aims to realise benefits like earlier disease identification and more proactive, personalised care approaches

Association between myosteatosis and impaired glucose metabolism: A deep learning whole-body magnetic resonance imaging population phenotyping approach.

There is increasing evidence that myosteatosis, which is currently not assessed in clinical routine, plays an important role in risk estimation in individuals with impaired glucose metabolism, as it is associated with the progression of insulin resistance. With advances in artificial intelligence, automated and accurate algorithms have become feasible to fill this gap. In this retrospective study, we developed and tested a fully automated deep learning model using data from two prospective cohort studies (German National Cohort [NAKO] and Cooperative Health Research in the Region of Augsburg [KORA]) to quantify myosteatosis on whole-body T1-weighted Dixon magnetic resonance imaging as (1) intramuscular adipose tissue (IMAT; the current standard) and (2) quantitative skeletal muscle (SM) fat fraction (SMFF). Subsequently, we investigated the two measures for their discrimination of and association with impaired glucose metabolism beyond baseline demographics (age, sex and body mass index [BMI]) and cardiometabolic risk factors (lipid panel, systolic blood pressure, smoking status and alcohol consumption) in asymptomatic individuals from the KORA study. Impaired glucose metabolism was defined as impaired fasting glucose or impaired glucose tolerance (140-200mg/dL) or prevalent diabetes mellitus. Model performance was high, with Dice coefficients of ≥0.81 for IMAT and ≥0.91 for SM in the internal (NAKO) and external (KORA) testing sets. In the target population (380 KORA participants: mean age of 53.6±9.2years, BMI of 28.2±4.9kg/m2, 57.4% male), individuals with impaired glucose metabolism (n=146; 38.4%) were older and more likely men and showed a higher cardiometabolic risk profile, higher IMAT (4.5±2.2% vs. 3.9±1.7%) and higher SMFF (22.0±4.7% vs. 18.9±3.9%) compared to normoglycaemic controls (all P≤0.005). SMFF showed better discrimination for impaired glucose metabolism than IMAT (area under the receiver operating characteristic curve [AUC] 0.693 vs. 0.582, 95% confidence interval [CI] [0.06-0.16]; P<0.001) but was not significantly different from BMI (AUC 0.733 vs. 0.693, 95% CI [-0.09 to 0.01]; P=0.15). In univariable logistic regression, IMAT (odds ratio [OR]=1.18, 95% CI [1.06-1.32]; P=0.004) and SMFF (OR=1.19, 95% CI [1.13-1.26]; P<0.001) were associated with a higher risk of impaired glucose metabolism. This signal remained robust after multivariable adjustment for baseline demographics and cardiometabolic risk factors for SMFF (OR=1.10, 95% CI [1.01-1.19]; P=0.028) but not for IMAT (OR=1.14, 95% CI [0.97-1.33]; P=0.11). Quantitative SMFF, but not IMAT, is an independent predictor of impaired glucose metabolism, and discrimination is not significantly different from BMI, making it a promising alternative for the currently established approach. Automated methods such as the proposed model may provide a feasible option for opportunistic screening of myosteatosis and, thus, a low-cost personalized risk assessment solution.

The Role of Self-Compassion, Self-Esteem of Bullying Victims: A Qualitative Study

Bullying is any form of intimidation or violent behavior carried out intentionally by one or a group of people who are stronger or more powerful towards another person, which aims to hurt and is carried out continuously. Bullying has a very bad impact on victims, especially teenagers. For example, it is easy to experience fear, anxiety and can lead to depression. This bullying behavior can affect the development of self-esteem, resulting in low self-esteem in teenagers. This gives rise to thoughts of one's own worthlessness and makes it difficult to socialize with the surrounding environment. Self-esteem is the basis for a person's assessment of himself both positively and negatively as a whole. This research uses quantitative methods with a literature review approach. Various sources relevant to the research topic were collected and then analyzed. So the results of this study show that increasing teenagers' self-esteem is effective in minimizing teenage victims of bullying. Therefore, it can be concluded that self- esteem is found to be related to bullying. Self-compassion in the health quality of life of adolescents is significant in the dimensions of physical, psychological well- being, relationships with parents and autonomy, as well as the school environment. However, self-compassion was found not to be very significant in the dimensions of social support and peers, because self-compassion in improving various health quality of life domains indicates the importance of developing a compassionate attitude towards oneself for adolescents in order to improve their quality of life.

