Pontifical Catholic University of Campinas

The literature on digital transformation in micro, small, and medium-sized enterprises (MSMEs) has advanced in a fragmented manner, often focusing separately on digitalization, innovation, or sustainability. This fragmentation has limited understanding of digital maturity within firms and reduced the effectiveness of public policies. The objective of this study is to develop and apply an integrated multivariate statistical model to construct a synthetic index that jointly captures digitalization processes, organizational innovation, and sustainable practices. The Multidimensional Index of Digital Strategy, Innovation, and Sustainability (IMEDIS) represents a methodological contribution that overcomes unidimensional approaches and provides a comparable and representative measure of digital maturity. Based on a sample of 654 Brazilian MSMEs, multivariate statistical techniques were applied to create a typology of digital maturity. The main conclusion is that digital maturity does not result solely from technology adoption but from the interaction of digital strategies, innovation, and sustainability, revealing strong structural heterogeneity across firms. IMEDIS is a useful tool for diagnosing, monitoring, and evaluating public policies and digital strategies for MSMEs, offering an analytical framework transferable to different national contexts.

The Alternative Model for Personality Disorders (AMPD) in the DSM-5 proposes a dimensional view of personality pathology, describing maladaptive variants of normal traits. This study conducted a systematic review and meta-analysis to analyze cross-cultural differences in the associations between AMPD domains, as measured by the Personality Inventory for DSM-5 (PID-5), and the Five-Factor Model (Big Five) of personality. Following PRISMA guidelines, 36 studies comprising 39 independent samples (N = 20,515) were included. Random-effects meta-analyses revealed strong convergence for four domain pairs, namely Negative Affectivity–Neuroticism (r = 0.70; 95% CI [0.66 to 0.73]), Detachment–Extraversion (r = −0.57; 95% CI [−0.53 to −0.62]), Disinhibition–Conscientiousness (r = −0.56; 95% CI [−0.51 to −0.62]), and Antagonism–Agreeableness (r = −0.49; 95% CI [−0.42 to −0.55]), supporting cross-model alignment. The association between Psychoticism and Openness to Experience was weak (r = 0.10; 95% CI [0.06 to 0.15]), indicating distinct constructs. This theoretical divergence was further discussed. Heterogeneity was substantial across analyses, with region explaining part of the variability, as associations were generally stronger in North American and weaker in Asian samples, though sensitivity and publication bias tests confirmed the robustness of results. Findings support the dimensional continuity between adaptive and maladaptive personality traits, but suggest that the Big Five alone do not fully capture pathological variation. Clinically, combining normal-range and maladaptive trait measures offers a more comprehensive approach to personality disorder assessment.

IntroductionObesity is increasingly recognized as a systemic condition with neuroinflammatory and psychobiological implications, yet its integrated relationship with inflammatory and neuroendocrine pathways in mental health remains insufficiently characterized. We investigated the associations between obesity, circulating biomarkers, and symptoms of depression, anxiety, stress, and post-traumatic stress disorder (PTSD).MethodsIn this cross-sectional study, 251 adults were stratified according to psychiatric diagnosis and body mass index (BMI). Inflammatory, neuroendocrine, and metabolic biomarkers were quantified, including cytokines, hs-CRP, cortisol, and vitamin D. Random Forest–based machine learning models were applied to identify the relative importance of biomarkers in predicting psychological symptom severity across clinical groups and BMI categories.ResultsVitamin D, IL-6, TNF-α, and hs-CRP consistently emerged as the most relevant predictors. Individuals with severe obesity but without a formal psychiatric diagnosis exhibited an inflammatory profile comparable to that observed in patients with mental disorders, suggesting the presence of a biomarker profile potentially associated with subclinical psychobiological alterations. In participants with mental disorders, the interaction between BMI and biomarkers was more complex and widespread, indicating a state of systemic inflammatory and neuroendocrine dysregulation.DiscussionThese findings indicate that extremes of nutritional status were associated with a higher frequency of inflammatory and hormonal alterations related to mental health symptoms. The integration of biomarker profiling with machine learning approaches may support early identifying patterns of biomarker relevance associated with mental health symptoms.

INTRODUCTION. One of the causes of the post-operative pain could be the irrigation solution used during the endodontic treatment. AIM. The aim of this study was to evaluate the pain symptoms after endodontic treatment of teeth with necrotic pulp, using two different concentrations of NaOCl during root canal instrumentation, as well as the necessity of using analgesics. MATERIALS AND METHODS. Fifty single-rooted upper incisors or canines were randomly divided into two groups ( n = 25) according to the concentration of NaOCl used: 5.25% or 8.25%. All canals were instrumented with the Reciproc system (R50) and irrigated with 10 mL of the respective chemical solution for each third, and subsequently obturated. Treatments were performed in a single session. Post-operative pain evaluation was conducted at intervals of 24 hours, 48 hours, 72 hours, and 7 days after treatment using a visual analogue scale (VAS), with patients being advised to take 400 mg of Ibuprofen in case of pain. RESULTS. The VAS results were analyzed using the Kruskal-Wallis test, with a significance level of 5%. There was no significant difference between the different concentrations of NaOCl regarding post-operative pain at any of the evaluated times ( p > 0.05). Considering the different evaluation times within the same group, it was observed that at the NaOCl concentration of 8.25%, the pain, although tolerable, was greater after the 24-hour interval compared to later periods. No patient reported the use of analgesics. CONCLUSION. It was concluded that there was no influence of the NaOCl concentration on post-operative pain, as both solutions exhibited similar responses, except at the 24-hour interval, where 8.25% NaOCl exhibited more post-operative pain. There was no need for the use of analgesics after treatment with the solutions used.

Monitoring inland surface water quality is essential for water security, ecosystem conservation, public health, and sustainable water resource management. Although in situ measurements remain indispensable, they are often limited by high costs, restricted spatial coverage, low temporal frequency, and discontinuous monitoring networks. This study presents a systematic literature review, guided by the PRISMA 2020 framework, of empirical studies published between 2021 and 2025 on the integration of artificial intelligence (AI) and remote sensing (RS) for inland surface water quality monitoring. Searches were conducted in the Web of Science database, resulting in a final corpus of 367 peer-reviewed articles. Preliminary bibliometric characterization and qualitative content analysis were performed to identify sensors, platforms, AI paradigms, algorithms, estimated parameters, validation strategies, limitations, challenges, trends, and research gaps. The results show rapid growth in the field, with Sentinel-2 and Landsat-8 as the most recurrent sensors and multispectral data as the dominant spectral source. Machine learning approaches, especially Random Forest, Artificial Neural Networks, XGBoost, and Support Vector Machine, predominated, while deep learning, multi-source integration, hybrid models, and Explainable AI emerged as relevant trends. AI–RS integration shows strong potential to complement conventional monitoring, but persistent challenges remain regarding in situ data dependence, limited external and temporal validation, model transferability, generalization, uncertainty reporting, validation robustness, and interpretability.