Outcomes of Distal Third Femur Fractures in Patients 18 Years and Older: A Pilot Study.

INTRODUCTION: The selection of the most optimal fixation method for fractures of the distal femur, whether intramedullary nail (NL), lateral locking plate (PL), or nail/plate (NP) is not always clear. This study retrospectively evaluates surgical patients with distal femur fractures and introduces a pilot study using cluster analysis to identify the most optimal fracture fixation method for a given fracture type. METHODS: This is a retrospective cohort study of patients 18 years and older with an isolated distal femur fracture who presented to our Level-1 trauma center between January 1, 2012, and December 31, 2022, and obtained NL, PL, or NP implants. Patients with polytrauma and those without at least six months of follow-up were excluded. A chart review was used to obtain demographics, fracture classification, fixation method, and postoperative complications. A cluster analysis was performed. The following factors were used to determine a successful outcome: ambulatory status pre-injury and 6-12 months postoperatively, infection, non-union, mortality, and implant failure. RESULTS: A total of 169 patients met inclusion criteria. No statistically significant association between the fracture classification and fixation type with overall outcome was found. However, patients treated with an NP (n = 14) had a success rate of 92.9% vs only a 68.1% success rate in those treated with a PL (n = 116) (p = 0.106). The most notable findings in the cluster analysis (15 total clusters) included transverse extraarticular fractures demonstrating 100% success if treated with NP (n = 6), 50% success with NL (n=2), and 78.57% success with PL fixation (n=14). NP constructs in complete articular fractures demonstrated success in 100% of patients (n = 5), whereas 77.78% of patients treated with NL (n = 9) and 61.36% of those treated with PL (n = 44). CONCLUSIONS: Plate fixation was the predominant fixation method used for distal third femur fractures regardless of fracture classification. However, NP constructs trended towards improved success rates, especially in complete intraarticular and transverse extraarticular fractures, suggesting the potential benefit of additional fixation with these fractures. Cluster analysis provided a heuristic way of creating patient profiles in patients with distal third femur fractures. However, a larger cohort study is needed to corroborate these findings to ultimately develop a clinical decision-making tool that also accounts for patient specific characteristics.

A clinical prognostic model of oxidative stress-related genes linked to tumor immune cell infiltration and the prognosis of ovarian cancer patients.

Background: According to statistics, ovarian cancer (OV) is the most prevalent type of gynecologic malignancy and has the highest mortality rate of all gynecologic tumors. Although several studies have shown that oxidative stress (OS) contributes significantly to the onset and progression of cancer, the role of OS in OV needs to be investigated further. Thus, it is critical to comprehend the function of OS-related genes in OV. Methods: In this study, all data related to the transcriptome and clinical status of the patients were retrieved from "The Cancer Genome Atlas" (TCGA) and "Gene Expression Omnibus" (GEO) databases. Using the unsupervised cluster analysis technique, all patients with OV were classified into two different subtypes (categories) based on the OS gene. All hub genes were screened using the weighted gene co-expression network analysis (WGCNA). Since the hub genes and the differentially expressed genes (DEGs) in both categories were found to intersect, the univariate Cox regression analysis was implemented. A multivariate Cox analysis was also performed to construct a novel clinical prognosis model, which was validated using data from the GEO cohort. In addition, the relationship between risk score and immune cell infiltration level was evaluated using CIBERSORT. Finally, qRT-PCR was used to confirm the expression of the genes used to construct the model. Results: Two subtypes of OS were obtained. The findings indicated that OS-C1 had a better survival outcome than OS-C2. The results of WGCNA yielded 112 hub genes. For univariate COX regression analyses, 49 OS-related trait genes were obtained. Finally, a clinical prognostic model containing two genes was constructed. This model could differentiate between patients with OV having varying years of survival in the TCGA and GEO cohorts. The model risk score was verified as an independent prognostic indicator. According to the results of CIBERSORT, many tumor-infiltrating immune cells were found to be significantly related to the risk score. Furthermore, the results revealed that patients with low-risk OV in the CTLA4 treatment group had a high likelihood of benefiting from immunotherapy. qRT-PCR results also showed that the expression of MARVELD1 and VSIG4 was high in the OV samples. Conclusions: Analysis of the results suggested that the newly developed model, which contained two characteristic OS-related genes, could successfully predict the survival outcomes of all patients with OV. The findings of this study could offer valuable information and insights into the refinement of personalized therapy and immunotherapy for OV in the future.

