Predictors of transition to schizophrenia and other long-lasting non-affective psychoses in first-episode patients with acute and transient psychotic disorders: A validation study

Background and objectives Almost half of the individuals with a first-episode of psychosis who initially meet criteria for acute and transient psychotic disorder (ATPD) will have had a diagnostic revision during their follow-up, mostly toward schizophrenia. This study aimed to determine the proportion of diagnostic transitions to schizophrenia and other long-lasting non-affective psychoses in patients with first-episode ATPD, and to examine the validity of the existing predictors for diagnostic shift in this population. Methods We designed a prospective two-year follow-up study for subjects with first-episode ATPD. A multivariate logistic regression analysis was performed to identify independent variables associated with diagnostic transition to persistent non-affective psychoses. This prediction model was built by selecting variables on the basis of clinical knowledge. Results Sixty-eight patients with a first-episode ATPD completed the study and a diagnostic revision was necessary in 30 subjects at the end of follow-up, of whom 46.7% transited to long-lasting non-affective psychotic disorders. Poor premorbid adjustment and the presence of schizophreniform symptoms at onset of psychosis were the only variables independently significantly associated with diagnostic transition to persistent non-affective psychoses. Conclusion Our findings would enable early identification of those inidividuals with ATPD at most risk for developing long-lasting non-affective psychotic disorders, and who therefore should be targeted for intensive preventive interventions. (AU)

Estimation of rice yield using multivariate analysis techniques based on meteorological parameters.

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980-81 to 2020-21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.

Development and validation of a simple and practical model for early detection of diabetic macular edema in patients with type 2 diabetes mellitus using easily accessible systemic variables.

OBJECTIVE: Diabetic macular edema (DME) is the leading cause of visual impairment in patients with diabetes mellitus (DM). The goal of early detection has not yet achieved due to a lack of fast and convenient methods. Therefore, we aim to develop and validate a prediction model to identify DME in patients with type 2 diabetes mellitus (T2DM) using easily accessible systemic variables, which can be applied to an ophthalmologist-independent scenario. METHODS: In this four-center, observational study, a total of 1994 T2DM patients who underwent routine diabetic retinopathy screening were enrolled, and their information on ophthalmic and systemic conditions was collected. Forward stepwise multivariable logistic regression was performed to identify risk factors of DME. Machine learning and MLR (multivariable logistic regression) were both used to establish prediction models. The prediction models were trained with 1300 patients and prospectively validated with 104 patients from Guangdong Provincial People's Hospital (GDPH). A total of 175 patients from Zhujiang Hospital (ZJH), 115 patients from the First Affiliated Hospital of Kunming Medical University (FAHKMU), and 100 patients from People's Hospital of JiangMen (PHJM) were used as external validation sets. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, and specificity were used to evaluate the performance in DME prediction. RESULTS: The risk of DME was significantly associated with duration of DM, diastolic blood pressure, hematocrit, glycosylated hemoglobin, and urine albumin-to-creatinine ratio stage. The MLR model using these five risk factors was selected as the final prediction model due to its better performance than the machine learning models using all variables. The AUC, ACC, sensitivity, and specificity were 0.80, 0.69, 0.80, and 0.67 in the internal validation, and 0.82, 0.54, 1.00, and 0.48 in prospective validation, respectively. In external validation, the AUC, ACC, sensitivity and specificity were 0.84, 0.68, 0.90 and 0.60 in ZJH, 0.89, 0.77, 1.00 and 0.72 in FAHKMU, and 0.80, 0.67, 0.75, and 0.65 in PHJM, respectively. CONCLUSION: The MLR model is a simple, rapid, and reliable tool for early detection of DME in individuals with T2DM without the needs of specialized ophthalmologic examinations.

Neonatal heart transplantation in the United States: Trends and outcomes.

