NFMPAtt-Unet: Neighborhood Fuzzy C-means Multi-scale Pyramid Hybrid Attention Unet for medical image segmentation.

Medical image segmentation is crucial for understanding anatomical or pathological changes, playing a key role in computer-aided diagnosis and advancing intelligent healthcare. Currently, important issues in medical image segmentation need to be addressed, particularly the problem of segmenting blurry edge regions and the generalizability of segmentation models. Therefore, this study focuses on different medical image segmentation tasks and the issue of blurriness. By addressing these tasks, the study significantly improves diagnostic efficiency and accuracy, contributing to the overall enhancement of healthcare outcomes. To optimize segmentation performance and leverage feature information, we propose a Neighborhood Fuzzy c-Means Multiscale Pyramid Hybrid Attention Unet (NFMPAtt-Unet) model. NFMPAtt-Unet comprises three core components: the Multiscale Dynamic Weight Feature Pyramid module (MDWFP), the Hybrid Weighted Attention mechanism (HWA), and the Neighborhood Rough Set-based Fuzzy c-Means Feature Extraction module (NFCMFE). The MDWFP dynamically adjusts weights across multiple scales, improving feature information capture. The HWA enhances the network's ability to capture and utilize crucial features, while the NFCMFE, grounded in neighborhood rough set concepts, aids in fuzzy C-means feature extraction, addressing complex structures and uncertainties in medical images, thereby enhancing adaptability. Experimental results demonstrate that NFMPAtt-Unet outperforms state-of-the-art models, highlighting its efficacy in medical image segmentation.

A New Classification and Laparoscopic Treatment of Extrahepatic Choledochal Cyst.

BACKGROUND: Prior typing methods fail to provide predictive insights into surgical complexities for extrahepatic choledochal cyst (ECC). This study aims to establish a new classification system for ECC through clustering of imaging results. Additionally, it seeks to compare the differences among the identified ECC types and assess the levels of surgical difficulty. METHODS: The imaging data of 124 patients were automatically grouped through a K-means clustering analysis. According to the characteristics of the new grouping, corrections and interventions were carried out to establish a new classification. Demographic data, clinical presentations, surgical parameters, complications, reoperation, and prognostic indicators were analyzed according to different types. Factors contributing to prolonged surgical time were also evaluated. RESULTS: A new classification system of ECC: Type A (upper segment), Type B (middle segment), Type C (lower segment), and Type D (entire bile duct). The incidences of comorbidities (calculus or infection) were significantly different (P=0.000, P=0.002). Additionally, variations in the incidence of postoperative cholangitis were statistically significant (P=0.046). The operative time was significantly different between groups (P=0.001). Age, BMI > 30, classification, and the presence of combined stones exhibit a significant association with prolonged operative time (P=0.002, P=0.000, P=0.011, P=0.011). CONCLUSION: In conclusion, our utilization of machine learning-driven cluster analysis has enabled the creation of a novel extrahepatic biliary dilatation typology. This classification, in conjunction with factors like age, combined stone occurrence, and obesity, significantly influences the complexity of laparoscopic choledochal cyst surgery, offering valuable insights for improved surgical treatment.

Exploring sex differences in blood-based biomarkers following exhaustive exercise using bioinformatics analysis.

This study examined the acute effects of exercise testing on immunology markers, established blood-based biomarkers, and questionnaires in endurance athletes, with a focus on biological sex differences. Twenty-four healthy endurance-trained participants (16 men, age: 29.2± 7.6 years, maximal oxygen uptake ( V ˙ O 2 max ): 59.4 ± 7.5 ml · min-1 · kg-1; 8 women, age: 26.8 ± 6.1 years, V ˙ O 2 max : 52.9 ± 3.1 ml · min-1 · kg-1) completed an incremental submaximal exercise test and a ramp test. The study employed exploratory bioinformatics analysis: mixed ANOVA, k-means clustering, and uniform manifold approximation and projection, to assess the effects of exhaustive exercise on biomarkers and questionnaires. Significant increases in biomarkers (lymphocytes, platelets, procalcitonin, hemoglobin, hematocrit, red blood cells, cell-free DNA (cfDNA)) and fatigue were observed post-exercise. Furthermore, differences pre- to post-exercise were observed in cytokines, cfDNA, and other blood biomarkers between male and female participants. Three distinct groups of athletes with differing proportions of females (Cluster 1: 100% female, Cluster 2: 85% male, Cluster 3: 37.5% female and 65.5% male) were identified with k-means clustering. Specific biomarkers (e.g., interleukin-2 (IL-2), IL-10, and IL-13, as well as cfDNA) served as primary markers for each cluster, potentially informing individualized exercise responses. In conclusion, our study identified exercise-sensitive biomarkers and provides valuable insights into the relationships between biological sex and biomarker responses.

