Exploratory Principal Component Analysis Research Articles

Auditory and mental well-being of teachers in urban noise environment: A partial least square structural equation model approach

Urban noise pollution significantly impacts public health, particularly affecting those in constant exposure, such as schoolteachers in urban areas. This study addresses the critical gap in understanding how persistent traffic noise impacts teachers’ auditory and mental health, a key concern given their essential role in educational outcomes. The necessity of this research is underscored by the potential for noise-induced disturbances to impair teaching effectiveness and teachers’ quality of life. The research question investigates the specific auditory and mental health outcomes for teachers working in noise-polluted environments. The study quantitatively measures hearing acuity and assesses mental well-being among teachers in five urban schools adjacent to high-traffic areas using audiometric assessments and a socio-acoustic questionnaire. Findings indicate a varied degree of noise-induced hearing loss (NIHL), with 36% of the participants exhibiting mild while 8% showing moderate hearing loss. The obtained value of hearing handicap ranges from 2.1 to 14.4%. However, these results for hearing handicaps were found to be less significant, indicating the non-considerable impact of classroom noise on the auditory health of teachers. Furthermore, through exploratory factor analysis (EFA) and principal component analysis (PCA), five critical factors impacting teachers’ well-being were identified: Physiological Impact, Acoustics, Ambient Environment, Social Awareness, and Mental Well-being. Subsequent analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) revealed that physiological impact and classroom acoustics, with path coefficients of 0.45 and 0.28, respectively, exerted a substantial and decisive influence on the mental well-being of schoolteachers, highlighting the critical role of environmental factors in shaping their psychological health. These insights underscore the pressing need for immediate interventions in school design and urban planning to effectively reduce noise pollution and protect the well-being of teachers.

울산지역 일부 고등학생의 치과공포에 대한 관련요인 분석

This study explored the effects of personal characteristics on dental fear in some high school students in Ulsan, and studied the correlation and causal relationship between general fear and dental fear. Data collection was conducted with 401 students enrolled in three high schools from April 22 to May 30, 2024. The validity and reliability of the questionnaire tool were analyzed by applying exploratory factor analysis and principal component analysis, and individual substrate types were classified through cluster analysis. Through t-test, one-way ANOVA, and Scheffe's post-test, the difference in fear related to dental treatment and general fear according to personal characteristics was compared, and the causal relationship between personal characteristics, general fear factors, and fear factors related to dental treatment was identified through path analysis. As a result of the study analysis, female students felt more fear in general than male students, and there was a significant difference between dental fear and general fear according to individual temperament, and as a result of studying the influence relationship between each factor through path analysis, the factor that has the greatest influence on the fear related to dental treatment was found to be the fear of physical threats. On the other hand, fear varies depending on temperament in all fear factors, so a customized approach is necessary to control dental fear.

Validating the CogSleep Screener in older adults at a memory and cognition clinic.

While sleep disturbances are prevalent in older people and are linked with poor health and cognitive outcomes, screening for the range of sleep disturbances is inefficient and therefore not ideal nor routine in memory and cognition clinic settings. We aimed to develop and validate a new brief self-report questionnaire for easy use within memory and cognition clinics. The design for this study was cross-sectional. Older adults (aged ≥50 in Sydney, Australia) were recruited from a memory and cognition research clinic. Participants (N = 402, mean age 67.3 years, range 50-86, 63.6% female) completed a comprehensive medical, neuropsychological, and mental health assessment, alongside self-report instruments, including existing sleep questionnaires and a new 10-item sleep questionnaire, the CogSleep Screener. We examined the factor structure, convergent validity, internal consistency, and discriminant validity of this novel questionnaire. Using exploratory principal component analysis, a 3-factor solution was generated highlighting the factors of Insomnia, Rapid Eye Movement (REM) Symptoms and Daytime Sleepiness. Each factor was significantly correlated with currently used sleep questionnaires for each subdomain (all Spearman rho >0.3, all p < 0.001), suggesting good convergent validity. Internal consistency was also good (Cronbach's α = 0.73). Receiver operating characteristic curves showed good discriminative ability between participants with and without sleep disturbances (all area under curve >0.7, all p < 0.01). The CogSleep Screener has good psychometric properties in older to elderly adults attending a memory and cognition clinic. The instrument has the potential to be used in memory clinics and other clinical settings to provide quick and accurate screening of sleep disturbances.