BACKGROUND: Heart transplantation in the neonatal period is associated with excellent survival. However, outcomes data are scant and have been obtained primarily from two single-center reports within the United States. We sought to analyze the outcomes of all neonatal heart transplants performed in the United States using the United Network for Organ Sharing (UNOS) dataset. METHODS: The UNOS dataset was queried for patients who underwent infant heart transplantation from 1987 to 2021. Patients were divided into two groups based on age - neonates (<=31 days), and older infants (32 days-365 days). Demographic and clinical characteristics were analyzed and compared, along with follow up survival data. RESULTS: Overall, 474 newborns have undergone heart transplantation in the United States since 1987. Freedom from death or re-transplantation for neonates was 63.5%, 58.8% and 51.6% at 5, 10, and 20 years, respectively. Patients in the newborn group had lower unadjusted survival compared to older infants (p < .001), but conditional 1-year survival was higher in neonates (p = .03). On multivariable analysis, there was no significant difference in survival between the two age groups (p = .43). Black race, congenital heart disease diagnosis, earlier surgical era, and preoperative mechanical circulatory support use were associated with lower survival among infant transplants (p < .05). CONCLUSIONS: Neonatal heart transplantation is associated with favorable long-term clinical outcomes. Neonates do not have a significant survival advantage over older infants. Widespread applicability is limited by the small number of available donors. Efforts to expand the donor pool to include non-standard donor populations ought to be considered.

Recurrence of rectal cancer on the pelvic sidewall after lateral lymph node dissection.

PURPOSE: Although lateral lymph node dissection has been performed to prevent lateral pelvic recurrence in locally advanced lower rectal cancer, the incidence of lateral pelvic recurrence after this procedure has not been investigated. Therefore, this study aimed to investigate the long-term outcomes of patients who underwent lateral pelvic lymph node dissection, with a particular focus on recurrence patterns. METHODS: This was a retrospective study conducted at a single high-volume cancer center in Japan. A total of 493 consecutive patients with stage II-III rectal cancer who underwent lateral lymph node dissection between January 2005 and August 2022 were included. The primary outcome measures included patterns of recurrence, overall survival, and relapse-free survival. Patterns of recurrence were categorized as lateral or central pelvic. RESULTS: Among patients who underwent lateral lymph node dissection, 18.1% had pathologically positive lateral lymph node metastasis. Lateral pelvic recurrence occurred in 5.5% of patients after surgery. Multivariate analysis identified age > 75 years, lateral lymph node metastasis, and adjuvant chemotherapy as independent risk factors for lateral pelvic recurrence. Evaluation of the recurrence rate by dissection area revealed approximately 1% of recurrences in each area after dissection. CONCLUSION: We demonstrated the prognostic outcome and limitations of lateral lymph node dissection for patients with advanced lower rectal cancer, focusing on the incidence of recurrence in the lateral area after the dissection. Our study emphasizes the clinical importance of lateral lymph node dissection, which is an essential technique that surgeons should acquire.

Impact of Preoperative Neutrophil to Prealbumin Ratio Index (NPRI) on Short-Term Complications and Long-Term Prognosis in Patients Undergoing Laparoscopic Radical Surgery for Colorectal Cancer.

Objective: This study aims to evaluate the impact and predictive value of the preoperative NPRI on short-term complications and long-term prognosis in patients undergoing laparoscopic radical surgery for colorectal cCancer (CRC). Methods: A total of 302 eligible CRC patients were included, assessing five inflammation-and nutrition-related markers and various clinical features for their predictive impact on postoperative outcomes. Emphasis was on the novel indicator NPRI to elucidate its prognostic and predictive value for perioperative risks. Results: Multivariate logistic regression analysis identified a history of abdominal surgery, prolonged surgical duration, CEA levels ≥5 ng/mL, and NPRI ≥ 3.94 × 10-2 as independent risk factors for postoperative complications in CRC patients. The Clavien--Dindo complication grading system highlighted the close association between preoperative NPRI and both common and severe complications. Multivariate analysis also identified a history of abdominal surgery, tumor diameter ≥5 cm, poorly differentiated or undifferentiated tumors, and NPRI ≥ 2.87 × 10-2 as independent risk factors for shortened overall survival (OS). Additionally, a history of abdominal surgery, tumor maximum diameter ≥5 cm, tumor differentiation as poor/undifferentiated, NPRI ≥ 2.87 × 10-2, and TNM Stage III were determined as independent risk factors for shortened disease-free survival (DFS). Survival curve results showed significantly higher 5-year OS and DFS in the low NPRI group compared to the high NPRI group. The incorporation of NPRI into nomograms for OS and DFS, validated through calibration and decision curve analyses, attested to the excellent accuracy and practicality of these models. Conclusion: Preoperative NPRI independently predicts short-term complications and long-term prognosis in patients undergoing laparoscopic colorectal cancer surgery, enhancing predictive accuracy when incorporated into nomograms for patient survival.

