Principal Predictors Research Articles

Enhancing University Students' Soft Skills: A Quantitative Study on Problem-Solving and Social Competence

This study investigates the development and interrelation of problem-solving skills and social competence among university students using the quantitative methodological approach. It addresses massive gaps in our knowledge regarding soft skill manifestation and evolution in higher education. Data was collected from 300 university students using validated survey instruments about problem-solving ability, social competence, academic performance, and other related variables through systematic sampling. There were a few significant positive correlations between problem-solving confidence, social competence, and academic performance. Multiple regression analysis demonstrated that problem-solving confidence and problem-solving skills are the principal predictors of academic outcome accounting for 25% of soft skill variance. Furthermore, the investigation indicates large substantive correlations between stress-coping mechanisms and social competence. This predicts that emotional resilience is influential in academic environments. This research analyzes the socioeconomic aspect of skill acquisition. Therefore, it underlines the need for specific education interventions. This contribution of the research to the theoretical discourse on the development of soft skills in higher education is further augmented by providing evidence-based recommendations for curriculum upgrading and institution policy reforms. The results of the study hold enormous implications for pedagogy practices and the development of education policy within higher education institutions.

Prediction of attention deficit hyperactivity disorder based on explainable artificial intelligence

Accurate assessment of Attention Deficit Hyperactivity Disorder (ADHD) is crucial for the effective treatment of affected individuals. Traditionally, psychometric tests such as the WISC-IV have been utilized to gather evidence and identify patterns or factors contributing to ADHD diagnosis. However, in recent years, the use of machine learning (ML) models in conjunction with post-hoc eXplainable Artificial Intelligence (XAI) techniques has improved our ability to make precise predictions and provide transparent explanations. The objective of this study is twofold: firstly, to predict the likelihood of an individual receiving an ADHD diagnosis using ML algorithms, and secondly, to offer interpretable insights into the decision-making process of the ML model. The dataset under scrutiny comprises 694 cases collected over the past decade in Spain, including information on age, gender, and WISC-IV test scores. The outcome variable is the professional diagnosis. Diverse ML algorithms representing various learning styles were rigorously evaluated through a stratified 10-fold cross-validation, with performance assessed using key metrics, including accuracy, area under the receiver operating characteristic curve, sensitivity, and specificity. Models were compared using both the full set of initial features and a well-suited wrapper-type feature selection algorithm (Boruta). Following the identification of the most suitable model, Shapley additive values were computed to assign weights to each predictor based on their additive contribution to the outcome and to elucidate the predictions. Strikingly, a reduced set of 8 out of the initial 20 variables produced results comparable to using the full feature set. Among the ML models tested, the Random Forest algorithm outperformed others on most metrics (ACC = 0.90, AUC = 0.94, Sensitivity = 0.91, Specificity = 0.92). Notably, the principal predictors, ranked by importance, included GAI – CPI, WMI, CPI, PSI, VCI, WMI – PSI, PRI, and LN. Individual case examples exhibit variations in predictions depending on unique characteristics, including instances of false positives and negatives. Our ML model adeptly predicted ADHD diagnoses in 90% of cases, with potential for further enhancement by expanding our database. Furthermore, the use of XAI techniques enables the elucidation of salient factors in individual cases, thereby aiding inexperienced professionals in the diagnostic process and facilitating comparison with expert assessments. It is important to note that this tool is designed to support the ADHD diagnostic process, where the medical professional always has the final say in decision-making.

Evaluation of Renal Function with Urinary NGAL and Doppler Ultrasonography in ICU Patients: A 1-Year Observational Pilot Study.

We estimated the diagnostic accuracy of urinary NGAL for the diagnosis of AKI. Urinary NGAL and Creatinine were measured daily for up to 3 days. Doppler ultrasonography was performed within 24 h of admission and for the following 3 days. Of the 21 patients, 44% had AKI during their ICU stay. The AKI group presented with higher values of serum Creatinine, renal length, MDRD as well as SAPS II already at admission. Urinary NGAL was significantly higher among patients with AKI and patients AKI-no at T0 (p < 0.0001) and increased steadily on T1 and T2. Urinary NGAL seemed to be a notable diagnostic marker for AKI from the first measurement (T0) with an area under the ROC of 0.93 (95% CI = 0.78-0.99) with a sensitivity of 99%. RRI levels were slightly higher in the AKI group at each time and increased gradually from T0 to T2 but reached statistical significance only at T2 (p = 0.02). Renal length and SAPS II at T0 showed high AuRoc and sensitivity. Urinary NGAL is a valuable marker for AKI in intensive care settings. It seemed that a pre-existing chronic renal disease, the SAPS II and the NGAL at admission represented the principal predictors of AKI.