Predictors of early child development for screening pregnant women most in need of support in Brazil.

Home visiting programmes can support child development and reduce inequalities, but failure to identify the most vulnerable families can undermine such efforts. We examined whether there are strong predictors of poor child development that could be used to screen pregnant women in primary health care settings to target early interventions in a Brazilian population. Considering selected predictors, we assessed coverage and focus of a large-scale home visiting programme named Primeira Infância Melhor (PIM). We undertook a prospective cohort study on 3603 children whom we followed from gestation to age four years. We then used 27 potential socioeconomic, psychosocial, and clinical risk factors measurable during pregnancy to predict child development, which was assessed by the Battelle Developmental Inventory (BDI) at the age of four years. We compared the results from a Bonferroni-adjusted conditional inference tree with exploratory linear regression and principal component analysis (PCA), and we conducted external validation using data from a second cohort from the same population. Lastly, we assessed PIM coverage and focus by linking 2015 cohort data with PIM databases. The decision tree analyses identified maternal schooling as the most important variable for predicting BDI, followed by paternal schooling. Based on these variables, a group of 214 children who had the lowest mean BDI (BDI = -0.48; 95% confidence interval (CI) = -0.63, -0.33) was defined by mothers with ≤5 years and fathers with ≤4 years of schooling. Maternal and paternal schooling were also the strongest predictors in the exploratory analysis using regression and PCA, showing linear associations with the outcome. However, their capacity to explain outcome variance was low, with an adjusted R2 of 5.3% and an area under the receiver operating characteristic curve of 0.62 (95% CI = 0.60, 0.64). External validation showed consistent results. We also provided an online screening tool using parental schooling data to support programme's targeting. PIM coverage during pregnancy was low, but the focus was adequate, especially among families with longer enrolment, indicating families most in need received higher dosage. Information on maternal and paternal schooling can improve the focus of home visiting programmes if used for initial population-level screening of pregnant women in Brazil. However, enrolment decisions require complementary information on parental resources and direct interactions with families to jointly decide on inclusion.

Investigating nearshore spatial and temporal trends in nutrient concentrations along an urban northern shoreline, Lake Ontario

The nearshore zone of the Laurentian Great Lakes is of significant ecological importance, providing critical biogeochemical processes supporting spatially diverse habitats for a variety of species. Extreme plant and algal growth are common due to excessive anthropogenic eutrophication, with exacerbations from dreissenid establishment and lake mixing (e.g., coastal upwelling events and nearshore-offshore water exchanges), resulting in nuisance algal blooms across the nearshore area adjacent to Western Durham, Ontario, Canada. Thus, our main goal was to characterize the trends in essential nutrients (i.e., phosphorus and nitrogen) within the nearshore by applying general additive models on irregular time-series from 2011 to 2022 and to identify plausible contributing factors using exploratory principal component analysis. Increasing trends for total phosphorus were observed in surface (0.5 m below the surface) and benthic (0.5 m above the bottom) waters despite decreases in point source loading to the area. Most notably, the local water pollution control plant (WPCP) outfall did not seem to drive lake phosphorus concentrations across the study area, depicted by an orthogonal relationship within the principal component analysis. While the WPCP is a point source to the nearshore, it does not appear to be the primary driver of temporal lake phosphorus trends. Our results suggest that weather, inertial forcings and lake hydrodynamics in combination with traditional point-sources across Western Durham are contributing to the increasing total phosphorus trends observed as the peak period of wave spectra, wave height, alongshore wind speeds, watershed loadings, and total phosphorus concentrations within Lake Ontario were positively associated.