Suspected serial killers and unsuspected statistical blunders.

A whole branch of theoretical statistics devotes itself to the analysis of clusters, the aim being to distinguish an apparent cluster arising randomly from one that is more likely to have been produced as a result of some systematic influence. There are many examples in medicine and some that involve both medicine and the legal field; criminal law in particular. Observed clusters or a series of cases in a given setting can set off alarm bells, the recent conviction of Lucy Letby in England being an example. It was an observed cluster, a series of deaths among neonates, that prompted the investigation of Letby. There have been other similar cases in the past and there will be similar cases in the future. Our purpose is not to reconsider any particular trial but, rather, to work with similar, indeed more extreme numbers of cases as a way to underline the statistical mistakes that can be made when attempting to make sense of the data. These notions are illustrated via a made-up case of 10 incidents where the anticipated count was only 2. The most common statistical analysis would associate a probability of less than 0.00005 with this outcome: A very rare event. However, a more careful analysis that avoids common pitfalls results in a probability close to 0.5, indicating that, given the circumstances, we were as likely to see 10 or more as we were to see less than 10.

Distinct profiles of mental health need and high need overall among New Zealand adolescents - Cluster analysis of population survey data.

OBJECTIVE: The objective was to identify clinically meaningful groups of adolescents based on self-reported mental health and wellbeing data in a population sample of New Zealand secondary school students. METHODS: We conducted a cluster analysis of six variables from the Youth19 Rangatahi Smart Survey (n = 7721, ages 13-18 years, 2019): wellbeing (World Health Organization Well-Being Index), possible anxiety symptoms (Generalized Anxiety Disorder 2-item, adapted), depression symptoms (short form of the Reynolds Adolescent Depression Scale) and past-year self-harm, suicide ideation and suicide attempt. Demographic, contextual and behavioural predictors of cluster membership were determined through multiple discriminant function analysis. We performed cross-validation analyses using holdout samples. RESULTS: We identified five clusters (n = 7083). The healthy cluster (n = 2855, 40.31%) reported positive mental health across indicators; the anxious cluster (n = 1994, 28.15%) reported high possible anxiety symptoms and otherwise generally positive results; the stressed and hurting cluster (n = 667, 9.42%) reported sub-clinical depression and possible anxiety symptoms and some self-harm; the distressed and ideating cluster (n = 1116, 15.76%) reported above-cutoff depression and possible anxiety symptoms and high suicide ideation; and the severe cluster (n = 451; 6.37%) reported the least positive mental health across indicators. Female, rainbow, Maori and Pacific students and those in higher deprivation areas were overrepresented in higher severity clusters. Factors including exposure to sexual harm and discrimination were associated with increasing cluster severity. CONCLUSION: We identified high prevalence of mental health challenges among adolescents, with distinct clusters of need. Youth mental health is not 'one size fits all'. Future research should explore youth behaviour and preferences in accessing support and consider how to best support the mental health of each cluster.

Assessment of Acute Mountain Sickness: Comparing the Chinese AMS Score to the Lake Louise Score.

Wu, Yu, Wenqi Zhao, Bao Liu, Jianyang Zhang, Zhifeng Zhong, Simin Zhou, Jiaxin Xie, Yuqi Gao, Peng Li, and Jian Chen. Assessment of Acute Mountain Sickness: Comparing the Chinese Ams Score to the Lake Louise Score. High Alt Med Biol 00:000-000, 2024. Objective: To compare the ability of the Chinese AMS Score (CAS) to detect acute mountain sickness (AMS) using the 2018 version of the Lake Louise Score (LLS) as reference. Methods: After flying from Chengdu (altitude: 500 m) to Lhasa (3,658 m), 2,486 young men completed a questionnaire. The questionnaire contained LLS and CAS items. An LLS ≥3 and/or a CAS ≥cutoff were used as the criteria for AMS. Hierarchical cluster analysis and two-step cluster analysis were used to investigate relationships between the symptoms. Results: AMS incidence rates were 33.8% (n = 840) with the LLS and 59.3% (n = 1,473) with the CAS (χ2 = 872.5, p < 0.001). The LLS and CAS had a linear relationship (orthogonal regression, Pearson r = 0.91, p < 0.001). With the LLS as the standard, the CAS had high diagnostic accuracy (area under the curve = 0.95, 95% confidence interval: 0.94-0.96). However, with the CAS, 25.5% (n = 633) more participants were labeled as having AMS than with the LLS (false positives). Two clusters were identified: one with headache only (419 participants, 66.2%) and one without headache but with other symptoms (214 participants, 33.8%). Reducing the weight of headache in the CAS allowed to align CAS and LLS. Conclusion: In comparison to the LLS, the CAS has a sensitivity close to 100% but lacks specificity given the high rate of false positives. The different weight of headaches may be the main reason for the discrepancy.