Phenotypic plasticity and thermal efficiency of temperature responses in two conspecific springtail populations from contrasting climates.

Temperature drives adaptation in life-history traits through direct effects on physiological processes. However, multiple life-history traits co-evolve as a life-history strategy. Therefore, physiological limitations constraining the evolution of trait means and phenotypic plasticity can be larger for some traits than the others. Comparisons of thermal responses across life-history traits can improve our understanding of the mechanisms determining the life-history strategies. In the present study, we focused on a soil microarthropod species abundant across the Northern Hemisphere, Folsomia quadrioculata (Collembola), with previously known effects of macroclimate. We selected an arctic and a temperate population from areas with highly contrasting climates - the arctic tundra and a coniferous forest floor, respectively - and compared them for thermal plasticity and thermal efficiency in growth, development, fecundity, and survival across four temperatures for a major part of their life cycle. We intended to understand the mechanisms by which temperature drives the evolution of life-history strategies. We found that the temperate population maximized performance at 10-15 °C, whereas the arctic population maintained its thermal efficiency across a wider temperature range (10-20 °C). Thermal plasticity varied in a trait-specific manner, and when considered together with differences in thermal efficiency, indicated that stochasticity in temperature conditions may be important in shaping the life-history strategies. Our study suggests that adopting a whole-organism approach and including physiological time considerations while analysing thermal adaptation will markedly improve our understanding of plausible links between thermal adaptation and responses to global climate change.

Damage evolution law of multi-hole blasting igneous rock and quantitative evaluation model of damage degree based on fractal theory and clustering algorithm.

The geological phenomenon of igneous rock invading coal seam is widely distributed, which induces mining risk and affects efficient mining. The pre-splitting blasting method of igneous rock is feasible but difficult to implement accurately, resulting in unnecessary safety and environmental pollution risks. In this paper, the blasting model with penetrating structural plane and the multi-hole blasting model with different hole spacing were established based on the Riedel-Hiermaier-Thoma (RHT) damage constitutive to explore the stress wave propagation law under detonation. The damage cloud diagram and damage degree algorithm were used to quantitatively describe the spatio-temporal evolution of blasting damage. The results show that the explosion stress wave presents a significant reflection stretching effect under the action of the structural plane, which can effectively aggravate the presplitting blasting degree of the rock mass inside the structural plane. The damage range of rock mass is synchronously evolved with the change of blasting hole spacing. The blasting in the igneous rock intrusion area of the 21,914 working face is taken as an application example, and the damage degree of rock mass is reasonably evaluated by the box-counting dimension and K-means clustering method, which proves the effectiveness of the blasting scheme and provides reference value for the implementation of related blasting projects.

Clustering analysis for classifying fake real estate listings.

With the rapid growth of online property rental and sale platforms, the prevalence of fake real estate listings has become a significant concern. These deceptive listings waste time and effort for buyers and sellers and pose potential risks. Therefore, developing effective methods to distinguish genuine from fake listings is crucial. Accurately identifying fake real estate listings is a critical challenge, and clustering analysis can significantly improve this process. While clustering has been widely used to detect fraud in various fields, its application in the real estate domain has been somewhat limited, primarily focused on auctions and property appraisals. This study aims to fill this gap by using clustering to classify properties into fake and genuine listings based on datasets curated by industry experts. This study developed a K-means model to group properties into clusters, clearly distinguishing between fake and genuine listings. To assure the quality of the training data, data pre-processing procedures were performed on the raw dataset. Several techniques were used to determine the optimal value for each parameter of the K-means model. The clusters are determined using the Silhouette coefficient, the Calinski-Harabasz index, and the Davies-Bouldin index. It was found that the value of cluster 2 is the best and the Camberra technique is the best method when compared to overlapping similarity and Jaccard for distance. The clustering results are assessed using two machine learning algorithms: Random Forest and Decision Tree. The observational results have shown that the optimized K-means significantly improves the accuracy of the Random Forest classification model, boosting it by an impressive 96%. Furthermore, this research demonstrates that clustering helps create a balanced dataset containing fake and genuine clusters. This balanced dataset holds promise for future investigations, particularly for deep learning models that require balanced data to perform optimally. This study presents a practical and effective way to identify fake real estate listings by harnessing the power of clustering analysis, ultimately contributing to a more trustworthy and secure real estate market.