Preoperative Shoulder MRI Findings to Predict Subscapularis Tendon Tear Requiring Surgical Repair.

This study aimed to investigate which indirect parameters on preoperative MRI were the principal predictors of subscapularis tendon tears (STTs) requiring surgical repair. Preoperative MRI scans of 86 patients were retrospectively reviewed for visual assessment of the STT, pathology of the long head of the biceps tendon (LHBT), posterior decentering (PD) of the humeral head, humeral rotation, fatty degeneration, and subscapularis muscle atrophy. To evaluate atrophy, visual grading using the anatomical line connecting the coracoid tip to the glenoid base, designated as the base-to-tip line (BTL), and thickness measurements were performed in the en-face view. Arthroscopically, 31 patients (36%) exhibited Lafosse type III or IV STT and underwent surgical repair. LHBT pathology (p = 0.002), PD of the humeral head (p = 0.012), fatty degeneration (p < 0.001), and BTL grade (p = 0.003) significantly correlated with STT. In the multivariate analysis, PD of the humeral head (p = 0.011, odds ratio [OR] = 5.14) and fatty degeneration (p = 0.046, OR = 2.81) were independent predictors of STT. PD of the humeral head and fatty degeneration of the subscapularis can help to diagnose clinically significant STT. Interpretation of these findings may contribute to the planning of an optimal surgical strategy.

Harnessing Code Interpreters for Enhanced Predictive Modeling: A Case Study on High-Density Lipoprotein Level Estimation in Romanian Diabetic Patients.

Diabetes is a condition accompanied by the alteration of body parameters, including those related to lipids like triglyceride (TG), low-density lipoproteins (LDLs), and high-density lipoproteins (HDLs). The latter are grouped under the term dyslipidemia and are considered a risk factor for cardiovascular events. In the present work, we analyzed the complex relationships between twelve parameters (disease status, age, sex, body mass index, systolic blood pressure, diastolic blood pressure, TG, HDL, LDL, glucose, HbA1c levels, and disease onset) of patients with diabetes from Romania. An initial prospective analysis showed that HDL is inversely correlated with most of the parameters; therefore, we further analyzed the dependence of HDLs on the other factors. The analysis was conducted with the Code Interpreter plugin of ChatGPT, which was used to build several models from which Random Forest performed best. The principal predictors of HDLs were TG, LDL, and HbA1c levels. Random Forest models were used to model all parameters, showing that blood pressure and HbA1c can be predicted based on the other parameters with the least error, while the less predictable parameters were TG and LDL levels. By conducting the present study using the ChatGPT Code Interpreter, we show that elaborate analysis methods are at hand and easy to apply by researchers with limited computational resources. The insight that can be gained from such an approach, such as what we obtained on HDL level predictors in diabetes, could be relevant for deriving novel management strategies and therapeutic approaches.

Prostitution in Bolivia: An analysis of attitudes and perceptions

AbstractPerceptions about prostitution in Bolivia are analyzed through a survey conducted in February 2021. The results show that while prostitution is generally viewed as immoral, most people surveyed think that buying and selling sex should be legal. Utilizing least squares and probit regressions, we find that the principal predictors of the morality of the sex industry and on whether it should be legal are gender, attitude toward sex, rape myths, political persuasion, stance on gender equality, and beliefs regarding the size of government. Men and sexual liberals are inclined to view prostitution more favorably, whereas people with politically conservative views, those with strong beliefs in gender equality, and those who favor a bigger role for government tilt in the opposite direction. The main recommendation from these findings is that a national legal framework should be established to formalize the industry.