CT radiomics combined with clinical and radiological factors predict hematoma expansion in hypertensive intracerebral hemorrhage.

This study aimed to establish a hematoma expansion (HE) prediction model for hypertensive intracerebral hemorrhage (HICH) patients by combining CT radiomics, clinical information, and conventional imaging signs. A retrospective continuous collection of HICH patients from three medical centers was divided into a training set (n = 555), a validation set (n = 239), and a test set (n = 77). Extract radiomics features from baseline CT plain scan images and combine them with clinical information and conventional imaging signs to construct radiomics models, clinical imaging sign models, and hybrid models, respectively. The models will be evaluated using the area under the curve (AUC), clinical decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI). In the training, validation, and testing sets, the radiomics model predicts an AUC of HE of 0.885, 0.827, and 0.894, respectively, while the clinical imaging sign model predicts an AUC of HE of 0.759, 0.725, and 0.765, respectively. Glasgow coma scale score at admission, first CT hematoma volume, irregular hematoma shape, and radiomics score were used to construct a hybrid model, with AUCs of 0.901, 0.838, and 0.917, respectively. The DCA shows that the hybrid model had the highest net profit rate. Compared with the radiomics model and the clinical imaging sign model, the hybrid model showed an increase in NRI and IDI. The hybrid model based on CT radiomics combined with clinical and radiological factors can effectively individualize the evaluation of the risk of HE in patients with HICH. CT radiomics combined with clinical information and conventional imaging signs can identify HICH patients with a high risk of HE and provide a basis for clinical-targeted treatment. HE is an important prognostic factor in patients with HICH. The hybrid model predicted HE with training, validation, and test AUCs of 0.901, 0.838, and 0.917, respectively. This model provides a tool for a personalized clinical assessment of early HE risk.

Uveal melanoma distant metastasis prediction system: A retrospective observational study based on machine learning.

Uveal melanoma (UM) patients face a significant risk of distant metastasis, closely tied to a poor prognosis. Despite this, there is a dearth of research utilizing big data to predict UM distant metastasis. This study leveraged machine learning methods on the Surveillance, Epidemiology, and End Results (SEER) database to forecast the risk probability of distant metastasis. Therefore, the information on UM patients from the SEER database (2000-2020) was split into a 7:3 ratio training set and an internal test set based on distant metastasis presence. Univariate and multivariate logistic regression analyses assessed distant metastasis risk factors. Six machine learning methods constructed a predictive model post-feature variable selection. The model evaluation identified the multilayer perceptron (MLP) as optimal. Shapley additive explanations (SHAP) interpreted the chosen model. A web-based calculator personalized risk probabilities for UM patients. The results show that nine feature variables contributed to the machine learning model. The MLP model demonstrated superior predictive accuracy (Precision = 0.788; ROC AUC = 0.876; PR AUC = 0.788). Grade recode, age, primary site, time from diagnosis to treatment initiation, and total number of malignant tumors were identified as distant metastasis risk factors. Diagnostic method, laterality, rural-urban continuum code, and radiation recode emerged as protective factors. The developed web calculator utilizes the MLP model for personalized risk assessments. In conclusion, the MLP machine learning model emerges as the optimal tool for predicting distant metastasis in UM patients. This model facilitates personalized risk assessments, empowering early and tailored treatment strategies.

Investigating spatiotemporal and kinematic gait parameters in individuals with Parkinson's disease with a history of freezing of gait and exploring the effects of dopaminergic therapy on freezing of gait subtypes.