Discrimination and analytical profiling of colored printed documents using ATR-FTIR spectroscopy coupled with explorative and predictive statistical analysis: Part I

Printed documents are a common form of evidence in forensic document examination. The integration of spectroscopy with chemometrics have evolved evidential analytical interpretation of printing inks. However, we report the first ever study that explores the examination of both black and colored printed documents combined with explorative Principal Component Analysis (PCA) and supervised techniques viz. Soft independent modelling of class analogy (SIMCA) and Partial Least Square- Discriminant Analysis (PLS-DA). The study investigated 74 (40 Ink-based and 34 Toner- based) colored printed document samples using ATR-FTIR to discriminate and determine the source of origin of an unknown printed document using a non-destructive approach. Qualitative analysis by ATR- FTIR indicated the presence of polystyrene, bisphenol A and acrylates as the common binder polymers in the samples. The study was also able to obtain pigment information like presence of PR 57 and PR 146 in magenta, Carbon black in black, Copper Phthalocyanine and PB 15 in Cyan and PY 74 in yellow colored printed samples. Further, PCA has been used as an explorative technique that showed a variance of 97 % in the dataset and indicating that the color Cyan contributes to the maximum classification accuracy. SIMCA has been used as a supervised method to classify the known and test samples to their respective defined classes. However, SIMCA could only classify Toner-based samples in their respective class and inconclusive results were obtained in case of Ink-based samples. Finally, PLS-DA was also used to classify the two class of samples which resulted in a discrimination accuracy of 98.6 %. The derived model was also used for validation study on blind test samples which provided 100 % classification results.

Predicting the critical organizational behavior and culture of the Turkish construction industry's occupational groups for determining the success of the construction business

Organizational culture is connected to people's capacity to organize themselves internally and adapt to the outside environment. Contracting companies were asked to complete a questionnaire based on organizational culture characteristics. This study intends to make a substantial theoretical contribution by demonstrating a broader perspective on exposing the essential cultural features using a quantitative approach with the fewest possible questions. Exploratory factor analysis and Principal Component Analysis were used to identify the most critical variables and extract a more manageable number of components from multiple related variables. Twenty-eight variables were reduced to thirteen to eliminate statistical cross-loading. Furthermore, the organizational culture was simplified from eight to three dimensions. Thirteen factors were created based on cultural habits in the construction sector, with three inclusive dimensions: multiple motivational, one–bipolar, and many bipolar. This study provides a more succinct survey that quantifies cultural habits and their influence on sectoral performance.

A Search for New Biological Pathways in Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy by Proteomic Research.

Background/Objectives: Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) is a hereditary small vessel disease leading to significant morbidity and mortality. Despite advances in genetic diagnosis, the underlying pathophysiology remains incompletely understood. Proteomic studies offer insights into disease mechanisms by identifying altered protein expression patterns. Here, we conducted a proteomic analysis to elucidate molecular pathways associated with CADASIL. Methods: We enrolled genetically diagnosed CADASIL patients and healthy, genetically related controls. Plasma samples were subjected to proteomic analysis using the Olink platform, measuring 552 proteins across six panels. The data were analyzed from several approaches by using three different statistical methods: Exploratory Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA), differential expression with moderated t-test, and gene set enrichment analysis (GSEA). In addition, bioinformatics analysis, including volcano plot, heatmap, and Variable Importance on Projection (VIP) scores from the PLS-DA model were drawn. Results: Significant differences in protein expression were observed between CADASIL patients and controls. RSPO1 and FGF-19 exhibited elevated levels (p < 0.05), while PPY showed downregulation (p < 0.05) in CADASIL patients, suggesting their involvement in disease pathogenesis. Furthermore, MIC-A/B expression varied significantly between patients with mutations in exon 4 versus exon 11 of the NOTCH3 gene (p < 0.05), highlighting potential immunological mechanisms underlying CADASIL. We identified altered pathways using GSEA, applied after ranking the study data. Conclusions: Our study provides novel insights into the proteomic profile of CADASIL, identifying dysregulated proteins associated with vascular pathology, metabolic dysregulation, and immune activation. These findings contribute to a deeper understanding of CADASIL pathophysiology and may inform the development of targeted therapeutic strategies. Further research is warranted to validate these biomarkers and elucidate their functional roles in disease progression.