Sociodemographic factor associations with maternal and placental outcomes: A cluster and partial least squares regression analysis.

INTRODUCTION: Maternal social disadvantage adversely affects maternal and offspring health, with limited research on placental outcomes. Therefore, we examined maternal sociodemographic factor associations with placental and birth outcomes in general (Lifeways Cross-Generation Cohort) and at-risk (PEARS Study of mothers with overweight or obesity) populations of pregnant women. METHODS: TwoStep cluster analysis profiled Lifeways mothers (n = 250) based on their age, parity, marital status, household income, private healthcare insurance, homeowner status, and education. Differences in placental and birth outcomes (untrimmed placental weight (PW), birthweight (BW) and BW:PW ratio) between clusters were assessed using one-way ANOVA and chi-square tests. Partial least squares regression analysed individual effects of sociodemographic factors on placental and birth outcomes in Lifeways and PEARS mothers (n = 461). RESULTS: Clusters were classified as "Married Homeowners" (n = 140, 56 %), "Highest Income" (n = 58, 23.2 %) and "Renters" (n = 52, 20.8 %) in the Lifeways Cohort. Renters were younger, more likely to smoke, have a means-tested medical card and more pro-inflammatory diets compared to other clusters (p < 0.01). Compared to Married Homeowners, renters' offspring had lower BW (-259.26 g, p < 0.01), shorter birth length (-1.31 cm, p < 0.01) and smaller head circumference (-0.59 cm, p = 0.02). PLS regression analyses identified nulliparity as having the greatest negative effect on PW (Lifeways and PEARS) while being a homeowner had the greatest positive effect on PW (Lifeways). CONCLUSION: Certain combinations of sociodemographic factors (particularly homeownership) were associated with less favourable lifestyle factors, and with birth, but not placental outcomes. When explored individually, parity contributed to the prediction of placental and birth outcomes in both cohorts of pregnant women.

Grain nutritional and antioxidant profiling of diverse lentil (Lens culinaris Medikus) genetic resources revealed genotypes with high nutritional value.

The lentil (Lens culinaris Medikus ssp. Culinaris) is a self-pollinating, diploid (2n = 2X = 14) crop with a genome size of 4 Gbp. The present study was conducted to provide a database for the evaluation of lentil antioxidant capacity, nutritional quality, and biochemical attributes. For these purposes, lentil germplasm, including 100 exotic and local genotypes from different agro-climatic zones of Pakistan, was collected. Significant variation (p < 0.05) was found among the genotypes under investigation using the Tukey HSD test. Ascorbate peroxidase was highest in ALTINOPARK (2,465 Units/g s. wt.), catalase in LPP 12110 (5,595 Units/g s. wt.), superoxide dismutase in LPP 12105 (296.75 Units/g s. wt.), and peroxidase in NIAB Masoor 2002 (3,170 Units/g s. wt.). Furthermore, NLM 15016 had a maximum total antioxidant capacity of 15.763 mg/g s. wt. The maximum values of total soluble sugars (83.93 mg/g. s. wt.) and non-reducing sugars (74.79 mg/g. s. wt.) were noticed in NLM 15015. The highest reducing sugars were detected in ILL 8006 (45.68 mg/g. s. wt.) ascorbic acid in LPP 12182 (706 µg/g s. wt.), total phenolic content in NLI 17003 (54,600 µM/g s. wt.), and tannins in NLI 17057 (24,563 µM/g s. wt.). The highest chlorophyll a (236.12 µg/g s. wt.), chlorophyll b (317 µg/g s. wt.), total chlorophyll (552.58 µg/g s. wt.), and lycopene (10.881 µg/g s. wt.) were found in NLH 12097. Maximum total carotenoids were revealed in the local approved variety Markaz 2009 (17.89 µg/g s. wt.). Principal component analysis (PCA), correlation analysis (Pearson's test), and agglomerative hierarchical clustering (AHC) were performed to detect the extent of variation in genotypes. In cluster analysis, all genotypes were categorized into three clusters. Cluster II genotypes showed remarkable divergence with cluster III. According to PCA, the contribution of PC-I regarding tested nutritional parameters toward variability was the highest (39.75%) and indicated positive factor loading for the tested nutritional and biochemical parameters. In conclusion, genotype X 2011S 33-34-32 can be used by the food industry in making pasta, multigrain bread, and snacking foods due to its high protein content for meat alternative seekers. Identified genotypes with high nutritional attributes can be utilized to improve quality parameters in the respective lentil breeding lines.