Freezing of Gait (FOG) is a prevalent and debilitating symptom in idiopathic Parkinson's disease (PD). This study evaluated spatiotemporal and kinematic gait parameters in individuals with PD with a history of FOG and explored the effects of dopaminergic therapy on FOG subtypes. One hundred and nine individuals with PD underwent clinical assessments and quantitative biomechanical measures during walking cycles before and after dopaminergic therapy. Individuals with FOG were classified into levodopa-responsive and levodopa-unresponsive groups. Individuals with FOG displayed longer disease duration and higher Unified Parkinson's Disease Rating Scale (UPDRS) II, III, IV scores, and total scores and levodopa equivalent dose, than those without FOG (all p < 0.0001). Following propensity score matching of 15 pairs based on UPDRS total score and disease duration during the off-medication state, the analysis comparing the FOG and non-FOG groups revealed no significant differences in spatiotemporal and kinematic parameters. In 39 cases of FOG, dopaminergic therapy improved gait performance in individuals with PD, enhancing spatiotemporal parameters (speed, stride length, step length, step variability) and kinematic parameters (shoulder and elbow flexion/extension range of motion (ROM), pelvic rotation, and hip abduction/adduction ROM) regardless of FOG responsiveness to dopaminergic therapy. A significant difference in trunk sway ROM (p = 0.029) remained before and after dopaminergic therapy, even after adjusting for disease duration and clinical severity. Dopaminergic therapy had varying effects on PD with FOG, improving several spatiotemporal and kinematic gait parameters but being less effective in levodopa-unresponsive cases. Quantitative biomechanical measures offer detailed insights into gait performance, aiding personalized fall risk assessment and guiding individualized rehabilitation programs.

Simulation-based personal fatality risk assessment due to the fragmentation hazard

In the military and chemical industry, modeling the fragmentation hazard field is of great significance in conducting the fatality risk assessment and calculating the safety distance for person. Although the ballistic methodology achieves the simulation of fragmentation flight trajectory, the acquisition of accurate initial projection data of fragmentation is a challenge. By coupling continuum-discontinuum element method and particle discrete element method, the fragmentation power algorithm is established, which achieves the integrated simulation of the initial projection data of fragmentation and the flight process of fragmentation. First, continuum-discontinuum element method is adopted to simulate the detonation products-driven fragmentation acceleration process by introducing the explosive detonation model, which achieves the acquisition of initial projection data of fragmentation. Then, the particle discrete element method is adopted to simulate the flight trajectory and power data of fragmentation. Based on the numerical and experimental result, the accuracy of fragmentation power algorithm is verified. In conjunction with the personal vulnerability model, a systematic numerical simulation framework is established to conduct the simulation-based personal fatality risk assessment when the metal-cased munition detonates accidentally, and the results indicate that the personal fatality risk due to the fragmentation becomes severer with the increase of munition curvature.

O-132 A systematic spatial HiPlex proteomics exploration of human testicular tissues from birth to adulthood