Pathophysiology and Management of Glycemic Alterations before and after Surgery for Pheochromocytoma and Paraganglioma

Glycemic alterations are frequent in patients with pheochromocytoma and paraganglioma (PPGL), but the real incidence of secondary diabetes mellitus (DM) is uncertain, because prospective multicenter studies on this topic are lacking in the literature. The main pathophysiological mechanisms of glucose homeostasis alterations in PPGL, related to catecholamine hypersecretion, are impaired insulin and glucagon-like peptide type 1 (GLP-1) secretion and increased insulin resistance. Moreover, it has been reported that different pathways leading to glucose intolerance may be related to the secretory phenotype of the chromaffin tumor. Predictive factors for the development of glucose intolerance in PPGL patients are a higher age at diagnosis, the need for a higher number of anti-hypertensive drugs, and the presence of secreting neoplasms. Tumor resection is strongly related to the resolution of DM in PPGL patients, with a significant improvement of glycemic control in most cases. We can hypothesize a different personalized therapeutic approach based on the secretory phenotype. The adrenergic phenotype is more closely related to reduced insulin secretion, so insulin therapy may be required. On the other hand, the noradrenergic phenotype mainly acts by increasing insulin resistance and, therefore, insulin-sensitizing antidiabetic agents can find a greater application. Regarding GLP-1 receptor agonists, the data suggest a possible promising therapeutic effect, based on the assumption that GLP-1 secretion is impaired in patients with PPGL. The principal predictors of remission of glycemic alterations after surgery for PPGL are a lower preoperative body mass index (BMI), a larger tumor, higher preoperative catecholamine levels, and a shorter duration of the disease (under three years). Otherwise, after resection of PPGL, hypoglycemia can occur as the result of an excessive rebound of preoperative hyperinsulinemia. It is a rare, but potentially severe complication reported in a lot of case reports and a few small retrospective studies. Higher 24-h urinary metanephrine levels, longer operative times and larger tumors are predictive factors for hypoglycemia in this setting. In conclusion, alterations of carbohydrate metabolism are clinically relevant manifestations of PPGL before and after surgery, but there is the need to conduct multicenter prospective studies to obtain an adequate sample size, and to allow the creation of shared strategies for the clinical management of these potentially severe manifestations of PPGL.

Differentiation of Transylvanian fruit distillates using supervised statistical tools based on isotopic and elemental fingerprint.

The spirit drinks industry is one of the largest in the world. Fruit distillates require adequate analysis methods combined with statistical tools to build differentiation models, according to distinct criteria (geographical and botanical origin, producer's fingerprint, respectively). Over time a database of alcoholic beverage fingerprints can be generated, being very important for product safety and authenticity control. To control the distillates' geographical origin, linear discriminant analysis (LDA) revealed that the cross-validation classification was correct for 88.2% of samples, but partial least squares discriminant analysis (PLS-DA) was slightly better suited for this purpose, with a correct classification rate of 91.2%. LDA effectiveness was proven for the trademark fingerprint differentiation, which was achieved at 93.5%, compared to 89.1% for PLS-DA. The principal predictors obtained by LDA were the same both for geographical origin and producer differentiation: B, δ13 C, Na, Cu, Ca and Be; highlighting the fact that in the production process of distillates each producer used fruits coming from the respective specific region. Through PLS-DA, some of the discrimination markers were the same for geographical origin and producer's identification, but others were completely specific: the rare earth elements Eu and Er only for geographical origin differentiation, and Cu solely as predictor for producer's identification. Regarding distillates' fruit variety, the correct discrimination rates of plum spirits from the rest were 84.2% for PLS-DA and 63% for LDA. LDA and PLS-DA were suitable for differentiation models development of fruits spirits according to geographical region, producer and fruit variety based on isotopic and elemental fingerprint. © 2022 Society of Chemical Industry.

Abstract 5706: Epigenetic signatures in association with breast cancer mortality and prognosis that are specific to tumor subtype and menopausal status