Eating Habits and Lifestyle Factors Related to Childhood Obesity Among Children Aged 5-6 Years: Cluster Analysis of Panel Survey Data in Korea.

BACKGROUND: Childhood obesity has emerged as a major health issue due to the rapid growth in the prevalence of obesity among young children worldwide. Establishing healthy eating habits and lifestyles in early childhood may help children gain appropriate weight and further improve their health outcomes later in life. OBJECTIVE: This study aims to classify clusters of young children according to their eating habits and identify the features of each cluster as they relate to childhood obesity. METHODS: A total of 1280 children were selected from the Panel Study on Korean Children. Data on their eating habits (eating speed, mealtime regularity, consistency of food amount, and balanced eating), sleep hours per day, outdoor activity hours per day, and BMI were obtained. We performed a cluster analysis on the children's eating habits using k-means methods. We conducted ANOVA and chi-square analyses to identify differences in the children's BMI, sleep hours, physical activity, and the characteristics of their parents and family by cluster. RESULTS: At both ages (ages 5 and 6 years), we identified 4 clusters based on the children's eating habits. Cluster 1 was characterized by a fast eating speed (fast eaters); cluster 2 by a slow eating speed (slow eaters); cluster 3 by irregular eating habits (poor eaters); and cluster 4 by a balanced diet, regular mealtimes, and consistent food amounts (healthy eaters). Slow eaters tended to have the lowest BMI (P<.001), and a low proportion had overweight and obesity at the age of 5 years (P=.03) and 1 year later (P=.005). There was a significant difference in sleep time (P=.01) and mother's education level (P=.03) at the age of 5 years. Moreover, there was a significant difference in sleep time (P=.03) and the father's education level (P=.02) at the age of 6 years. CONCLUSIONS: Efforts to establish healthy eating habits in early childhood may contribute to the prevention of obesity in children. Specifically, providing dietary guidance on a child's eating speed can help prevent childhood obesity. This research suggests that lifestyle modification could be a viable target to decrease the risk of childhood obesity and promote the development of healthy children. Additionally, we propose that future studies examine long-term changes in obesity resulting from lifestyle modifications in children from families with low educational levels.

Fluorescent multichannel sensor array based on three carbon dots derived from Tibetan medicine waste for the quantification and discrimination of multiple heavy metal ions in water.

A fluorescent multichannel sensor array has been established based on three carbon dots derived from Tibetan medicine waste for rapid quantification and discrimination of six heavy metal ions. Due to the chelation between metal ions and carbon dots (CDs), this fluorescence "turn off" mode sensing array can quantify six metal ions as low as "µM" level. Moreover, the six heavy metal ions display varying quenching effects on these three CDs owing to diverse chelating abilities between each other, producing differential fluorescent signals for three sensing channels, which can be plotted as specific fingerprints and converted into intuitive identification profiles via principal component analysis (PCA) and hierarchical cluster analysis (HCA) technologies to accurately distinguish Cu2+, Fe3+, Mn2+, Ag+, Ce4+, and Ni2+ with the minimum differentiated concentration of 5 µM. Valuably, this sensing array unveils good sensitivity, exceptional selectivity, ideal stability, and excellent anti-interference ability for both mixed standards and actual samples. Our contribution provides a novel approach for simultaneous determination of multiple heavy metal ions in environmental samples, and it will inspire the development of other advanced optical sensing array for simultaneous quantification and discrimination of multiple targets.