Exploring the impacts of risk factors on mortality patterns of global Alzheimer's disease and related dementias from 1990 to 2021.

Alzheimer's Disease and Related Dementias (ADRD) affect millions of people worldwide, with mortality rates influenced by several risk factors and exhibiting significant heterogeneity across geographical regions. This study aimed to investigate the impact of risk factors on global ADRD mortality patterns from 1990 to 2021, utilizing clustering and modeling techniques. Data on ADRD mortality rates, cardiovascular disease, and diabetes prevalence were obtained for 204 countries from the GBD platform. Additional variables such as HDI, life expectancy, alcohol consumption, and tobacco use prevalence were sourced from the UNDP and WHO. All the data were extracted for men, women, and the overall population. Longitudinal k-means clustering and generalized estimating equations were applied for data analysis. The findings revealed that cardiovascular disease had significant positive effects of 1.84, 3.94, and 4.70 on men, women, and the overall ADRD mortality rates, respectively. Tobacco showed positive effects of 0.92, 0.13, and 0.39, while alcohol consumption had negative effects of - 0.59, - 9.92, and - 2.32, on men, women, and the overall ADRD mortality rates, respectively. The countries were classified into five distinct subgroups. Overall, cardiovascular disease and tobacco use were associated with increased ADRD mortality rates, while moderate alcohol consumption exhibited a protective effect. Notably, tobacco use showed a protective effect in cluster A, as did alcohol consumption in cluster B. The effects of risk factors on ADRD mortality rates varied among the clusters, highlighting the need for further investigation into the underlying causal factors.

The use of Guyton's approach to the control of cardiac output for clinical fluid management.

Infusion of fluids is one of the most common medical acts when resuscitating critically ill patients. However, fluids most often are given without consideration of how fluid infusion can actually improve tissue perfusion. Arthur Guyton's analysis of the circulation was based on how cardiac output is determined by the interaction of the factors determining the return of blood to the heart, i.e. venous return, and the factors that determine the output from the heart, i.e. pump function. His theoretical approach can be used to understand what fluids can and cannot do. In his graphical analysis, right atrial pressure (RAP) is at the center of this interaction and thus indicates the status of these two functions. Accordingly, trends in RAP and cardiac output (or a surrogate of cardiac output) can provide important guides for the cause of a hemodynamic deterioration, the potential role of fluids, the limits of their use, and when the fluid is given, the response to therapeutic interventions. Use of the trends in these values provide a physiologically grounded approach to clinical fluid management.

Cancer-associated fibroblasts (CAFs) gene signatures predict outcomes in breast and prostate tumor patients.

BACKGROUND: Over the last two decades, tumor-derived RNA expression signatures have been developed for the two most commonly diagnosed tumors worldwide, namely prostate and breast tumors, in order to improve both outcome prediction and treatment decision-making. In this context, molecular signatures gained by main components of the tumor microenvironment, such as cancer-associated fibroblasts (CAFs), have been explored as prognostic and therapeutic tools. Nevertheless, a deeper understanding of the significance of CAFs-related gene signatures in breast and prostate cancers still remains to be disclosed. METHODS: RNA sequencing technology (RNA-seq) was employed to profile and compare the transcriptome of CAFs isolated from patients affected by breast and prostate tumors. The differentially expressed genes (DEGs) characterizing breast and prostate CAFs were intersected with data from public datasets derived from bulk RNA-seq profiles of breast and prostate tumor patients. Pathway enrichment analyses allowed us to appreciate the biological significance of the DEGs. K-means clustering was applied to construct CAFs-related gene signatures specific for breast and prostate cancer and to stratify independent cohorts of patients into high and low gene expression clusters. Kaplan-Meier survival curves and log-rank tests were employed to predict differences in the outcome parameters of the clusters of patients. Decision-tree analysis was used to validate the clustering results and boosting calculations were then employed to improve the results obtained by the decision-tree algorithm. RESULTS: Data obtained in breast CAFs allowed us to assess a signature that includes 8 genes (ITGA11, THBS1, FN1, EMP1, ITGA2, FYN, SPP1, and EMP2) belonging to pro-metastatic signaling routes, such as the focal adhesion pathway. Survival analyses indicated that the cluster of breast cancer patients showing a high expression of the aforementioned genes displays worse clinical outcomes. Next, we identified a prostate CAFs-related signature that includes 11 genes (IL13RA2, GDF7, IL33, CXCL1, TNFRSF19, CXCL6, LIFR, CXCL5, IL7, TSLP, and TNFSF15) associated with immune responses. A low expression of these genes was predictive of poor survival rates in prostate cancer patients. The results obtained were significantly validated through a two-step approach, based on unsupervised (clustering) and supervised (classification) learning techniques, showing a high prediction accuracy (≥ 90%) in independent RNA-seq cohorts. CONCLUSION: We identified a huge heterogeneity in the transcriptional profile of CAFs derived from breast and prostate tumors. Of note, the two novel CAFs-related gene signatures might be considered as reliable prognostic indicators and valuable biomarkers for a better management of breast and prostate cancer patients.