Abstract Background: Despite advancements in screening and treatment options for breast cancer (BC), mortality from BC has increased, accounting for 15% of new cancer deaths in the U.S. in 2020. BC is heterogeneous cancer with different molecular subtypes defined by hormone-receptor status, and the prognoses of BC patients differ by their menopausal status in conjunction with their molecular subtypes. Epigenetic dysregulation patterns can serve as principal predictors of BC progression and mortality by capturing these differences in biology that are linked to BC outcomes, leading to personalized treatment plans. In this study, we aimed to detect subtype- or menopausal-specific DNA methylation (DNAm) in BC tumor tissues across the genome that are associated with BC progression or all-cause mortality. Methods: Associations between site-specific DNAm in tumor and BC survival were evaluated using the Illumina Infinium HumanMethylation450 BeadChip array data from the Cancer Genome Atlas participants. Cox proportional hazards models were used in the strata by tumor subtypes and menopausal status, adjusting for age, race, cancer stage, tumor purity, and cell type proportion. Detected site-specific DNAm was further analyzed to identify subtype- or menopausal-specific differentially methylated regions (DMRs) and functional pathways. The validation of the results was carried out on an independent dataset. Results: We identified eight CpG probes that were associated with survival outcomes in subtype- or menopausal-specific manner. Seven probes among women with luminal A subtype were associated with all-cause mortality or progression-free interval (PFI); four probes among women with luminal B were associated with PFI; and five probes among post-menopausal women were associated with PFI. Of them, six probes presented a lower risk of all-cause mortality or BC progression with higher DNAm. The identified DMRs differed by tumor subtype and menopausal status, and a majority of DMRs were driven by several weaker associations in the same direction among proximal loci. Also, we identified subtype- or menopausal-specific functional genomic pathways. Particularly, a pathway of insulin regulation was associated with all-cause mortality and PFI among pre-menopausal women. The detected site-specific probes were validated with consistent directions and magnitudes of effects on prognosis across two independent datasets. Conclusion: We identified subtype- or menopausal-specific DNAm in BC tumor tissues, DMRs, and functional pathways in association with all-cause mortality or BC progression. Our findings warrant future studies with larger independent datasets for replication and functional implications of detected DNAm. Our detected genome-wide-associated-CpG loci could improve prognosis prediction for BC patients, thus contributing to tailored therapeutic regimens. Citation Format: Do Hyun Kim, Alexandra M. Binder, Hua Zhou, Su Yon Jung. Epigenetic signatures in association with breast cancer mortality and prognosis that are specific to tumor subtype and menopausal status [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5706.

Prevalence and Socio-Demographic Factors of Bipolar Mood Disorders in Children and Adolescents: Identifying the Principal Predictors

Objectives: Our objective is to measure the prevalence of bipolar mood disorder (BMD) in Iranian children and adolescents and its comorbidity with psychiatric disorders. Also, the main purpose of this study is to characterize the main risk factors for BMD in children and adolescents. Methods: This is an analytical cross-sectional study. In a community-based study, we sampled 1000 children and adolescents from the age of 6 to 18 years in each province via the multistage cluster sampling method. The total valid sample size reached 29 812 cases. The instructed clinical psychologists completed the Persian version of the kiddie schedule for affective disorders and schizophrenia present and lifetime version (K-SADS-PL). Furthermore, the demographic data were obtained. To analyze the data, descriptive statistics, multinomial, and multiple logistic regressions were utilized to evaluate the relationships. Results: The total prevalence rates for BMD were 0.29%; it was 0.26% in males and 0.29% in females. BMD rates were larger in children and adolescents whose mothers had an occupation. Also, after controlling the effective variables (sex and age), location (rural or urban), the father’s education, and the psychiatric hospitalization of the mother or the father, none predicted BMD significantly. Moreover, patients with comorbidities showed a superior prevalence compared to those without comorbidities, ranging from 1.96% for posttraumatic stress disorder to 39.22% for the oppositional defiant disorder. Conclusion: BMD was more prevalent among women. The gender or the father’s education level was not the risk factor for BMD symptoms. Several factors, such as maternal education and maternal job were also important for the prevalence of BMD symptoms.

Domestic dogs (Canis familiaris) grieve over the loss of a conspecific

Behavioural reactions towards a dead conspecific have been observed rarely in wild canids and there is no documented scientific evidence of grief in pet dogs. A quantitative analysis of grief-related responses in both dogs and owners was conducted, using the validated online Mourning Dog Questionnaire. The survey was completed by 426 Italian adults who had owned at least two dogs, one of whom died while the other was still alive. This research aims to explore whether, how and what a dog may experience over the loss of a companion dog. Multiple logistic regression indicates that both a friendly or parental relationship between two dogs but also the fact that dogs used to share food and the owner’s grief and anger are principal predictors of negative behavioural changes. According to dog owners’ answers, the surviving dog after the death of the companion dog changed both in terms of activities (“playing”, “sleeping”, and “eating”) and emotions (fearfulness), which occurred as a function of the quality of the relationship between the two animals. By contrast, the time the two dogs had spent together had no effect on the behaviours of surviving dog. Owner perceptions about their dog’s reactions and emotions were not related to the memory or suffering of the event that tended to diminish over time. These findings indicate that a dog may show grief-related behavioural and emotional patterns when a close conspecific dies, with aspects of the latter possibly related to the owner’s emotional status.