Screening of Rice (Oryza sativa L.) Genotypes for Salinity Tolerance and Dissecting Determinants of Tolerance Mechanism.

Soil salinity imposes osmotic, ionic, and oxidative stresses on plants, resulting in growth inhibition, developmental changes, metabolic adaptations, and ion sequestration or exclusion. Identifying salinity-tolerant resources and understanding physiological and molecular mechanisms of salinity tolerance could lay a foundation for the improvement of salinity tolerance in rice. In this study, a series of salinity-tolerance-related morphological and physiological traits were investigated in 46 rice genotypes, including Sea Rice 86, to reveal the main strategies of rice in responding to salinity stress at the seedling stage. No genotypes showed the same tolerance level as the two landraces Pokkali and Nona Bokra, which remain the donors for improving the salinity tolerance of rice. However, due to undesirable agronomic traits of these donors, alternative cultivars such as JC118S and R1 are recommended as novel source of salinity tolerance. Correlation and principal component analyses revealed that the salinity tolerance of rice seedlings is not only controlled by growth vigor but also regulated by ion transport pathways such as long-distance Na+ transport, root Na+ sequestration, and root K+ retention. Therefore, such key traits should be targeted in future breeding programs as the strategy of obtaining better Na+ exclusion is still the bottleneck for improving salinity tolerance in rice.

Role and limitations of the in-store waste collection system at supermarkets.

In-store collection is defined as the activity of installing collection boxes at retail stores, such as supermarkets, for the collection of recyclables. The use of in-store collection reduces the burden of garbage collection in municipalities, which may reduce administrative and environmental burdens and costs. Previous discussions on in-store collection have ignored environmental impacts and the costs to consumers and that supermarkets should become players in the collection of recyclables. Therefore, it is necessary to clarify whether the use of in-store collection effectively contributes to a reduction in environmental burdens and costs for society. This study aimed to analyze the environmental burden and costs associated with integrating in-store collection into municipal solid waste (MSW) management systems. A total of 1734 municipalities in Japan were classified into six clusters using cluster analysis to analyze the characteristics of municipal and in-store collection by municipality. Model cities representing each cluster were created, and three scenarios were established to analyze the CO2 emissions and costs associated with municipal and in-store collection. The scenarios were cases where recyclables were collected through in-store collection (Scenario 1), recyclables were collected through municipal collection (Scenario 2), and both in-store collection and municipal collection were combined, similar to the current system (Scenario 3). The reduction in CO2 emissions in each model city in Scenario 1 was -37.0 to 53.5% compared to that in Scenario 3. There was a 0.90-1.96-fold increase in cost in Scenario 1 relative to Scenario 3. Suggestions for the appropriate implementation of in-store collection are proposed based on these results. For example, an increase in in-store collection reduces CO2 emissions but leads to an increase in costs. When integrating in-store collection into an MSW management system, reviewing the municipal collection system is necessary.

Identifying subgroups in heart failure patients with multimorbidity by clustering and network analysis.

This study presents a workflow for identifying and characterizing patients with Heart Failure (HF) and multimorbidity utilizing data from Electronic Health Records. Multimorbidity, the co-occurrence of two or more chronic conditions, poses a significant challenge on healthcare systems. Nonetheless, understanding of patients with multimorbidity, including the most common disease interactions, risk factors, and treatment responses, remains limited, particularly for complex and heterogeneous conditions like HF. We conducted a clustering analysis of 3745 HF patients using demographics, comorbidities, laboratory values, and drug prescriptions. Our analysis revealed four distinct clusters with significant differences in multimorbidity profiles showing differential prognostic implications regarding unplanned hospital admissions. These findings underscore the considerable disease heterogeneity within HF patients and emphasize the potential for improved characterization of patient subgroups for clinical risk stratification through the use of EHR data.

Clinical phenotypes of comorbidities in end-stage knee osteoarthritis: a cluster analysis.