Mapping soil trace metal distribution using remote sensing and multivariate analysis.

Trace metal soil contamination poses significant risks to human health and ecosystems, necessitating thorough investigation and management strategies. Researchers have increasingly utilized advanced techniques like remote sensing (RS), geographic information systems (GIS), geostatistical analysis, and multivariate analysis to address this issue. RS tools play a crucial role in collecting spectral data aiding in the analysis of trace metal distribution in soil. Spectroscopy offers an effective understanding of environmental contamination by analyzing trace metal distribution in soil. The spatial distribution of trace metals in soil has been a key focus of these studies, with factors influencing this distribution identified as soil type, pH levels, organic matter content, land use patterns, and concentrations of trace metals. While progress has been made, further research is needed to fully recognize the potential of integrated geospatial imaging spectroscopy and multivariate statistical analysis for assessing trace metal distribution in soils. Future directions include mapping multivariate results in GIS, identifying specific anthropogenic sources, analyzing temporal trends, and exploring alternative multivariate analysis tools. In conclusion, this review highlights the significance of integrated GIS and multivariate analysis in addressing trace metal contamination in soils, advocating for continued research to enhance assessment and management strategies.

Multivariate curve resolution-based data fusion approaches applied in 1H NMR metabolomic analysis of healthy cohorts.

BACKGROUND: Metabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics. RESULTS: This study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, 'Low-Level data fusion' (LLDF) and 'Mid-Level data fusion' (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing-encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers. SIGNIFICANCE: Both methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis.

Chemical Characterization of the Essential Oil Compositions of Mentha spicata and M. longifolia ssp. cyprica from the Mediterranean Basin and Multivariate Statistical Analyses.

This present study aims to characterize the essential oil compositions of the aerial parts of M. spicata L. and endemic M. longifolia ssp. cyprica (Heinr. Braun) Harley by using GC-FID and GC/MS analyses simultaneously. In addition, it aims to perform multivariate statistical analysis by comparing with the existing literature, emphasizing the literature published within the last two decades, conducted on both species growing within the Mediterranean Basin. The major essential oil components of M. spicata were determined as carvone (67.8%) and limonene (10.6%), while the major compounds of M. longifolia ssp. cyprica essential oil were pulegone (64.8%) and 1,8-cineole (10.0%). As a result of statistical analysis, three clades were determined for M. spicata: a carvone-rich chemotype, a carvone/trans-carveol chemotype, and a pulegone/menthone chemotype, with the present study result belonging to the carvone-rich chemotype. Carvone was a primary determinant of chemotype, along with menthone, pulegone, and trans-carveol. In M. longifolia, the primary determinants of chemotype were identified as pulegone and menthone, with three chemotype clades being pulegone-rich, combined menthone/pulegone, and combined menthone/pulegone with caryophyllene enrichment. The primary determinants of chemotype were menthone, pulegone, and caryophyllene. The present study result belongs to pulegone-rich chemotype.

Multivariate statistical analysis of bioavailability of heavy metals and mineral characterization in selected species of coastal flora.

This study presents a thorough investigation into the concentration of heavy metals and mineral composition within four distinct coastal flora species: Cyperus conglomeratus, Halopyrum mucronatum, Sericostem pauciflorum, and Salvadora persica. Employing rigorous statistical methodologies such as Pearson coefficient correlation, principal component analysis (PCA), analysis of variance (ANOVA), and interclass correlation (ICC), we aimed to elucidate the bioavailability of heavy metals, minerals, and relevant physical characteristics. The analysis focused on essential elements including copper (Cu), iron (Fe), manganese (Mn), zinc (Zn), magnesium (Mg2+), calcium (Ca2+), sodium (Na+), potassium (K+), and chloride (Cl-), all of which are known to play pivotal roles in the ecological dynamics of coastal ecosystems. Through PCA, we discerned distinctive patterns within PC1 to PC4, collectively explaining an impressive 99.65% of the variance observed in heavy metal composition across the studied flora species. These results underscore the profound influence of environmental factors on the mineral composition of coastal flora, offering critical insights into the ecological processes shaping these vital ecosystems. Furthermore, significant correlations among mineral contents in H. mucronatum; K+ with content of Na+ (r = 0.989) and Mg2+ (r = 0.984); as revealed by ICC analyses, contributed to a nuanced understanding of variations in electrical conductivity (EC), pH levels, and ash content among the diverse coastal flora species. By shedding light on heavy metal and mineral dynamics in coastal flora, this study not only advances our scientific understanding but also provides a foundation for the development of targeted environmental monitoring and management strategies aimed at promoting the ecological sustainability and resilience of coastal ecosystems in the face of ongoing environmental challenges.