Abstract Study question Can we map the key maturation stages of human spermatogonial stem cells (SSCs) within their niche during infancy and puberty? Summary answer Using highplex spatial proteomics, we describe the key cellular events taking place during human testis maturation. What is known already The human testis is a complex “ecosystem” that matures in response to endocrine and exocrine signals and interactions between its diverse cellular components. Unlike animal models, in humans, testicular maturation is a slow process that takes place over more than a decade. In contrast to ovaries, the human testis retains its immature state until the onset of puberty. Single-cell RNA sequencing data has revealed significant developmental changes for germ cell and the somatic niche during infancy and puberty. Study design, size, duration Using the hyperplex imaging technique (CellDIVE) and a panel of 30 protein markers - selected to label key components of the developing human testis - we have systematically analyzed a time series of ∼75 testicular biopsies from boys aged 8 months to adulthood. Following sequential staining and capture of high-resolution images, our analysis pipeline involves the conversion of image into single-cell data, which allows a systematic description of the spatial SSC/niche microenvironment interactions across development. Participants/materials, setting, methods The panel of antibody markers was defined using integrated scRNAseq data and represents all key cell types, including germ cell markers (e.g., MAGEA4), Sertoli cell markers (e.g., SOX9), Leydig cell markers (e.g., INSL3), myoid cell markers (e.g., ACTA2) ... Main results and the role of chance To describe the events occurring during human testicular maturation, we build a Tissue Microarray (TMA) containing ∼75 biopsies of boys of different ages (0-16yrs old) and using an in-house panel of 30 markers for high-dimensional multiplex CELL DIVE images. Our analytical strategy uses Uniform Manifold Approximation and Projection (UMAP) maps to quantify and statistically compare single-cell data. Our marker panel allows us to phenotype testicular cells at different stages of development, including SSCs (state 0 to state 4), Sertoli cells, Leydig cells, and so on. Moreover, employing spatial-latent Dirichlet allocation (LDA), we identified recurrent cellular neighbourhoods (RCNs), revealing distinct cell clusters and their corresponding RCNs. These RCNs reflect recurring spatial arrangements of cells within testicular tissues, allowing us to infer the spatial organization and interplay among different cell types. Additionally, we explored spatial distances and neighbourhood frequencies across different tissues and conducted spatial co-occurrence analysis to assess the likelihood of finding adjacent cell types. The identification of RCNs offers valuable insights into the microenvironmental dynamics and cellular relationships taking place within the developing testis. Looking ahead, we aim to delve into the functional significance of RCNs in shaping the developmental trajectories and physiological processes within human testicular tissues. Limitations, reasons for caution Due to limitations in proteomics techniques, we could only include ∼30 protein markers on our panel. Although the biopsy samples used in our study are all from chemo-naïve patients, it possible that the conditions affecting these paediatric patients may impact the architecture or development of their testicular tissues. Wider implications of the findings Our study provides a systematic approach to study the development and organization of human testicular structures, which will allow to enumerate the cellular content of individual testicular biopsies and infer their stage of maturation, therefore providing a personalised baseline assessment for each patient at the time of cryopreservation. Trial registration number not applicable

Assessment of Health Risk from Exposure to Respirable Particulate Matter (RPM) among Motorcycle Taxi Drivers in Bangkok and Adjacent Provinces, Thailand

Exposure to outdoor air pollutants, particularly respirable particulate matter (RPM), can cause adverse health outcomes. The cross-sectional study aimed to assess motorcycle taxi driver’s health risk from exposure to RPM. A total of 153 motorcycle taxi drivers were recruited in Bangkok and five adjacent provinces during May and June 2022. The standardized questionnaire for data collection contained exposure time (hour/day), frequency of exposure (days/years), duration of exposure (year), body weight (kg), and averaging time (days). The average RPM concentration from six provinces were assessed personal air sampling pumps and ranged from 0.006-0.031 mg/m3. Bangkok showed the highest average RPM concentration (0.031 mg/m3), followed by Pathumthani (0.028 mg/m3), Samut Prakan (0.009 mg/m3), Nakhon Pathom (0.008 mg/m3), Nonthaburi (0.007 mg/m3), and Samut Sakhon (0.006 mg/m3), respectively. The Hazard Quotient (HQ) values for a non-carcinogenic risk to human health caused by RPM exposure in each province indicated a negligible risk (HQ=0.005-0.028). HQ averages (HQ=0.013) from all provinces were also at an acceptable level (≤1). Not all motorcycle taxi drivers are safe from RPM exposure, although their exposure is within acceptable limits depending on their individual susceptibility. Therefore, this is the first report on quantifying exposure to RPM from personal air sampling and health risk assessment among motorcycle taxi drivers. These findings would be useful information for further preventing and controlling ambient air pollution including policies and strategies to mitigate the risks for motorcycle-taxi drivers and the other exposed populations.

Attention and Interoception Alter Perceptual and Neural Pain Signatures-A Case Study.