Motivated to eat green or your greens? Comparing the role of motivation towards the environment and for eating regulation on ecological eating behaviours – A Self-Determination Theory perspective

Agriculture being responsible for 35% of gas emission worldwide (Foley, 2011), it is now relevant more than ever before to adopt more sustainable eating behaviours. Given its nature, ecological eating is considered both an eating and an environmental behaviour. The literature, however, has yet to identify whether ecological eating is primarily associated with motivation towards the environment or the regulation of eating. Using Self-Determination Theory framework, the present research aimed to identify the principal motivational predictors of ecological eating by using a path model combining and comparing the predicting value of eating and environmental motivation on ecological eating behaviours. Results suggest that ecological eating predominately belongs to the environmental domain, is mainly related to self-determined motivation towards the environment, and to a lesser extent, self-determined motivation towards eating. Understanding the motivational processes underlying ecological eating is critical to designing efficient intervention and directing future research.

Global solar radiation predictions in Fiji Islands based on empirical models

Many difficulties associated with Global Solar Radiation (GSR)’ variability in space and time and with the angular position in sky hemisphere hamper its accurate knowledge. Fiji Islands, with a tropical maritime climate without great extremes of heat or cold, have GSR measurement limited by a small number of stations due to the technical and financial restricts. Yet there are various options of available empirical models to select the most suitable one(s) for estimating GSR accurately using weather-related variables available for each location. This paper, therefore, focuses on evaluation of the performances of twenty (20) empirical models in predicting GSR between 1984 and 2018 in six (6) meteorological stations within Fiji Islands. Simulations were performed for 35-year period (1984–2018) and results were compared with the National Aeronautics and Space Administration (NASA)’s MERRA (Modern-Era Retrospective Analysis for Research and Applications) observational datasets after bias corrections. Model performance indices such as Mean Error (ME), Nash-Sutcliffe Efficiency coefficient (NSE), Percentage Mean Error (PME), Root Mean Square Error (RMSE) and correlation coefficient (r) were adopted. Obtained coefficients of determination from the regression analysis (0.415 ≤ R2 ≤ 0.988; at 95% confidential level) performed for the models’ calibration between 1984 and 2018 suggest that the models’ potentials to simulate GSR varies across the stations and among the models. About 33% of the models in group I, which relate GSR to the sunshine duration, monthly average day length and/or extra-terrestrial radiation performed favourably well in predicting GSR while 75% of models in group II (with air temperature as additional predictor) and 100% in group III (humidity) performed very well. It is concluded that inclusion of air temperature and humidity as two principal predictors alongside with sunshine duration, day length and extra-terrestrial radiation produce the best GSR prediction (RMSE ≤ 5 MJ m−2) over Fiji Islands.

Recipient obesity as a risk factor in kidney transplantation

BackgroundThe aim of the study was to investigate the effect of recipient obesity on the short- and long-term outcomes of patients undergoing primary kidney transplantation (KT).Patients and methodsA total of 578 patients receiving primary KT in our department between 1993 and 2017 were included in the study. Patients were divided according to their body mass index (BMI) into normal weight (BMI 18.5–24.9 kg/m2; N = 304), overweight (BMI 25–29.9 kg/m2; N = 205) and obese (BMI ≥ 30 kg/m2; N = 69) groups. Their clinicopathological characteristics, outcomes, and survival rates were analyzed retrospectively.ResultsObesity was associated with an increased rate of surgical complications such as wound infection (P < 0.001), fascial dehiscence (P = 0.023), and lymphoceles (P = 0.010). Furthermore, the hospital stay duration was significantly longer in the groups with obese patients compared to normal weight and overweight patients (normal weight: 22 days, overweight: 25 days, and obese: 33 days, respectively; P < 0.001). Multivariate analysis showed that recipient obesity (BMI ≥ 30) was an independent prognostic factor for delayed graft function (DGF) (OR 2.400; 95% CI, 1.365–4.219; P = 0.002) and postoperative surgical complications (OR 2.514; 95% CI, 1.230–5.136; P = 0.011). The mean death-censored graft survival was significantly lower in obese patients (normal weight: 16.3 ± 0.6 years, overweight: 16.3 ± 0.8 years, obese 10.8 ± 1.5 years, respectively; P = 0.001). However, when using the Cox proportional hazards model, the association between recipient obesity and death-censored renal graft failure disappeared, after adjustment for important covariates, whereas the principal independent predictors of graft loss were recipient diabetes mellitus and hypertension and kidneys from donors with expanded donor criteria.ConclusionIn conclusion, obesity increases the risk of DGF and post-operative surgical complications after primary KT. Appropriate risk-adapted information concerning this must be provided to such patients before KT. Furthermore, obesity-typical concomitant diseases seem to negatively influence graft survival and need to be considered after the transplantation of obese patients.