OBJECTIVES: Comorbidities, as components of these heterogeneous features, often coexist with knee osteoarthritis, and are particularly prevalent in end-stage knee osteoarthritis. Here, we attempted to identify the different clinical phenotypes of comorbidities in patients with end-stage knee osteoarthritis by cluster analysis. METHODS: A total of 421 inpatients diagnosed with end-stage knee osteoarthritis who underwent inpatient surgery were included in this cross-sectional study. 23 demographic, comorbidity, inflammatory immune and evaluation scale variables were collected. Systematic clustering after factor analysis and separate two-step cluster analysis were performed for individual comorbidity variables and all variables, respectively, to objectively identify the different clinical phenotypes of the study patients. RESULTS: Four clusters were finally identified. Cluster 1 had the largest proportion of obese patients (93.8%) and hypertension was common (71.2%). Almost all patients in cluster 2 were depressed (95.8%) and anxiety disorders (94.7%). Cluster 3 combined patients with isolated end-stage knee osteoarthritis and a few comorbidities. Cluster 4 had the highest proportion of patients with rheumatoid arthritis (58.8%). CONCLUSIONS: Patients with end-stage knee osteoarthritis may be classified into four different clinical phenotypes: "isolated end-stage knee osteoarthritis"; "obesity + hypertension"; "depression + anxiety"; and "rheumatoid arthritis", which may help guide individualized patient care and treatment strategies.

Cluster analysis dissecting cognitive deficits in older adults with major depressive disorder and the association with neurofilament light chain.

BACKGROUND: Cognitive impairment is a growing problem with increasing burden in global aging. Older adults with major depressive disorder (MDD) have higher risk of dementia. Neurofilament light chain (NfL) has been proven as a potential biomarker in neurodegenerative disease, including dementia. We aimed to investigate the association between cognitive deficits and NfL levels in older adults with MDD. METHODS: In this cross-sectional study, we enrolled 39 MDD patients and 15 individuals with mild neurocognitive disorder or major neurocognitive disorder, Alzheimer's type, as controls, from a tertiary psychiatric hospital. Both groups were over age 65 and with matched Mini-Mental State Examination (MMSE) score. Demographic data, clinical variables, and plasma NfL levels were obtained. We used cluster analysis according to their cognitive profile and estimated the correlation between plasma NfL levels and each cognitive domain. RESULTS: In the MDD group, participants had higher rate of family psychiatry history and current alcohol use habit compared with controls. Control group of neurocognitive disorders showed significantly lower score in total MMSE and higher plasma NfL levels. Part of the MDD patients presented cognitive deficits clustered with that of neurocognitive disorders (cluster A). In cluster A, the total MMSE score (r=-0.58277, p=0.0287) and the comprehension domain (r=-0.71717, p=0.0039) were negatively correlated to NfL levels after adjusting for age, while the associations had not been observed in the other cluster. CONCLUSIONS: We noted the negative correlation between NfL levels and cognition in MDD patients clustered with neurodegenerative disorder, Alzheimer's type. NfL could be a promising candidate as a biomarker to predict subtype of patients in MDD to develop cognitive decline. Further longitudinal studies and within MDD cluster analysis are required to validate our findings for clinical implications.

Correlation Aware Relevance-Based Semantic Index for Clinical Big Data Repository.

In this paper, we focus on indexing mechanisms for unstructured clinical big integrated data repository systems. Clinical data is unstructured and heterogeneous, which comes in different files and formats. Accessing data efficiently and effectively are critical challenges. Traditional indexing mechanisms are difficult to apply on unstructured data, especially by identifying correlation information between clinical data elements. In this research work, we developed a correlation-aware relevance-based index that retrieves clinical data by fetching most relevant cases efficiently. In our previous work, we designed a methodology that categorizes medical data based on the semantics of data elements and merges them into an integrated repository. We developed a data integration system for medical data sources that combines heterogeneous medical data and provides access to knowledge-based database repositories to different users. In this research work, we designed an indexing system using semantic tags extracted from clinical data sources and medical ontologies that retrieves relevant data from database repositories and speeds up the process of data retrieval. Our objective is to provide an integrated biomedical database repository that can be used by radiologists as a reference, or for patient care, or by researchers. In this paper, we focus on designing a technique that performs data processing for data integration, learn the semantic properties of data elements, and develop a correlation-aware topic index that facilitates efficient data retrieval. We generated semantic tags by identifying key elements from integrated clinical cases using topic modeling techniques. We investigated a technique that identifies tags for merged categories and provides an index to fetch data from an integrated database repository. We developed a topic coherence matrix that shows how well a topic is supported by a corpus from clinical cases and medical ontologies. We were able to find more relevant results using an annotation index from an integrated database repository, and there was a 61% increase in a recall. We evaluated results with the help of experts and compared them with naive index (index with all terms from the corpus). Our approach improved data retrieval quality by providing most relevant results and reduced data retrieval time as we applied correlation-aware index on an integrated data repository. Topic indexing approach proposed in this research work identifies tags based on a correlation between different data elements, improves data retrieval time, and provides most relevant cases as an outcome of this system.