Fluctuations of chronic pain levels are determined by a complex interplay of cognitive, emotional and perceptual variables. We introduce a pain tracking platform composed of wearable neurotechnology and a smartphone application to measure and predict chronic pain levels and its interplay with other dimensions of experience. Our method measures, dynamically and at home, pain strength, phenomenal and neural time series collected with an online tool and low-density EEG. Here we used data from a single participant who performed an attention task at home for a period of 20 days to investigate the role of attention to different bodily systems in chronic pain. We show a relationship between emotions and pain strength while allocating attention to the heartbeat, the breathing, the affected or the unaffected limb. We found that pain was maximal when attending to the affected limb and decreased when the participant focused on his breathing or his heartbeat. These results provide interesting insights regarding the role of attention to interoceptive signals in chronic pain. We found power changes in the delta, theta, alpha and beta (but not in the gamma) band between the four attention conditions. However, there was no reliable association of these changes to pain intensity ratings. Theta power was higher when attention was directed to the unaffected limb compared to the others. Further, the pain ratings, when attending to unaffected limb, were associated with alpha and theta power band changes. Overall, we demonstrate that our neurophysiology and experience tracking platform can capture how body attention allocation alters the dynamics of subjective measures and its neural correlates. This research approach is proof of concept for the development of personalized clinical assessment tools and a testbed for behavioural, subjective and biomarkers characterization.

Longitudinal observation of tRNA-derived fragments profiles in gestational diabetes mellitus and its diagnostic value.

Gestational Diabetes Mellitus (GDM) poses significant risks to maternal and fetal health. Current diagnostic methods based on glucose tolerance tests have limitations for early detection. tRNA-derived small RNAs (tsRNAs) have emerged as potential molecular regulators in various diseases, including metabolic disorders. However, the diagnostic value of tsRNAs in plasma for early GDM or postpartum remains unclear. This longitudinal study profiled the expression of tsRNAs across different gestational stages and postpartum in women with GDM (n = 40) and healthy control gestational women (HCs, n = 40). High-throughput small RNA sequencing identified candidate tsRNAs, which were then validated and correlated with clinical biochemical markers such as fasting blood glucose (FBG), HOMA-IR, and GHbA1c. tRF-1:32-Val-AAC-1-M6, tRF-1:31-Glu-CTC-1-M2, and tRF-1:30-Gly-CCC-1-M4 were consistently upregulated in the GDM group compared to HCs during the second trimester (p < 0.05). Only tRF-1:31-Glu-CTC-1-M2 was highly expressed during the first trimester, and tRF-1:30-Gly-CCC-1-M4 increased during postpartum. tRF-1:31-Glu-CTC-1-M2 showed a significant correlation with FBG levels in the first trimester (R = 0.317, p = 0.047). The expression of tRF-1:30-Gly-CCC-1-M4 was significantly correlated with HOMA-IR (r = 0.65, p < 0.001) and GHBA1c (r = 0.33, p = 0.037) during postpartum. A joint diagnostic model incorporating tsRNAs expression and clinical markers demonstrated enhanced predictive power for GDM (ROC AUC = 0.768). Our results revealed distinct expression patterns of specific tsRNAs in GDM, showcasing their correlation with key metabolic parameters. This underscores their promising role as biomarkers for early prediction and diagnosis of GDM. The integration of tRFs into a composite biomarker panel holds the potential to improve clinical outcomes by enabling personalized risk assessment and targeted interventions.

Validity of Mobility-Based Exposure Assessment of Air Pollution: A Comparative Analysis with Home-Based Exposure Assessment.

Air pollution exposure is typically assessed at the front door where people live in large-scale epidemiological studies, overlooking individuals' daily mobility out-of-home. However, there is limited evidence that incorporating mobility data into personal air pollution assessment improves exposure assessment compared to home-based assessments. This study aimed to compare the agreement between mobility-based and home-based assessments with personal exposure measurements. We measured repeatedly particulate matter (PM2.5) and black carbon (BC) using a sample of 41 older adults in the Netherlands. In total, 104 valid 24 h average personal measurements were collected. Home-based exposures were estimated by combining participants' home locations and temporal-adjusted air pollution maps. Mobility-based estimates of air pollution were computed based on smartphone-based tracking data, temporal-adjusted air pollution maps, indoor-outdoor penetration, and travel mode adjustment. Intraclass correlation coefficients (ICC) revealed that mobility-based estimates significantly improved agreement with personal measurements compared to home-based assessments. For PM2.5, agreement increased by 64% (ICC: 0.39-0.64), and for BC, it increased by 21% (ICC: 0.43-0.52). Our findings suggest that adjusting for indoor-outdoor pollutant ratios in mobility-based assessments can provide more valid estimates of air pollution than the commonly used home-based assessments, with no added value observed from travel mode adjustments.