Investigating the effect of prescribing status and patient characteristics on the therapeutic outcomes in patients with diabetes using a leftover drug adjustment protocol.

Treatment for diabetes includes anti-diabetic medication in addition to lifestyle improvements through diet and exercise. In Japan, protocol-based pharmacotherapy management allows drug treatment to be provided through cooperation between physicians and pharmacists, based on a protocol that is prepared and agreed upon in advance. However, there are no studies to clarify the relationship between patient characteristics and therapeutic effects after pharmacist intervention in protocol-based pharmacotherapy management for patients with diabetes. Therefore, this study aimed to use protocol-based reports from pharmacies to understand the status of outpatient diabetes medication compliance. We classified patients with diabetes on the basis of patient characteristics that can be collected in pharmacies and investigated the characteristics that impacted diabetes treatment. Patients were prescribed oral anti-diabetic drugs at outpatient clinics of Hitachinaka General Hospital, Hitachi, Ltd., from April 2016 to March 2021. Survey items included patient characteristics (sex, age, number of drugs used, observed number of years of anti-diabetic drug prescription, number of anti-diabetic drug prescription days, and presence or absence of leftover anti-diabetic drugs) and HbA1c levels. Graphical analyses indicated the relationship between each categorised patient characteristic using multiple correspondence analyses. Subsequently, the patients were clustered using K-means cluster analysis based on the coordinates obtained for each patient. Patient characteristics and HbA1c values were compared between the groups for each cluster. A total of 1,910 patients were included and classified into three clusters, with clusters 1, 2, and 3 containing 625, 703, and 582 patients, respectively. Patient characteristics strongly associated with Cluster 1 were ages between 65 and 74 years, use of three or more anti-diabetic drugs, use of 3 years or more of anti-diabetic drugs, and leftover anti-diabetic drugs. Furthermore, Cluster 1 had the highest number of patients with worsening HbA1c levels compared with other clusters. Using the leftover drug adjustment protocol, we clarified the patient characteristics that affected the treatment course. We anticipate that through targeted interventions in patients exhibiting these characteristics, we can identify those who are irresponsibly continuing with drug treatment, are not responding well to therapy, or both. This could substantially improve the efficacy of their anti-diabetic care.

The pH paradox.

This paper highlights the critical role of pH or proton activity measurements in environmental studies and emphasises the importance of applying proper statistical approaches when handling pH data. This allows for more informed decisions to effectively manage environmental data such as from mining influenced water. Both the pH and {H+} of the same system display different distributions, with pH mostly displaying a normal or bimodal distribution and {H+} showing a lognormal distribution. It is therefore a challenge of whether to use pH or {H+} to compute the mean or measures of central tendency for further environmental statistical analyses. In this study, different statistical techniques were applied to understand the distribution of pH and {H+} from four different mine sites, Metsämonttu in Finland, Felsendome Rabenstein in Germany, Eastrand and Westrand mine water treatment plants in South Africa. Based on the statistical results, the geometric mean can be used to calculate the average of pH if the distribution is unimodal. For a multimodal pH data distribution, peak identifying methods can be applied to extract the mean for each data population and use them for further statistical analyses.

Unveiling metabolic patterns in dementia: Insights from high-resolution quantitative blood-oxygenation-level-dependent MRI.