The disease recurrence perception scale for patients with inflammatory bowel disease: Instrument development and cross-sectional validation study.

Disease recurrence perception plays a key role in disease management and subsequent disease recurrence prevention. However, there are no specific tools for assessing disease recurrence perception in patients with inflammatory bowel disease (IBD) characterized by alternating remission and recurrence. To develop and validate an instrument for measuring disease recurrence perception of patients with IBD, the study was conducted in two steps: (1) instrument development and (2) psychometric tests. A total of 623 patients with IBD participated in the study. The common sense model of illness self-regulation (CSM) was used as a framework for instrument development. The administered version contained 48 items intended to be relevant to at least one of the six dimensions of the model. Based on preliminary analyzes, 12 items were deleted leaving 36 items for more detailed psychometric and factor analyzes. The Cronbach's alpha coefficient of the total 36-item instrument was 0.915. The content validity indexes at item and scale levels were satisfactory. The test-retest reliability of the total instrument was 0.870. Exploratory principal components analysis (n = 278) was used to identify six components congruent with intended CSM constructs that accounted for 62.6% of total item variance. Confirmatory factor analysis (n = 345) found acceptable fit for the six factor measurement model (χ2/df = 1.999, GFI = 0.846, NFI = 0.855, IFI = 0.922, TLI = 0.910, CFI = 0.921, RMSEA = 0.054). Overall, the DRPSIBD demonstrated satisfactory reliability and validity to warrant further development as a measure of disease recurrence perception of patients with IBD.

Assessing the Stability of Clusters of Neuropsychiatric Symptoms in Alzheimer's Disease and Mild Cognitive Impairment.

The aim of the study was to investigate the factors that underpin neuropsychiatric symptoms and how they might evolve over time in people with Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) dementia. Neuropsychiatric symptoms are psychiatric and behavioural manifestations that occur in people with AD. These are highly prevalent along the continuum of the disease, including at the stage of MCI, as well as before cognitive decline. Various small- and large-scale projects have investigated the underlying factors that underpin these symptoms; however, the identification of clear clusters is still a matter of debate; furthermore, no study has investigated how the clusters might change across the development of AD pathology by comparing different time points. Our objective was to investigate the factors that underpin neuropsychiatric symptoms in Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) and to assess how the loadings might differ based on considerations such as the disease stage of the samples. Data was obtained from the Alzheimer's Disease Neuroimaging Initiative database (adni. loni.usc.edu), using scores from the Neuropsychiatric Inventory, followed up yearly from baseline until month 72. Participant groups included those with MCI or AD dementia, or a mixture of both, with all participants presenting with at least one neuropsychiatric symptom. A series of exploratory Principal Component and Factor (Principal Axis) Analyses were performed using Direct Oblimin rotation. The best-fitting structure was interpreted for each time point. A consistent, unique structure could not be identified, as the factors were unstable over time, both within the MCI and AD groups. However, some symptoms showed a tendency to load on the same factors across most measurements (i.e., agitation with irritability, depression with anxiety, elation with disinhibition, delusions with hallucinations). Although the analyses revealed some degree of co-occurrence of neuropsychiatric symptoms across time points/samples, there was also considerable variation. In the AD group, more discrete syndromes were evident at the early time points, whereas a more complex picture of co-occurring symptoms, with differences likely reflecting disease staging, was seen at later time points. As a clear and distinctive factor structure was not consistently identified across time points/ samples, this highlights the potential importance of sample selection (e.g., disease stage and/or heterogeneity) when studying, for example, the neurobiological underpinnings of neuropsychiatric symptoms.