Multivariate Functional Regression Via Nested Reduced-Rank Regularization

We propose a nested reduced-rank regression (NRRR) approach in fitting a regression model with multivariate functional responses and predictors to achieve tailored dimension reduction and facilitate model interpretation and visualization. Our approach is based on a two-level low-rank structure imposed on the functional regression surfaces. A global low-rank structure identifies a small set of latent principal functional responses and predictors that drives the underlying regression association. A local low-rank structure then controls the complexity and smoothness of the association between the principal functional responses and predictors. The functional problem boils down to an integrated matrix approximation task through basis expansion, where the blocks of an integrated low-rank matrix share some common row space and/or column space. This nested reduced-rank structure also finds potential applications in multivariate time series modeling and tensor regression. A blockwise coordinate descent algorithm is developed. We establish the consistency of NRRR and show through nonasymptotic analysis that it can achieve at least a comparable error rate to that of the reduced-rank regression. Simulation studies demonstrate the effectiveness of NRRR. We apply the proposed methods in an electricity demand problem to relate daily electricity consumption trajectories with daily temperatures. Supplementary files for this article are available online.

Assessing habitat quality on alpha and beta diversity of Odonata larvae (Insect) in logging areas in Amazon forest

The present study evaluated the effects of habitat quality on the alpha and beta diversity of odonate larvae under conditions of reduced impact and conventional logging. We hypothesized that the variation in the abiotic conditions found in areas of conventional logging would result in a greater loss of alpha and beta diversity in these areas in comparison with areas of reduced-impact logging and native forest. The study area was located in northeastern Para state, in northern Brazil. We analyzed data from 10 control streams, located in areas of preserved native forest, 11 streams in forest harvested by reduced-impact logging, and nine streams in areas that had been logged conventionally. Environmental factors such as dissolved oxygen, woody debris, water temperature, sediments, and canopy cover were the principal predictors of the diversity of the Odonata. Our results showed that turnover was the principal component structuring beta diversity in the three areas (native forest, reduced impact and conventional logging). The results of the present study indicate that management initiatives based on reduced-impact logging techniques could be adopted to guarantee socio-economic benefits while minimizing the impacts of logging on local biodiversity.

Bolstering geometric morphometrics sample sizes with damaged and pathologic specimens: Is near enough good enough?

Obtaining coordinate data for geometric morphometric studies often involves the sampling of dry skeletal specimens from museum collections. But many specimens exhibit damage and/or pathologic conditions. Such specimens can be considered inadequate for the analyses of shape and are excluded from study. However, the influences that damaged specimens may have on the assessment of normal shape variation have only been explored in two-dimensional coordinate data and no studies have addressed the inclusion of pathological specimens to date. We collected three-dimensional coordinate data from the cranium and mandible of 100 crab-eating macaques (Macaca fascicularis). Tests typically employed to analyze shape variation were performed on five datasets that included specimens with varying degrees of damage/pathology. We hypothesized that the inclusion of these specimens into larger datasets would strengthen statistical support for dominant biological predictors of shape, such as sex and size. However, we also anticipated that the analysis of only the most questionable specimens may confound statistical outputs. We then analyzed a small sample of good quality specimens bolstered by specimens that would generally be excluded due to damage or pathologic morphology and compared the results with previous analyses. The inclusion of damaged/pathologic specimens in a larger dataset resulted in increased variation linked to allometry, sexual dimorphism, and covariation, supporting our initial hypothesis. We found that analyzing the most questionable specimens alone gave consistent results for the most dominant aspects of shape but could affect outputs for less influential principal components and predictors. The small dataset bolstered with damaged/pathologic specimens provided an adequate assessment of the major components of shape, but finer scale differences were also identified. We suggest that normal and repeatable variation contributed by specimens exhibiting damage and/or pathology emphasize the dominant components and shape predictors in larger datasets, however, the various unique conditions may be more influential for limited sample sizes. Furthermore, we find that exclusion of damaged/pathologic specimens can, in some cases, omit important demographic-specific shape variation of groups of individuals more likely to exhibit these conditions. These findings provide a strong case for inclusion of these specimens into studies that focus on the dominant aspects of intraspecific shape variation. However, they may present issues when testing hypotheses relating to more fine-scale aspects of morphology.