BACKGROUND: Oxygen extraction fraction (OEF) and deoxyhemoglobin (DoHb) levels reflect variations in cerebral oxygen metabolism in demented patients. PURPOSE: Delineating the metabolic profiles evident throughout different phases of dementia necessitates an integrated analysis of OEF and DoHb levels. This is enabled by leveraging high-resolution quantitative blood oxygenation level dependent (qBOLD) analysis of magnitude images obtained from a multi-echo gradient-echo MRI (mGRE) scan performed on a 3.0 Tesla scanner. METHODS: Achieving superior spatial resolution in qBOLD necessitates the utilization of an mGRE scan with only four echoes, which in turn limits the number of measurements compared to the parameters within the qBOLD model. Consequently, it becomes imperative to discard non-essential parameters to facilitate further analysis. This process entails transforming the qBOLD model into a format suitable for fitting the log-magnitude difference (L-MDif) profiles of the four echo magnitudes present in each brain voxel. In order to bolster spatial specificity, the log-difference qBOLD model undergoes refinement into a representative form, termed as r-qBOLD, particularly when applied to class-averaged L-MDif signals derived through k-means clustering of L-MDif signals from all brain voxels into a predetermined number of clusters. The agreement between parameters estimated using r-qBOLD for different cluster sizes is validated using Bland-Altman analysis, and the model's goodness-of-fit is evaluated using a χ 2 ${\chi ^2}$ -test. Retrospective MRI data of Alzheimer's disease (AD), mild cognitive impairment (MCI), and non-demented patients without neuropathological disorders, pacemakers, other implants, or psychiatric disorders, who completed a minimum of three visits prior to MRI enrolment, are utilized for the study. RESULTS: Utilizing a cohort comprising 30 demented patients aged 65-83 years in stages 4-6 representing mild, moderate, and severe stages according to the clinical dementia rating (CDR), matched with an age-matched non-demented control group of 18 individuals, we conducted joint observations of OEF and DoHb levels estimated using r-qBOLD. The observations elucidate metabolic signatures in dementia based on OEF and DoHb levels in each voxel. Our principal findings highlight the significance of spatial patterns of metabolic profiles (metabolic patterns) within two distinct regimes: OEF levels exceeding the normal range (S1-regime), and OEF levels below the normal range (S2-regime). The S1-regime, accompanied by low DoHb levels, predominantly manifests in fronto-parietal and perivascular regions with increase in dementia severity. Conversely, the S2-regime, accompanied by low DoHb levels, is observed in medial temporal (MTL) regions. Other regions with abnormal metabolic patterns included the orbitofrontal cortex (OFC), medial-orbital prefrontal cortex (MOPFC), hypothalamus, ventro-medial prefrontal cortex (VMPFC), and retrosplenial cortex (RSP). Dysfunction in the OFC and MOPFC indicated cognitive and emotional impairment, while hypothalamic involvement potentially indicated preclinical dementia. Reduced metabolic activity in the RSP suggested early-stage AD related functional abnormalities. CONCLUSIONS: Integrated analysis of OEF and DoHb levels using r-qBOLD reveals distinct metabolic signatures across dementia phases, highlighting regions susceptible to neuronal loss, vascular involvement, and preclinical indicators.

Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework.

In hazardous environments like mining sites, mobile inspection robots play a crucial role in condition monitoring (CM) tasks, particularly by collecting various kinds of data, such as images. However, the sheer volume of collected image samples and existing noise pose challenges in processing and visualizing thermal anomalies. Recognizing these challenges, our study addresses the limitations of industrial big data analytics for mobile robot-generated image data. We present a novel, fully integrated approach involving a dimension reduction procedure. This includes a semantic segmentation technique utilizing the pre-trained VGG16 CNN architecture for feature selection, followed by random forest (RF) and extreme gradient boosting (XGBoost) classifiers for the prediction of the pixel class labels. We also explore unsupervised learning using the PCA-K-means method for dimension reduction and classification of unlabeled thermal defects based on anomaly severity. Our comprehensive methodology aims to efficiently handle image-based CM tasks in hazardous environments. To validate its practicality, we applied our approach in a real-world scenario, and the results confirm its robust performance in processing and visualizing thermal data collected by mobile inspection robots. This affirms the effectiveness of our methodology in enhancing the overall performance of CM processes.

Three-Dimensional Segmentation of Equine Paranasal Sinuses in Multidetector Computed Tomography Datasets: Preliminary Morphometric Assessment Assisted with Clustering Analysis.

The paranasal sinuses, a bilaterally symmetrical system of eight air-filled cavities, represent one of the most complex parts of the equine body. This study aimed to extract morphometric measures from computed tomography (CT) images of the equine head and to implement a clustering analysis for the computer-aided identification of age-related variations. Heads of 18 cadaver horses, aged 2-25 years, were CT-imaged and segmented to extract their volume, surface area, and relative density from the frontal sinus (FS), dorsal conchal sinus (DCS), ventral conchal sinus (VCS), rostral maxillary sinus (RMS), caudal maxillary sinus (CMS), sphenoid sinus (SS), palatine sinus (PS), and middle conchal sinus (MCS). Data were grouped into young, middle-aged, and old horse groups and clustered using the K-means clustering algorithm. Morphometric measurements varied according to the sinus position and age of the horses but not the body side. The volume and surface area of the VCS, RMS, and CMS increased with the age of the horses. With accuracy values of 0.72 for RMS, 0.67 for CMS, and 0.31 for VCS, the possibility of the age-related clustering of CT-based 3D images of equine paranasal sinuses was confirmed for RMS and CMS but disproved for VCS.