Whose stance is it, anyway? A corpus-based study of stance expressions in science news articles

Communicating scientific research to the public is as important as ever. Several studies have investigated the ways that science writers communicate personal feelings and assessments, otherwise known as stance, in their writing, reporting somewhat mixed results. In this study, I examine the grammatical stance marking of science news articles. I compare the ways that writers and the researchers that they quote uniquely contribute to the overall expression of stance in these articles. Results reveal that the researchers’ talk substantially contribute to the overall stance of science news articles compared to their writers, indicated by a greater use of modal verbs, stance verbs and adjectives controlling that complement clauses, and stance adverbials, including a greater use of ‘boosters’. This study suggests that researchers’ talk in science news articles co-participate in the newsworthiness of science and offer an alternative perspective on academic research uncommon in research writing.

Does Web-Based Relationship Education Improve Sexual Intimacy? Initial Examination, Replication, and Exploration of Moderation in a Low-Income Sample.

Sexual intimacy, characterized as the experience between individuals of sharing general affection and sexual activity with one another within the Personal Assessment of Intimacy in Relationships inventory, is positively related to relationship satisfaction and stability. However, many studies of couple therapy have shown that it only results in small-sized (and often non-significant) improvements in sexual intimacy. Furthermore, there are numerous financial, logistical, and psychological barriers to couple therapy. Thus, the current study sought to examine whether two brief online relationship education programs (OurRelationship and ePREP) could overcome these barriers and yield similar-sized effects to more intensive couple therapy. In two independently collected samples of low-income couples (NSample 1 = 742 Couples; M AgeSample 1 = 33.19; NSample 2 = 671 Couples; M AgeSample 2 = 33.48), the current study found that: OurRelationship (d = 0.24-0.28) and ePREP (d = 0.26-0.34) produced small-sized changes in sexual intimacy relative to a waitlist control condition in both samples, the magnitude of the effect size replicated in a second sample and, with rare exception, these changes were generally not moderated by key variables of interest. Given that web-based relationship education is significantly shorter, less expensive, and more accessible than in-person couple interventions, web-based relationship education could be considered a viable candidate for couples experiencing concerns with sexual intimacy.

Методика управління кадровими ризиками підприємства

This article analyzes the essence of personnel risks of enterprises and develops a methodology for managing them. The methodology for managing personnel risks of an enterprise primarily involves identifying the risk and identifying its components. It also includes various aspects of the process of managing personnel and the enterprise as a whole, the key elements of which are: personnel risk factors; indicators of personnel risks; probable losses or gains; methods of personnel risk management; budgeting; Implementation deadlines. It is noted that the development of methods for managing personnel risks begins only when the critical point of the onset of negative consequences of the implementation of personnel risks in the functioning of the enterprise is reached, which entail serious problems. The main methods of managing personnel risks of an enterprise have been identified, which include preventive and reactive methods. The preventative method is to eliminate personnel risks before they reach the crisis stage. The reactive method is aimed at eliminating the negative consequences of personnel risks. Both methods should be used in the process of managing personnel risks of an enterprise. The proposed system of methods for managing personnel risks of an enterprise, built on the main stages of the personnel management process, allows us to formulate a number of recommendations in order to neutralize the impact of personnel risks, namely: recruitment, selection and selection of personnel, adaptation of personnel to working conditions at the enterprise; employee assessment and certification; personnel training and development; dismissal of workers. It has been proven that managing personnel risks requires the use of a set of measures to eliminate them. For this purpose, it is advisable to use risk transfer methods through outstaffing, outsourcing or personnel leasing. It is indicated that the management of personnel risks of an enterprise is closely related to the goals and objectives of the strategic development of the enterprise; personnel management system; personal assessment of employees and continuous identification and monitoring of the performance of enterprise personnel. Based on the results of the study, the main directions for minimizing the impact of personnel risks of the enterprise were identified, and the main methods for avoiding them were proposed.