Critical evaluation of energy dispersive X-ray fluorescence spectrometry for multielemental analysis of coffee samples: Sample preparation, quantification and chemometric approaches

In the present contribution, a critical evaluation of the use of EDXRF (Energy dispersive X-ray fluorescence spectrometry) for elemental composition determination (K, Ca, Mn, Fe, Cu, Zn, Sr and Rb) of coffee samples is presented. Firstly, several sample treatment procedures (pressed pellets and loose powder) and quantification strategies (fundamental parameters and empirical calibration) were evaluated.In addition to quality and nutritional purposes, information of elemental content in coffee samples can be also useful for coffee traceability and provenance studies. In view of that, a comparison of different tools such as explorative principal component analysis (PCA) and classification by partial least square regression (PLS-DA) were also tested as discriminating tools of different types of coffee samples (i.e., pure, instant and coffee with additives). Chemometric analysis was performed using coffee elemental concentrations determined by the developed EDXRF method and, for the first time, using raw EDXRF spectra obtained, without the need of a fitting and quantification procedure.

Belonging in Surgery: A Validated Instrument and Single Institutional Pilot.

The aim of this study was to develop and validate an instrument to measure Belonging in Surgery among surgical residents. Belonging is the essential human need to maintain meaningful relationships and connections to one's community. Increased belongingness is associated with better well-being, job performance, and motivation to learn. However, no tools exist to measure belonging among surgical trainees. A panel of experts adapted a belonging instrument for use among United States surgery residents. After administration of the 28-item instrument to residents at a single institution, a Cronbach alpha was calculated to measure internal consistency, and exploratory principal component analyses were performed. Multiple iterations of analyses with successively smaller item samples suggested the instrument could be shortened. The expert panel was reconvened to shorten the instrument. Descriptive statistics measured demographic factors associated with Belonging in Surgery. The overall response rate was 52% (114 responses). The Cronbach alpha among the 28 items was 0.94 (95% CI: 0.93-0.96). The exploratory principal component analyses and subsequent Promax rotation yielded 1 dominant component with an eigenvalue of 12.84 (70% of the variance). The expert panel narrowed the final instrument to 11 items with an overall Cronbach alpha of 0.90 (95% CI: 0.86, 0.92). Belonging in Surgery was significantly associated with race (Black and Asian residents scoring lower than White residents), graduating with one's original intern cohort (residents who graduated with their original class scoring higher than those that did not), and inversely correlated with resident stress level. An instrument to measure Belonging in Surgery was validated among surgical residents. With this instrument, Belonging in Surgery becomes a construct that may be used to investigate surgeon performance and well-being.

Multimodal emotion recognition based on the fusion of vision, EEG, ECG, and EMG signals

This paper presents a novel approach for emotion recognition (ER) based on Electroencephalogram (EEG), Electromyogram (EMG), Electrocardiogram (ECG), and computer vision. The proposed system includes two different models for physiological signals and facial expressions deployed in a real-time embedded system. A custom dataset for EEG, ECG, EMG, and facial expression was collected from 10 participants using an Affective Video Response System. Time, frequency, and wavelet domain-specific features were extracted and optimized, based on their Visualizations from Exploratory Data Analysis (EDA) and Principal Component Analysis (PCA). Local Binary Patterns (LBP), Local Ternary Patterns (LTP), Histogram of Oriented Gradients (HOG), and Gabor descriptors were used for differentiating facial emotions. Classification models, namely decision tree, random forest, and optimized variants thereof, were trained using these features. The optimized Random Forest model achieved an accuracy of 84%, while the optimized Decision Tree achieved 76% for the physiological signal-based model. The facial emotion recognition (FER) model attained an accuracy of 84.6%, 74.3%, 67%, and 64.5% using K-Nearest Neighbors (KNN), Random Forest, Decision Tree, and XGBoost, respectively. Performance metrics, including Area Under Curve (AUC), F1 score, and Receiver Operating Characteristic Curve (ROC), were computed to evaluate the models. The outcome of both results, i.e., the fusion of bio-signals and facial emotion analysis, is given to a voting classifier to get the final emotion. A comprehensive report is generated using the Generative Pretrained Transformer (GPT) language model based on the resultant emotion, achieving an accuracy of 87.5%. The model was implemented and deployed on a Jetson Nano. The results show its relevance to ER. It has applications in enhancing prosthetic systems and other medical fields such as psychological therapy, rehabilitation, assisting individuals with neurological disorders, mental health monitoring, and biometric security.