Using Clustering Methods to Map the Experience Profiles of Dementia Caregivers.

Background and Objectives: Caregivers of persons living with dementia report wide-ranging lived experiences, including feelings of burden and frustration but also positivity about caregiving. This study applies clustering methodology to novel survey data to explore variation in caregiving experience profiles, which could then be used to design and target caregiver interventions aimed at improving caregiver well-being. Research Design and Methods: The k-means clustering algorithm partitioned a sample of 81 caregivers from the Midwest region of the United States on the basis of 8 variables capturing caregiver emotions, attitudes, knowledge, and network perceptions (adversity: burden, anxiety, network malfeasance; network nonfeasance; positivity: positive aspects of caregiving, preparedness and confidence in community-based care, knowledge about community services for older adults, and network uplift). The experience profile of each segment is described qualitatively and then regression methods were used to examine the association between (a) experience profiles and caregiver demographic characteristics and (b) experience profiles and study attrition. Results: The clustering algorithm identified 4 segments of caregivers with distinct experience profiles: Thriving (low adversity, high positivity); Struggling with Network (high network malfeasance); Intensely Struggling (high adversity, low positivity); Detached (unprepared, disconnected, but not anxious). Experience profiles were associated with significantly different demographic profiles and attrition rates. Discussion and Implications: How caregivers respond to support interventions may be contingent on caregivers' experience profile. Research and practice should focus on identifying public health strategies tailored to fit caregiver experiences. Clinical Trial Registration: NCT03932812.

Reducing firearm access for youth at risk for suicide in a pediatric emergency department.

Background: Firearm-related suicide is the second leading cause of pediatric firearm death. Lethal means counseling (LMC) can improve firearm safe-storage practices for families with youth at risk of suicide. Objectives: This study aims to evaluate the feasibility of pediatric emergency department (ED) behavioral mental health (BMH) specialists providing LMC to caregivers of youth presenting with BMH complaints and to test for changes in firearm safety practices, pre-post ED LMC intervention, as measures of preliminary efficacy. Methods: Prospective pilot feasibility study of caregivers of youth presenting to a pediatric ED with BMH complaints. Caregivers completed an electronic survey regarding demographics and firearm safe-storage knowledge/practices followed by BMH specialist LMC. Firearm owners were offered a free lockbox and/or trigger lock. One-week follow-up surveys gathered self-reported data on firearm safety practices and intervention acceptability. One-month interviews with randomly sampled firearm owners collected additional firearm safety data. Primary outcomes were feasibility measures, including participant accrual/attrition and LMC intervention acceptability. Secondary outcomes included self-reported firearm safety practice changes. Feasibility benchmarks were manually tabulated, and Likert-scale acceptability responses were dichotomized to strongly agree/agree vs. neutral/disagree/strongly disagree. Descriptive statistics were used for univariate and paired data responses. Results: In total, 81 caregivers were approached; of which, 50 (81%) caregivers enrolled. A total of 44% reported having a firearm at home, 80% completed follow-up at one week. More than 80% affirmed that ED firearm safety education was useful and that the ED is an appropriate place for firearm safety discussions. In total, 58% of participants reported not having prior firearm safety education/counseling. Among firearm owners (n = 22), 18% reported rarely/never previously using a safe-storage device, and 59% of firearm owners requested safe storage devices.At 1-week follow-up (n = 40), a greater proportion of caregivers self-reported asking about firearms before their child visited other homes (+28%). Among firearm owners that completed follow-up (n = 19), 100% reported storing all firearms locked at one week (+23% post-intervention). In total, 10 caregivers reported temporarily/permanently removing firearms from the home. Conclusion: It is feasible to provide LMC in the pediatric ED via BMH specialists to families of high-risk youth. Caregivers were receptive to LMC and reported finding this intervention useful, acceptable, and appropriate. Additionally, LMC and device distribution led to reported changes in safe storage practices.

Farm specific estimates of forage variability result in larger optimal control limits for forage quality monitoring.