Temporal pattern classification of PM2.5 chemical compositions in Seoul, Korea using K-means clustering analysis.

Particulate matter with a diameter ≤ 2.5 µm (PM2.5) is a complex mixture of particles with a variety of compositions potentially including sulfate ions (SO42-), nitrate ions (NO3-), ammonium ions (NH4+), organic and inorganic elemental carbon, and metals. Here, the temporal composition evolution of PM2.5 was analyzed to characterize its emission source, origin, and external influences. The concentrations of wintertime PM2.5 chemical compositions in Seoul, Korea during the period of 2012-2021 were classified into four representative clusters using a K-means cluster analysis method. Cluster 1 exhibited high concentrations of NO3- and NH4+ ions mainly due to the prevalence of emissions from domestic manure and fertilizer sources in the northeast. High concentrations of these two ions are conducive to generation of ammonium nitrate (NH4NO3) through atmospheric chemical reactions, resulting in relatively long-lasting high PM2.5 concentrations in Seoul. In cluster 2, high concentrations of SO42-, vanadium, and nickel were observed in frequent south-westerly winds, indicating the domestic influence of industrial facilities. Cluster 3 showed high concentrations of potassium ions and organic carbon, highlighting a pronounced external influence transported from China via prevailing westerly winds. Cluster 4 showed low PM2.5 concentrations accompanied by strong winds in warm environments, which are uncommon in winter. This study revealed that the air quality in Seoul, which was influenced by many factors, could be classified into four representative patterns. Our results provide insights into the emission sources, major influences, and responsible mechanisms of high PM2.5 concentrations in Seoul, which can help with air quality policies.

Profiling the socioeconomic characteristics, dietary intake, and health status of Korean older adults for nutrition plan customization: a comparison of principal component, factor, and cluster analyses.

Objectives: This study was conducted to establish profiles of socioeconomic characteristics, dietary intake, and health status among Korean older adults by employing 3 multivariate analysis techniques. Methods: Data were obtained from 1,352 adults aged 65 years and older who participated in the 2019 Korea National Health and Nutrition Examination Survey. Principal component analysis (PCA), factor analysis (FA), and cluster analysis (CA) were utilized for profiling, with data preprocessing undertaken to facilitate these approaches. Results: PCA, FA, and CA yielded similar results, reflecting the high common variance among the variables. PCA identified 4 components, accounting for 71.6% of the accumulated variance. FA revealed 5 factors, displaying a Kaiser-Meyer-Olkin value of 0.51 and explaining 74.3% of the total variance. Finally, CA grouped the participants into 4 clusters (R2=0.465). Both PCA and FA identified dietary intake (energy, protein, carbohydrate, etc.), social support from family (incorporating family structure, number of family numbers, and engagement in social eating), and health status (encompassing oral, physical, and subjective health) as key factors. CA classified Korean older adults into 4 distinct typologies, with significant differences observed in dietary intake, health status, and household income (p<0.01). Conclusion: The study utilized PCA, FA, and CA to analyze profiling domains and derive characteristics of older adults in Korea, followed by a comparison of the results. The variables defining the clusters in CA were consistent with those identified by PCA and FA.

Data-driven classification of cognitively normal and mild cognitive impairment subtypes predicts progression in the NACC dataset.