Appropriateness of intended antibiotic prescribing using clinical case vignettes in primary care, and related factors.

BACKGROUND: Factors associated with the appropriateness of antibiotic prescribing in primary care have been poorly explored. In particular, the impact of computerised decision-support systems (CDSS) remains unknown. OBJECTIVES: We aim at investigating the uptake of CDSS and its association with physician characteristics and professional activity. METHODS: Since May 2022, users of a CDSS for antibiotic prescribing in primary care in France have been invited, when registering, to complete three case vignettes assessing clinical situations frequently encountered in general practice and identified as at risk of antibiotic misuse. Appropriateness of antibiotic prescribing was defined as the rate of answers in line with the current guidelines, computed by individuals and by specific questions. Physician's characteristics associated with individual appropriate antibiotic prescribing (< 50%, 50-75% and > 75% appropriateness) were identified by multivariate ordinal logistic regression. RESULTS: In June 2023, 60,067 physicians had registered on the CDSS. Among the 13,851 physicians who answered all case vignettes, the median individual appropriateness level of antibiotic prescribing was 77.8% [Interquartile range, 66.7%-88.9%], and was < 50% for 1,353 physicians (10%). In the multivariate analysis, physicians' characteristics associated with appropriateness were prior use of the CDSS (OR = 1.71, 95% CI 1.56-1.87), being a general practitioner vs. other specialist (OR = 1.34, 95% CI 1.20-1.49), working in primary care (OR = 1.14, 95% CI 1.02-1.27), mentoring students (OR = 1.12, 95% CI 1.04-1.21) age (OR = 0.69 per 10 years increase, 95% CI 0.67-0.71). CONCLUSION: Individual appropriateness for antibiotic prescribing was high among CDSS users, with a higher rate in young general practitioners, previously using the system. CDSS could improve antibiotic prescribing in primary care.

Individual appropriateness for antibiotic prescribing is high among CDSS users.CDSS use could passively improve antibiotic prescribing in primary care.Factors associated with appropriateness for antibiotic prescribing for primary care diseases are: prior use of CDSS, general practice speciality vs. other specialities, younger age and mentoring of students.

Unplanned pregnancy in the extreme South of Brazil: prevalence, trends, and associated factors. / Gravidez não planejada no extremo Sul do Brasil: prevalência, tendência e fatores associados.

The study aims to estimate the proportion of puerperae with an unplanned pregnancy, evaluate trends and identify factors associated with its occurrence in Rio Grande-RS, Brazil. Trained interviewers applied a single, standardized questionnaire to all puerperae residing in the municipality in 2007, 2010, 2013, 2016 and 2019. The chi-square test compared proportions and the Poisson regression with robust variance adjustment in the multivariate analysis. The prevalence ratio (PR) was the effect measure employed. The study includes 12,415 puerperae (98% of the total). The unplanned pregnancy rate was 63.3% (95%CI: 62.5%-64.1%). After adjusting, the highest PR for not planning pregnancy were observed among younger, black women, living without a partner, with more significant household agglomeration, lower schooling, and household income, multiparous and smokers. The rate of unplanned pregnancy is high and stable, with a higher propensity among women those with the highest risk of unfavorable events during pregnancy and childbirth. Reaching these women in high schools, companies, services and health professionals, in addition to the mass media, can be strategies to prevent unplanned pregnancy.