Developing and validating a tool for assessing the confidence in the competence of midwifery tutors in India on WHO core competency domains.

Negligible quantitative research evidence exists on standardisation and psychometric validation of questionnaires that measure midwifery educators' confidence in their competence. This study developed a self-assessment of confidence in competence questionnaire in India based on the WHO Midwifery Educator Core Competencies (2014) with an aim to develop and validate a self-assessment tool measuring midwifery tutors' confidence in competence in imparting quality midwifery education. The questionnaire was developed as part of a multi-centre study to identify confident midwifery tutors for further training as educators, supporting India's rollout of professional midwives. The questionnaire underwent rigorous psychometric testing among 2016 midwifery tutors in India. Following exploratory Principal Component Analyses (PCA), the nine core competencies outlined in the WHO document were analysed separately. The results indicate that the questionnaire is psychometrically valid, with an internal consistency range of 0.81-0.93 for the nine domains. This robust testing process ensures the reliability and validity of the questionnaire. The self-assessment questionnaire can potentially be a valuable tool in India and other high-, middle-, and low-income countries. From a programmatic perspective, it can help identify key gaps and prioritise training needs, particularly in low-resource settings, so that limited resources are best utilised to fill the most prominent gaps. Furthermore, it can provide a universal platform for comparing data from different settings, facilitating global collaboration and learning in midwifery education.

Are remote health clinics primary health care focused? Validation of the Primary Health Care Engagement (PHCE) Scale for the Australian remote primary health care setting.

To test and validate a measure of primary health care (PHC) engagement in the Australian remote health context. PHC principles include quality improvement, community participation and orientation of health care, patient-centred continuity of care, accessibility, and interdisciplinary collaboration. Measuring the alignment of services with the principles of PHC provides a method of evaluating the quality of care in community settings. A two-stage design of initial content and face validity evaluation by a panel of experts and then pilot-testing the instrument via survey methods was conducted. Twelve experts from clinical, education, management and research roles within the remote health setting evaluated each item in the original instrument. Panel members evaluated the representativeness and clarity of each item for face and content validity. Qualitative responses were also collected and included suggestions for changes to item wording. The modified tool was pilot-tested with 47 remote area nurses. Internal consistency reliability of the Australian Primary Health Care Engagement scale was evaluated using Cronbach's alpha. Construct validity of the Australian scale was evaluated using exploratory factor analysis and principal component analysis. Modifications to suit the Australian context were made to 8 of the 28 original items. This modified instrument was pilot-tested with 47 complete responses. Overall, the scale showed high internal consistency reliability. The subscale constructs 'Quality improvement', 'Accessibility-availability' and 'population orientation' showed low levels of internal consistency reliability. However, the mean inter-item correlation was 0.31, 0.26 and 0.31, respectively, which are in the recommended range of 0.15 to 0.50 and indicate that the items are correlated and are measuring the same construct. The Australian PHCE scale is recommended as a tool for the evaluation of health services. Further testing on a larger sample may provide clarity over some items which may be open to interpretation.

Influencing factors of Six Sigma management in the construction of students mental health service system in the post-COVID-19 era

PurposeThis study aims to construct a mental health service system for middle school students in the post-COVID-19 era with the framework of Six Sigma DMAIC (define, measure, analyze, improve and control) and analyze the influencing factors of the mental health service system to study the implementation strategies of quality-oriented mental health services in middle schools.Design/methodology/approachThis study was conducted in Tianjin, China, from September to November 2022, and 350 middle school students from Tianjin Public Middle School were selected as subjects. A questionnaire survey was used to collect data. In this study, the Six Sigma DMAIC method, sensitivity analysis method, exploratory factor analysis and principal component analysis were used to analyze the mental health services provided to middle school students.FindingsBased on the Six Sigma DMAIC framework, this study indicates that the contribution rate of the mental health service process factor is the largest in the post-COVID-19 era. The mental health cultivation factor ranks second in terms of its contribution. Mental health quality and policy factors are also important in the construction of middle school students’ mental health service system. In addition, the study highlights the importance of parental involvement and social support in student mental health services during the post-COVID-19 era.Originality/valueTo the best of the authors’ knowledge, a study on middle school students’ mental health in the post-Covid-19 era has not yet been conducted. This study developed a quality-oriented mental health system and analyzed the influencing factors of mental health for middle school students based on data analysis and the Six Sigma DMAIC method.