INTRODUCTION: Data-driven neuropsychological methods can identify mild cognitive impairment (MCI) subtypes with stronger associations to dementia risk factors than conventional diagnostic methods. METHODS: Cluster analysis used neuropsychological data from participants without dementia (mean age = 71.6 years) in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (n = 26,255) and the "normal cognition" subsample (n = 16,005). Survival analyses examined MCI or dementia progression. RESULTS: Five clusters were identified: "Optimal" cognitively normal (oCN; 13.2%), "Typical" CN (tCN; 28.0%), Amnestic MCI (aMCI; 25.3%), Mixed MCI-Mild (mMCI-Mild; 20.4%), and Mixed MCI-Severe (mMCI-Severe; 13.0%). Progression to dementia differed across clusters (oCN < tCN < aMCI < mMCI-Mild < mMCI-Severe). Cluster analysis identified more MCI cases than consensus diagnosis. In the "normal cognition" subsample, five clusters emerged: High-All Domains (High-All; 16.7%), Low-Attention/Working Memory (Low-WM; 22.1%), Low-Memory (36.3%), Amnestic MCI (16.7%), and Non-amnestic MCI (naMCI; 8.3%), with differing progression rates (High-All < Low-WM = Low-Memory < aMCI < naMCI). DISCUSSION: Our data-driven methods outperformed consensus diagnosis by providing more precise information about progression risk and revealing heterogeneity in cognition and progression risk within the NACC "normal cognition" group.

Identifying New Subtypes of Multiple System Atrophy Using Cluster Analysis.

Background: Multiple system atrophy (MSA) is a disease with diverse symptoms and the commonly used classifications, MSA-P and MSA-C, do not cover all the different symptoms seen in MSA patients. Additionally, these classifications do not provide information about how the disease progresses over time or the expected outcome for patients. Objective: To explore clinical subtypes of MSA with a natural disease course through a data-driven approach to assist in the diagnosis and treatment of MSA. Methods: We followed 122 cases of MSA collected from 3 hospitals for 3 years. Demographic characteristics, age of onset, clinical signs, scale assessment scores, and auxiliary examination were collected. Age at onset; time from onset to assisted ambulation; and UMSARS I, II, and IV, COMPASS-31, ICARS, and UPDRS III scores were selected as clustering elements. K-means, partitioning around medoids, and self-organizing maps were used to analyze the clusters. Results: The results of all three clustering methods supported the classification of three MSA subtypes: The aggressive progression subtype (MSA-AP), characterized by mid-to-late onset, rapid progression and severe clinical symptoms; the typical subtype (MSA-T), characterized by mid-to-late onset, moderate progression and moderate severity of clinical symptoms; and the early-onset slow progression subtype (MSA-ESP), characterized by early-to-mid onset, slow progression and mild clinical symptoms. Conclusions: We divided MSA into three subtypes and summarized the characteristics of each subtype. According to the clustering results, MSA patients were divided into three completely different types according to the severity of symptoms, the speed of disease progression, and the age of onset.

Cluster analysis of teachers report for identifying symptoms of autism spectrum and/or attention deficit hyperactivity in school population: EPINED study.

An early detection of Neurodevelopmental Disorders (NDDs) is crucial for their prognosis; however, the clinical heterogeneity of some disorders, such as autism spectrum disorder (ASD) or attention-deficit hyperactivity disorder (ADHD) is an obstacle to accurate diagnoses in children. In order to facilitate the screening process, the current study aimed to identify symptom-based clusters among a community-based sample of preschool and school-aged children, using behavioral characteristics reported by teachers. A total of 6894 children were assessed on four key variables: social communication differences, restricted behavior patterns, restless-impulsiveness, and emotional lability (pre-schoolers) or inattention and hyperactivity-impulsivity (school-aged). From these behavioral profiles, four clusters were identified for each age group. A cluster of ASD + ADHD and others including children with no pathology was clearly identified, whereas two other clusters were characterized by subthreshold ASD and/or ADHD symptoms. In the school-age children, the presence of ADHD was consistently observed with ASD patterns. In pre-schoolers, teachers were more proficient at identifying children who received a diagnosis for either ASD and/or ADHD from an early stage. Considering the significance of early detection and intervention of NDDs, teachers' insights are important. Therefore, promptly identifying subthreshold symptoms in children can help to minimize consequences in social and academic functioning.