Background: Occupational cancer is one of the most common occupational diseases. Identifying exposure to carcinogens is important both for implementing measures to prevent and control the risk of exposure and for the post-exposure medical surveillance of workers. The purpose of this study is to develop and validate a questionnaire that can be used in clinical practice to identify exposure and ultimately facilitate the diagnosis of occupational cancer. Methods: The first step involves designing the questionnaire, which will then be validated in the second step by ensuring it meets all the validation criteria for a questionnaire. Results: Scientific validation, logical validation, and construct validation were completed to ensure that the questionnaire is appropriate both structurally and in terms of content. The questionnaire was evaluated by comparing it to a reference standard, and the results, based on the exposure risk level category, showed good agreement (k = 0.719). The intra-observer and inter-observer reliability tests showed an excellent concordance for exposure risk level category (k = 1.000 and k = 0.945). Conclusions: The questionnaire was developed and validated to identify the risk of occupational cancer, and its systematic use in Romania could make a better selection of subjects who should be included in the post-exposure surveillance.
Deep brain stimulation (DBS) remains one of the most effective advanced therapies for Parkinson’s disease (PD), yet considerable heterogeneity persists in clinical outcomes, patient selection, and long-term neuropsychiatric effects. This narrative review synthesizes recent evidence on the mechanisms, efficacy, and evolving applications of DBS, integrating findings from neurophysiology, neuroimaging, genetics, and computational modeling. Current data indicate that stimulation of the subthalamic nucleus (STN) and globus pallidus internus (GPi) reliably improves cardinal motor symptoms and reduces dopaminergic medication requirements, with sustained benefits over time. However, axial symptoms, including gait disturbance, postural instability, and speech, demonstrate variable or limited responsiveness, underscoring the complexity of the underlying neural circuits. Non-motor outcomes also show substantial variability: sleep and gastrointestinal functions often improve, whereas apathy, impulsivity, and speech disturbances may emerge or worsen in selected patients. Emerging biomarkers, including evoked cortical responses, oscillatory signatures, radiomic features, and gray matter morphometry, show promise in refining patient-specific predictions, although external validation remains limited. Genetic variants, particularly in GBA and LRRK2, further modulate cognitive trajectories and long-term outcomes. Advances in imaging-guided “asleep” DBS, directional leads, and personalized programming strategies have improved precision and safety. Concurrently, machine learning and adaptive DBS paradigms represent major future directions, enabling real-time, biomarker-driven modulation tailored to disease dynamics. By integrating multidomain evidence, this review highlights both the established therapeutic value and the persistent challenges of DBS, emphasizing the need for mechanistically informed, precision-based approaches. These developments collectively outline a path toward more individualized and circuit- specific neuromodulation strategies in PD.
Whether [18F]FDG PET/CT radiomic features provide information on tumor proliferation beyond conventional PET parameters remains uncertain. We investigated their associations with Ki-67 in untreated primary breast carcinoma using a predefined panel of technically reproducible features. This retrospective, single-center study included 42 women and 19 radiomic features previously selected based on interobserver reproducibility and redundancy, independently of Ki-67. Associations with continuous Ki-67 were evaluated using Spearman correlations, permutation tests, bootstrap confidence intervals, and false discovery rate (FDR) correction, alongside four conventional PET parameters. Five radiomic features showed significant associations with Ki-67 after FDR correction. The strongest association was observed for firstorder_90Percentile (ρ = 0.556; 95% CI, 0.279–0.745; q = 0.002), comparable to that of SUVmax (ρ = 0.565). However, none of these five associations remained significant after adjustment for metabolic tumor volume (MTV), SUVmax, or both, followed by FDR correction. Excluding six small lesions attenuated the correlations for both SUVmax (ρ = 0.440) and firstorder_90Percentile (ρ = 0.427). Although several radiomic features were associated with Ki-67, an independent contribution beyond conventional PET parameters was not demonstrated. These findings require confirmation in independent cohorts.
Background: Artificial intelligence (AI) is increasingly investigated for radiographic assessment of peri-implant bone conditions, although radiographic marginal bone loss does not alone establish a clinical diagnosis of peri-implantitis. This narrative review aimed to critically synthesize current evidence on AI-assisted radiographic assessment of peri-implant marginal bone levels and bone loss. Methods: A structured literature search was conducted in PubMed/MEDLINE and Scopus. Eligible original studies applying AI or machine-learning methods to human dental radiographs for peri-implant bone assessment were included in a qualitative synthesis. Results: Seventeen studies were included. Periapical radiographs were the predominant imaging modality, while panoramic radiographs and CBCT were less frequently investigated. AI applications included implant and landmark localization, marginal bone-level measurement and quantification, bone-loss severity and defect-morphology classification, and prediction using imaging-derived features. Reported performance was generally promising, although substantial heterogeneity was observed in datasets, target tasks, reference standards, validation strategies, and performance metrics. Conclusions: Current radiograph-based AI systems show potential for image analysis, quantitative assessment, and clinical decision support. However, radiographic AI performance should not be interpreted as autonomous diagnostic accuracy for peri-implantitis. External validation, longitudinal assessment, and integration with relevant clinical parameters remain priorities for future research.