Clustering of neuropsychiatric symptoms across time in Alzheimer’s disease and Mild Cognitive Impairment

AbstractBackgroundNeuropsychiatric symptoms are behavioural manifestations highly prevalent along the Alzheimer’s disease (AD) continuum, including at the stage of Mild Cognitive Impairment (MCI). Although various projects have investigated the factors that underpin these symptoms, the most stable clustering pattern is still matter for debate; furthermore, to our knowledge, no study has investigated, longitudinally, how clusters might change due to development of AD pathology. Therefore, our objective is to investigate neuropsychiatric clusters over time, in a large sample of MCI and AD dementia patients.MethodsSamples were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu), using data from the Neuropsychiatric Inventory (NPI), from the baseline assessment visit, followed up yearly until data recorded at month 72. The samples included MCI and AD dementia patients (based on ADNI’s inclusion criteria), presenting with at least one neuropsychiatric symptom at the time‐point selected. Using SPSS (Version 28), we performed a series of exploratory principal components analyses (PCA), Varimax rotation, and principal axis factor analyses (FA), comparing Promax and Direct Oblimin rotations.ResultsThe best‐fitting structure was interpreted at each time‐point, based on: Eigenvalues&gt;1, items’ loadings&gt;0.4, scree plot patterns, minimum 40% of variance explained by the model, and independence of factors. Results from both PCA and FA indicated that a unique structure could not be identified, as factors were not stable over time; however, some symptoms tended to load on the same factors across most measurements (i.e. delusions with hallucinations; agitation with depression, anxiety and irritability; to a lesser extent elation with disinhibition; apathy with aberrant motor behaviour, sleep disturbances and appetite disorder).ConclusionsThe available evidence reveals that factors underlying the neuropsychiatric symptoms in a sample of AD and MCI patients are not consistent across the time‐points. However, some symptoms tend to co‐occur across time, with implications, for example, that an investigation into one of those symptoms should consider the presence of the other; furthermore, as clear theoretically driven factors are not distinctively identified at every time‐point, this illustrates the potential importance of sample selection (e.g., disease stage, and/or heterogeneity) on studies of neuropsychiatric symptoms.

The Intuitive Eating Scale-2 Adapted for Mexican Pregnant Women: Psychometric Properties and Influence of Sociodemographic Variables.

A weight-inclusive approach to health involves the promotion of intuitive eating, i.e., the individual's ability to be aware of their physiological hunger and satiety cues to determine when and how much to eat, while paying attention to how certain foods affect their body. The second version of the Intuitive Eating Scale (IES-2) evaluates four interrelated traits of intuitive eating: Unconditional Permission to Eat (UPE), Eating for Physical rather than emotional Reasons (EPR), Reliance on internal Hunger/Satiety Cues (RHSC), and Body-Food Choice Congruence (BFCC). In this study, our aim was to evaluate the psychometric properties of a Mexican Spanish adaptation of the IES-2 for pregnant women and examine the relationship between intuitive eating traits and maternal sociodemographic characteristics. A sample of 514 pregnant women answered our IES-2 adaptation and a sociodemographic questionnaire. We determined the quality, validity, and reliability of our adaptation through descriptive measures, frequency distributions, intra-class correlations, and extreme answer group comparison for each item, eliminating those with weak technical properties. We then performed an exploratory principal component analysis and a confirmatory factor analysis. Last, we analyzed the association between intuitive eating and maternal sociodemographic and reproductive variables through correlation tests and multivariable linear regressions. Psychometric tests confirmed the validity and reliability of our IES-2 adaptation, which comprised 18 out of the 23 original items. Notably, both the exploratory and confirmatory factor analyses yielded not four but five factors, due to the EPR subscale splitting in two (the "emotional" and "physical" components of EPR). We attribute this novel finding to the emotional manifestations that naturally accompany pregnancy, which may incline pregnant women to base their eating behaviors more on the emotional than the physical component that would otherwise dominate their EPR trait. Further research is also needed about the UPE subscale during pregnancy, due to item removal and subtle changes in meaning. Finally, the influence of sociodemographic variables on the IES-2 score was extremely low, suggesting that other variables, possibly of a psychological nature, may have greater influence on a pregnant woman's intuitive eating.