Este estudo estimou a proporção de puérperas que não planejaram a gravidez, avaliou tendência e identificou fatores associados à sua ocorrência no município de Rio Grande-RS. Entre 01/01 e 31/12 de 2007, 2010, 2013, 2016 e 2019 entrevistadoras treinadas aplicaram questionário único e padronizado a todas as puérperas residentes neste município. Utilizou-se teste qui-quadrado para comparar proporções e regressão de Poisson com ajuste da variância robusta na análise multivariável. A medida de efeito utilizada foi razão de prevalências (RP). O estudo incluiu 12.415 puérperas (98% do total). A prevalência de não planejamento foi 63,3% (IC95%: 62,5%-64,1%). Após ajuste, as maiores RP para não planejamento da gravidez foram observadas entre mulheres de menor idade, cor da pele preta, com companheiro, maior aglomeração domiciliar, pior escolaridade e renda familiar, maior paridade e tabagistas. Houve pequeno aumento na prevalência de não planejamento da gravidez no final do período principalmente entre àquelas com maiores riscos de eventos desfavoráveis na gestação e parto. Alcançar estas mulheres nas escolas de ensino médio, empresas, serviços e profissionais de saúde, além de meios de comunicação de massa, pode auxiliar na prevenção desse tipo de gravidez.

[Usual source of care and Brazilian adolescent access to Primary Health Care (PHC) services]. / Fonte usual de cuidado e o acesso de adolescentes brasileiros a serviços de Atenção Primária à Saúde (APS).

We analyzed the association between the recognition of a usual source of care (USC) of Primary Health Care (PHC) and access to services among Brazilian adolescents. This is a cross-sectional study using data from the National Adolescent School-based Health Survey with 68,968 Brazilian adolescents and cluster sampling. Descriptive analyses were carried out with Pearson's χ2 and prevalence ratios (PR) using logistic regression models between access and recognition of USC. It was observed that 74.6% reported access, and this was higher among females (79.3%). In the multivariate analysis, there was a positive association (PR: 1.25; 95%CI: 1.24-1.26); and, when stratified by sex, positive associations for both sexes, (PR: 1.30; 95%CI: 1.28-1.31) male and (PR: 1.21; 95%CI: 1.20-1.23) female. The majority of Brazilian adolescents demonstrated PHC as a USC and were able to access services, but lack of access was more frequent among the most economically vulnerable and those with risk behaviors, indicating potentially avoidable inequities with more equitable and longitudinal PHC services.

Objetivou-se analisar a associação entre o reconhecimento de uma fonte usual do cuidado de Atenção Primária à Saúde (APS) e o acesso aos serviços de APS, entre adolescentes brasileiros. Estudo transversal, a partir da Pesquisa Nacional de Saúde do Escolar realizada com 68.968 adolescentes brasileiros, através de amostragem por conglomerados. Foram realizadas análises descritivas através do χ2 de Pearson e a razão de prevalência (RP) através dos modelos de regressão logística entre acesso aos serviços de APS e o reconhecimento da FUC APS. Dos adolescentes que procuraram os serviços de APS, 74,6% referiram acesso, sendo a maior do sexo feminino (79,3%). Na análise multivariada, observa-se associação positiva (RP: 1,25; IC95%: 1,24-1,26), e na estratificado por sexo, observou-se associações positivas para ambos os sexos, (RP: 1,30; IC95%: 1,28-1,31) masculino e (RP: 1,21; IC95%: 1,20-1,23) feminino. Verifica-se que a maioria dos adolescentes brasileiros que têm a APS como sua FUC conseguiram acessar os serviços de APS, apesar de que, a falta de acesso foram mais frequentes entre os mais vulneráveis economicamente e devido a comportamentos de risco, indicando iniquidades potencialmente evitáveis por meio de uma APS mais efetiva e longitudinal.

High-risk space-time clusters of mortality in children 5 to 14 years of age in Mato Grosso state, Brazil. / Clusters Espaço-Temporais de Alto Risco da Mortalidade Infantojuvenil em Mato Grosso, Brasil.