Variation in forage composition decreases the accuracy of diets delivered to dairy cows. However, variability of forages can be managed using a renewal reward model (RRM) and genetic algorithm (GA) to optimize sampling and monitoring practices for farm conditions. Specifically, use of quality-control-charts to monitor forage composition can identify changes in composition for which adjustment in the formulated diet will result in a better match of the nutrients delivered to cows. The objectives of this study were 1) assess the use of a clustering algorithm to estimate the mean time the process is stable or in-control (d) (TStable) and the magnitude of the change in forage composition between stable periods (ΔForage) for corn silage and alfalfa-grass silage which are input parameters for the RRM; 2) compare optimized farm-specific sampling practices (number of samples (n), sampling interval (TSample) and control limits (ΔLimit) using previously proposed defaults and our estimates for the TStable and ΔForage input parameters; and 3) conduct a simulation study to compare the number of recommended diet changes costs of quality control under the proposed sampling and monitoring protocols. We estimated the TStable and ΔForage parameters for corn silage NDF and starch and alfalfa-grass silage NDF and CP using a k-means clustering approach applied to forage samples collected from 8 farms, 3x/week during a 16-week period. We compared 4 sampling and monitoring protocols that resulted from the 2 methods for estimating TStable and ΔForage (default values and our proposed method) and either optimizing only the control limit (Optim1) or optimizing the control limits, the number of samples, and the number of days between sampling (Optim2). We simulated the outcomes of implementing the optimized monitoring protocols using a quality control chart for corn silage and alfalfa-grass silage of each farm. Estimates of T^Stable and Δ^Forage from the k-means clustering analysis were, respectively, shorter and larger than previously proposed default values. In the simulated quality control monitoring, larger Δ^Forage estimates increased the optimized ΔLimit resulting in fewer detected shifts in composition of forages and a lower frequency of false alarms and a lower quality control cost ($/d). Recommended diet reformulation intervals from the simulated quality control analysis were specific for the type of forage and farm management practices. The median of the diet reformulation intervals for all farms using our optimal protocols was 14 d (Q1 = 8, Q3 = 26) for corn silage and 16 d (Q1 = 8, Q3 = 26) for alfalfa-grass silage.

Lifestyle trajectories in middle-aged adults and their relationship with health indicators.

Introduction: Understanding the impact of different lifestyle trajectories on health preservation and disease risk is crucial for effective interventions. Methods: This study analyzed lifestyle engagement over five years in 3,013 healthy adults aged 40-70 from the Barcelona Brain Health Initiative using K-means clustering. Nine modifiable risk factors were considered, including cognitive, physical, and social activity, vital plan, diet, obesity, smoking, alcohol consumption, and sleep. Self-reported diagnoses of new diseases at different time-points after baseline allowed to explore the association between these five profiles and health outcomes. Results: The data-driven analysis classified subjects into five lifestyle profiles, revealing associations with health behaviors and risk factors. Those exhibiting high scores in health-promoting behaviors and low-risk behaviors, demonstrate a reduced likelihood of developing diseases (p < 0.001). In contrast, profiles with risky habits showed distinct risks for psychiatric, neurological, and cardiovascular diseases. Participant's lifestyle trajectories remained relatively stable over time. Discussion: Our findings have identified risk for distinct diseases associated to specific lifestyle patterns. These results could help in the personalization of interventions based on data-driven observation of behavioral patterns and policies that promote a healthy lifestyle and can lead to better health outcomes for people in an aging society.

Changes in the combination of the triglyceride-glucose index and obesity indicators estimate the risk of cardiovascular disease.