Verification of the active pharmaceutical ingredient in tablets using a low-cost near-infrared spectrometer

The present study investigated the possibilities and limitations of using a low-cost NIR spectrometer for the verification of the presence of the declared active pharmaceutical ingredients (APIs) in tablet formulations, especially for medicine screening studies in low-resource settings. Spectra from 950 to 1650 nm were recorded for 170 pharmaceutical products representing 41 different APIs, API combinations or placebos. Most of the products, including 20 falsified medicines, had been collected in medicine quality studies in African countries. After exploratory principal component analysis, models were built using data-driven soft independent modelling of class analogy (DD-SIMCA), a one-class classifier algorithm, for tablet products of penicillin V, sulfamethoxazole/trimethoprim, ciprofloxacin, furosemide, metronidazole, metformin, hydrochlorothiazide, and doxycycline. Spectra of amoxicillin and amoxicillin/clavulanic acid tablets were combined into a single model. Models were tested using Procrustes cross-validation and by projection of spectra of tablets containing the same or different APIs. Tablets containing no or different APIs could be identified with 100 % specificity in all models. A separation of the spectra of amoxicillin and amoxicillin/clavulanic acid tablets was achieved by partial least squares discriminant analysis. 15 out of 19 external validation products (79 %) representing different brands of the same APIs were correctly identified as members of the target class; three of the four rejected samples showed an API mass percentage of the total tablet weight that was out of the range covered in the respective calibration set. Therefore, in future investigations larger and more representative spectral libraries are required for model building. Falsified medicines containing no API, incorrect APIs, or grossly incorrect amounts of the declared APIs could be readily identified.Variation between different NIR-S-G1 spectroscopic devices led to a loss of accuracy if spectra recorded with different devices were pooled. Therefore, piecewise direct standardization was applied for calibration transfer.The investigated method is a promising tool for medicine screening studies in low-resource settings.

Assessing Antecedents of Behavioral Intention to Use e-LMS: A Case From a Private Institution in the Northern Region of India

Students' and instructors' adoption of “e-learning management systems (e-LMSs)” is critical to their success in a “virtual learning environment”. Students can use “e-learning” to obtain instructional materials to supplement “traditional classroom” instruction. This study intends to highlight important factors of e-learning adoption in higher education institutes. The aim is to determine the factors forming positive intention towards using e-LMS based on “individual characteristics” (“Instructor Quality (IQ)”, “Course Quality (CQ)”, “Learning Value (LV)”, “Social Influence (SI)”, “Facilitating Condition (FC)”, “System Quality (SQ)”, “Perceived Usefulness (PU)”, and “Perceived Ease of Use (PEOU)”). The questionnaire-based survey based on 369 responses from LMS users. The proposed research model was tested using Structural Equation Modelling (SEM). Initially, Post Adoption Behavior was required to be investigated. The original measurement model contained eight independent constructs. A modified measurement model was created after the initial model was enhanced using exploratory factor and principal component analysis. The results of this study will help practitioners and policy makers in the effective development and implementation of e-Learning Management Systems in HEI's especially in developing countries like India. From the theoretical point of view, the result of this study will further enrich the IT adoption literature.