Anti-neutrophil cytoplasmic antibodies-associated vasculitis: a guide and case study.

Vasculitis is a relatively rare and poorly understood condition causing inflammation of the blood vessels, which in turn can affect a patient's respiratory and renal systems. In some cases, ocular involvement can cause loss of sight and hearing loss may also be a red flag for vasculitis, which, if not treated early, can cause complete hearing loss. Anti-neutrophil cytoplasmic antibodies (ANCA)-associated vasculitis (AAV) is a group comprising granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA) and eosinophilic granulomatosis (EGP). AAV is fatal if untreated and as increased risk escalates with age, coupled with a decline in renal function, these are the principal predictors of poor outcome. Vital roles for nursing vasculitis patients lie in managing inflammation and pain, as these distressing symptoms are prevalent in the disease. Because of the multiple complications that can occur with vasculitis, treatment-related information is a high priority for these patients. As nurses are well placed to deliver information, value lies in their role in reducing the negative impacts on treatment regimens and compliance that accompany patients' poor insight into their condition.

A comparative analysis of predictors of teacher self‐efficacy in student engagement, instruction and classroom management in Nordic, Anglo‐Saxon and East and South‐East Asian countries

Going beyond previous studies, we investigated differential effects of teacher self‐efficacy (TSE) across the three basic dimensions of educational equality in student engagement, instructional strategies and classroom management in East and South‐East Asian, Anglo‐Saxon and Nordic country clusters in a cross‐cultural analysis. It was found that all three domains of TSE show different patterns of associations in relation to teacher, classroom, principal and school predictors across the three country clusters. Most variation occurred within the classroom and principal predictors, whereas the teacher and school predictors were more homogeneously related to the three country clusters and the three domains of TSE. However, there were more similarities between the Nordic and Anglo‐Saxon clusters than the East and South‐East Asian clusters and any of the other two. Cultural values, as found in the GLOBE study and by Hofstede, were used as a cultural framework to interpret the differences occurring in the country clusters.

Clinical Profile, Treatment and Predictors during the First COVID-19 Wave: A Population-Based Registry Analysis from Castile and Leon Hospitals.

The first wave of the COVID-19 pandemic collapsed the hospitals in Castile and Leon (Spain). An analysis of the clinical characteristics, drug therapies and principal outcome predictors in the COVID-19 hospitalized patients from 1 March to 31 May 2020 is presented through a population-based registry study. Hospital stay variables, ventilation mode data and clinical outcomes were observed. In Castile and Leon hospitals, 7307 COVID-19 patients were admitted, with 57.05% being male and a median of 76 years. The mortality rate was 24.43%, with a high incidence of severe acute respiratory syndrome (SARS) (14.03%) and acute kidney injury (AKI) (10.87%). The most used medicines were antibiotics (90.83%), antimalarials (42.63%), steroids (44.37%) and antivirals, such as lopinavir/ritonavir (42.63%). The use of tocilizumab (9.37%) and anti-SIRS (systemic inflammatory response syndrome) medicines (7.34%) were remarkable. Fundamentally, death occurred more likely over 65 years of age (OR: 9.05). In addition, the need for ventilation was associated with a higher probability of death (OR: 3.59), SARS (OR: 5.14) and AKI (OR: 2.31). The drug-use pattern had been modified throughout the COVID-19 first wave. Multiple factors, such as age, gender and the need for mechanical ventilation, were related to the worst evolution prognosis of the disease.