The study aimed to detect high-risk areas for deaths of children and adolescents 5 to 14 years of age in the state of Mato Grosso, Brazil, from 2009 to 2020. This was an exploratory ecological study with municipalities as the units of analysis. Considering mortality data from the Mortality Information System (SIM) and demographic data from the Brazilian Institute of Geography and Statistics (IBGE), the study used multivariate statistics to identify space-time clusters of excess mortality risk in this age group. From 5 to 9 years of age, two clusters with high mortality risk were detected; the most likely located in the state's southern mesoregion (RR: 1.6; LRT: 8,53). Among the 5 clusters detected in the 10-14-year age group, the main cluster was in the state's northern mesoregion (RR: 2,26; LRT: 7,84). A reduction in mortality rates was observed in the younger age group and an increase in these rates in the older group. The identification of these clusters, whose analysis merits replication in other parts of Brazil, is the initial stage in the investigation of possible factors associated with morbidity and mortality in this group, still insufficiently explored, and for planning adequate interventions.

O objetivo deste estudo é detectar as áreas de maior risco para óbitos de crianças e adolescentes de 5 a 14 anos no estado de Mato Grosso entre os anos de 2009 e 2020. Estudo ecológico, tipo exploratório, cuja unidade de análise foram os municípios. Considerando dados de mortalidade do SIM e os demográficos do IBGE, o estudo utilizou a estatística multivariada para a identificação dos clusters espaço-temporais de sobrerrisco de mortalidade nesta faixa etária. Dos 5 aos 9 anos, dois clusters de alto risco de mortalidade foram detectados; o mais provável localizado na mesorregião sul (RR: 1,6; LRV: 8,53). Dentre os 5 clusters detectados na faixa etária dos 10 aos 14 anos, o principal foi localizado na mesorregião norte (RR: 2,26; LRV: 7,84). Foi identificada redução das taxas de mortalidade na faixa etária mais jovem e aumento destas taxas na faixa etária mais velha. A identificação destes clusters, cuja análise merece ser replicada a outras partes do território nacional, é a etapa inicial para a investigação de possíveis fatores associados à morbi-mortalidade deste grupo ainda pouco explorado e para o planejamento de intervenções adequadas.

MVMRmode: Introducing an R package for plurality valid estimators for multivariable Mendelian randomisation.

BACKGROUND: Mendelian randomisation (MR) is the use of genetic variants as instrumental variables. Mode-based estimators (MBE) are one of the most popular types of estimators used in univariable-MR studies and is often used as a sensitivity analysis for pleiotropy. However, because there are no plurality valid regression estimators, modal estimators for multivariable-MR have been under-explored. METHODS: We use the residual framework for multivariable-MR to introduce two multivariable modal estimators: multivariable-MBE, which uses IVW to create residuals fed into a traditional plurality valid estimator, and an estimator which instead has the residuals fed into the contamination mixture method (CM), multivariable-CM. We then use Monte-Carlo simulations to explore the performance of these estimators when compared to existing ones and re-analyse the data used by Grant and Burgess (2021) looking at the causal effect of intelligence, education, and household income on Alzheimer's disease as an applied example. RESULTS: In our simulation, we found that multivariable-MBE was generally too variable to be much use. Multivariable-CM produced more precise estimates on the other hand. Multivariable-CM performed better than MR-Egger in almost all settings, and Weighted Median under balanced pleiotropy. However, it underperformed Weighted Median when there was a moderate amount of directional pleiotropy. Our re-analysis supported the conclusion of Grant and Burgess (2021), that intelligence had a protective effect on Alzheimer's disease, while education, and household income do not have a causal effect. CONCLUSIONS: Here we introduced two, non-regression-based, plurality valid estimators for multivariable MR. Of these, "multivariable-CM" which uses IVW to create residuals fed into a contamination-mixture model, performed the best. This estimator uses a plurality of variants valid assumption, and appears to provide precise and unbiased estimates in the presence of balanced pleiotropy and small amounts of directional pleiotropy.

Neurovascular Coupling Analysis Based on Multivariate Variational Gaussian Process Convergent Cross-Mapping.