BACKGROUND: Cardiovascular disease (CVD) is closely associated with the triglyceride glucose (TyG) index and its related indicators, particularly its combination with obesity indices. However, there is limited research on the relationship between changes in TyG-related indices and CVD, as most studies have focused on baseline TyG-related indices. METHODS: The data for this prospective cohort study were obtained from the China Health and Retirement Longitudinal Study. The exposures were changes in TyG-related indices and cumulative TyG-related indices from 2012 to 2015. The K-means algorithm was used to classify changes in each TyG-related index into four classes (Class 1 to Class 4). Multivariate logistic regressions were used to evaluate the associations between the changes in TyG-related indices and the incidence of CVD. RESULTS: In total, 3243 participants were included in this study, of whom 1761 (54.4%) were female, with a mean age of 57.62 years at baseline. Over a 5-year follow-up, 637 (19.6%) participants developed CVD. Fully adjusted logistic regression analyses revealed significant positive associations between changes in TyG-related indices, cumulative TyG-related indices and the incidence of CVD. Among these changes in TyG-related indices, changes in TyG-waist circumference (WC) showed the strongest association with incident CVD. Compared to the participants in Class 1 of changes in TyG-WC, the odds ratio (OR) for participants in Class 2 was 1.41 (95% confidence interval (CI) 1.08-1.84), the OR for participants in Class 3 was 1.54 (95% CI 1.15-2.07), and the OR for participants in Class 4 was 1.94 (95% CI 1.34-2.80). Moreover, cumulative TyG-WC exhibited the strongest association with incident CVD among cumulative TyG-related indices. Compared to the participants in Quartile 1 of cumulative TyG-WC, the OR for participants in Quartile 2 was 1.33 (95% CI 1.00-1.76), the OR for participants in Quartile 3 was 1.46 (95% CI 1.09-1.96), and the OR for participants in Quartile 4 was 1.79 (95% CI 1.30-2.47). CONCLUSIONS: Changes in TyG-related indices are independently associated with the risk of CVD. Changes in TyG-WC are expected to become more effective indicators for identifying individuals at a heightened risk of CVD.

Distinguishing risk of curve progression in adolescent idiopathic scoliosis with bone microarchitecture phenotyping - a 6-year longitudinal study.

Low bone mineral density and impaired bone qualities have been shown to be important prognostic factors for curve progression in Adolescent Idiopathic Scoliosis (AIS). There is no evidence-based integrative interpretation method to analyse high-resolution peripheral quantitative computed tomography (HR-pQCT) data in AIS. This study aimed to (a) utilize unsupervised machine learning to cluster bone microarchitecture phenotypes on HR-pQCT parameters in AIS girls, (b) assess the phenotypes' risk of curve progression and progression to surgical threshold at skeletal maturity (primary cohort), and (c) investigate risk of curve progression in a separate cohort of mild AIS girls whose curve severity did not reach bracing threshold at recruitment (secondary cohort). Patients were followed up prospectively for 6.22 ± 0.33 years in the primary cohort (N = 101). Three bone microarchitecture phenotypes were clustered by Fuzzy C-Means at time of peripubertal peak height velocity (PHV). Phenotype-1 had normal bone characteristics. Phenotype-2 was characterized by low bone volume and high cortical bone density, and Phenotype-3 had low cortical and trabecular bone density and impaired trabecular microarchitecture. The difference in bone qualities amongst the phenotypes was significant at peripubertal PHV and continued to skeletal maturity. Phenotype-3 had significantly increased risk of curve progression to surgical threshold at skeletal maturity (Odd Ratios (OR) = 4.88; 95% Confidence Interval (CI): 1.03-28.63). In the secondary cohort (N = 106), both Phenotype-2 (adjusted OR = 5.39; 95%CI: 1.47-22.76) and Phenotype-3 (adjusted OR = 3.67; 95%CI: 1.05-14.29) had increased risk of curve progression ≥6° with mean follow-up of 3.03 ± 0.16 years. In conclusion, three distinct bone microarchitecture phenotypes could be clustered by unsupervised machine learning on HR-pQCT generated bone parameters at peripubertal PHV in AIS. The bone qualities reflected by these phenotypes were found to have significant differentiating risk of curve progression and progression to surgical threshold at skeletal maturity in AIS.

Adolescent Idiopathic Scoliosis (AIS) is an abnormal spinal curvature commonly presents during puberty growth. Evidence has shown that low bone mineral density and impaired bone qualities are important risk factors for curve progression in AIS. High-resolution peripheral quantitative computed tomography (HR-pQCT) has improved our understanding of bone qualities in AIS. It generates a large amount of quantitative and qualitative bone parameters from a single measurement, but the data are not easy for clinicians to interpret and analyse. This study enrolled AIS girls and used unsupervised machine learning model to analyse their HR-pQCT data at first clinic visit. The model clustered the patients into 3 bone microarchitecture phenotypes (i.e. Phenotype-1: normal, Phenotype-2: low bone volume and high cortical bone density, and Phenotype-3: low cortical and trabecular bone density and impaired trabecular microarchitecture). They were longitudinally followed up for 6 years until skeletal maturity. We observed the three phenotypes were persistent, and Phenotype-3 had a significantly increased risk of curve progression to severity that requires invasive spinal surgery (Odds Ratio = 4.88, P = 0.029). The difference in bone qualities reflected by these 3 distinct phenotypes could aid clinicians to differentiate risk of curve progression and surgery at early stages of AIS.