Neurovascular coupling (NVC) provides important insights into the intricate activity of brain functioning and may aid in the early diagnosis of brain diseases. Emerging evidences have shown that NVC could be assessed by the coupling between electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). However, this endeavor presents significant challenges due to the absence of standardized methodologies and reliable techniques for coupling analysis of these two modalities. In this study, we introduced a novel method, i.e., the collaborative multi-output variational Gaussian process convergent cross-mapping (CMVGP-CCM) approach to advance coupling analysis of EEG and fNIRS. To validate the robustness and reliability of the CMVGP-CCM method, we conducted extensive experiments using chaotic time series models with varying noise levels, sequence lengths, and causal driving strengths. In addition, we employed the CMVGP-CCM method to explore the NVC between EEG and fNIRS signals collected from 26 healthy participants using a working memory (WM) task. Results revealed a significant causal effect of EEG signals, particularly the delta, theta, and alpha frequency bands, on the fNIRS signals during WM. This influence was notably observed in the frontal lobe, and its strength exhibited a decline as cognitive demands increased. This study illuminates the complex connections between brain electrical activity and cerebral blood flow, offering new insights into the underlying NVC mechanisms of WM.

Genetic and Lifestyle Risk Factors of Metabolic Dysfunction-Associated Fatty Liver Disease and Its Relationship with Premature Coronary Artery Disease: A Study on the Pars Cohort.

BACKGROUND: The main objective of this study is to identify the risk factors of metabolic dysfunction-associated fatty liver disease (MAFLD) in coronary artery disease (CAD) patients. METHODS: The present retrospective cohort study is part of the Pars Cohort Study (PCS). The participants were categorized as having MAFLD or not. The pattern of independent variables in patients was compared with those who did not have MAFLD. All variables were retained in the multivariable logistic regression model. RESULTS: Totally, 1862 participants with CAD were enrolled in this study. MAFLD was diagnosed in 647 (40.1%) participants. Gender, diabetes, hypertension, tobacco, opium, alcohol, age, weight, waist circumference, cholesterol, HDL, triglyceride, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) were significantly different in MAFLD and non-MAFLD patients. Also, the results of multivariable logistic regression show male gender (OR=0.651, 95% CI: 0.470‒0.902, P value=0.01) and opium consumption (OR=0.563, 95% CI: 0.328‒0.968, P value<0.001) to be negative risk factors of MAFLD occurrence in CAD patients. Having diabetes (OR=2.414, 95% CI: 1.740-3.349, P value<0.001), high waist circumference (OR=1.078, 95% CI: 1.055‒1.102, P value<0.01), high triglyceride (OR=1.005, 95% CI: 1.001‒1.008, P value=0.006), and high ALT (OR=1.039, 95% CI: 1.026‒1.051, P value<0.01) were positive risk factors of MAFLD in CAD patients. CONCLUSION: Our study found that consuming opium decreases the likelihood of MAFLD in CAD patients, since these patients have decreased appetite and lower body mass index (BMI). On the other hand, female gender, having diabetes, high waist circumference, high triglyceride levels, and high ALT levels increase the probability of MAFLD in CAD patients.

Multivariate analysis applied to X-ray fluorescence to assess soil contamination pathways: case studies of mass magnetic susceptibility in soils near abandoned coal and W/Sn mines.

Determining the origin and pathways of contaminants in the natural environment is key to informing any mitigation process. The mass magnetic susceptibility of soils allows a rapid method to measure the concentration of magnetic minerals, derived from anthropogenic activities such as mining or industrial processes, i.e., smelting metals (technogenic origin), or from the local bedrock (of geogenic origin). This is especially effective when combined with rapid geochemical analyses of soils. The use of multivariate analysis (MVA) elucidates complex multiple-component relationships between soil geochemistry and magnetic susceptibility. In the case of soil mining sites, X-ray fluorescence (XRF) spectroscopic data of soils contaminated by mine waste shows statistically significant relationships between magnetic susceptibility and some base metal species (e.g., Fe, Pb, Zn, etc.). Here, we show how qualitative and quantitative MVA methodologies can be used to assess soil contamination pathways using mass magnetic susceptibility and XRF spectra of soils near abandoned coal and W/Sn mines (NW Portugal). Principal component analysis (PCA) showed how the first two primary components (PC-1 + PC-2) explained 94% of the sample variability, grouped them according to their geochemistry and magnetic susceptibility in to geogenic and technogenic groups. Regression analyses showed a strong positive correlation (R2 > 0.95) between soil geochemistry and magnetic properties at the local scale. These parameters provided an insight into the multi-element variables that control magnetic susceptibility and indicated the possibility of efficient assessment of potentially contaminated sites through mass-specific soil magnetism.