Biological data have grown exponentially due to single-cell multi-omics, high-content imaging, and large-scale clinical records; this has transformed modern biology. The landscape has shifted to one of data abundance, and the current approaches to computational analysis are not up to the task of dealing with the scale and complexity of this change. The information in big biological data can be characterized by the volume it occupies, velocity, variety, veracity, and value, and each of these features presents its own challenges to the researcher. Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and natural language processing (NLP), has progressively turned into a requirement in tackling these issues. This chapter discusses how AI can be used to curate, represent, learn, and model the analysis of big biological data. Questions include data curation via methods to compress, represent, and outline big biological data, how to learn representations, how to model with big biological data, and how to translate into clinical problems. Using the conversation of genomics, proteomics, transcriptomics, structural biology, imaging, and electronic health records, this chapter reveals how AI is transformative in bioinformatics. In conclusion, the chapter points to enduring challenges, namely interpretability, data governance, and reproducibility, and provides a step into the future of explainable, trustworthy, and reproducible AI in biology.
The process of aging population is increasing the relevance of comprehending age-related changes in skin structure and function, as well as their implications for clinical care. This narrative review explores aspects of the intrinsic and extrinsic mechanisms of skin aging, their molecular and morphological consequences, and their clinical expression, with particular attention to chronic cutaneous insufficiency. The intrinsic aging process is reflecting declining cellular functionality, impaired tissue homeostasis, and endocrine changes, especially regarding estrogen deficiency after menopause. These processes are reducing collagen synthesis, hydration, and elasticity and thus contributing to epidermal and dermal atrophy and barrier dysfunction. On the other hand, extrinsic aging is driven mainly by cumulative ultraviolet exposure, with additional contributions from pollution, smoking, diet, and behavioral and psychosocial factors. Processes such as oxidative stress, inflammation, DNA damage, glycation, and increased matrix metalloproteinase activity promote extracellular matrix degradation, linking the environmental exposure to the photoaging and cutaneous carcinogenesis. Main clinical manifestations include xerosis, chronic pruritus, pigmentary changes, benign lesions, actinic keratoses, and basal cell and squamous cell carcinomas. Inflammatory, allergic, autoimmune, and drug-induced dermatoses, including psoriasis, lichen planus, and bullous pemphigoid, are complicating healthcare in older adults, while systemic comorbidities and medication use are influencing clinical presentation and treatment. Dermatoporosis is characterized by cutaneous atrophy, purpura, pseudoscars, and susceptibility to injury after minor trauma. Reduced hyaluronic acid availability and CD44 expression contribute to impaired hydration and mechanical resilience, potentially leading to ulceration and deep dissecting hematomas. Management measures are emphasizing the role of photoprotection, skin hydration, trauma prevention, and reduction of modifiable exposures, with corticosteroid use warranting reassessment in secondary dermatoporosis. Other discussed approaches are topical hyaluronic acid, retinoids, alpha-hydroxy acids, and vitamin C, but the proposed interventions have limited supporting evidence. Therefore, it is considered that early recognition of these interconnected changes may be supportive for planning prevention, a timely diagnosis, and individualized care, based on considerations like skin fragility, associated disease, and treatment burden in older patients.
Background/Objective: In order to address the current knowledge gap with regard to serum calcium-to-magnesium ratio (Ca/Mg) in patients with adrenal incidentalomas, we aimed to analyze Ca/Mg in menopausal women diagnosed with these tumors versus (tumor-free) controls across DXA categories. Methods. This is a retrospective, real-world, cross-sectional study in 474 menopausal women admitted at a single tertiary center of endocrinology. Results. Tumor group (N=111) had osteoporosis/osteopenia/normal DXA prevalence: 17.1%, 36.9% and 46%, respectively, control group: 13.8%, 45.5%, and 40.8%. Ca/Mg ratio was similar between DXA sub-groups in each group. Tumor group: Ca/Mg corelated with age (r=0.288, p=0.047), P1NP (r= -0.459, p=0.011), osteocalcin (r= -0.37, p=0.026) and CrossLaps (r= -0.446, p=0.006) in subjects with normal DXA (N=51), while in those osteoporotic (N=19), with glycated hemoglobin A1c (r=0.596, p=0.025). Control group (N=363): Ca/Mg corelated with age (N=165, r=0.178, p=0.034), fasting glycemia (r=0.253, p=0.003), glycated hemoglobin A1c (r=0.22, p=0.045), osteocalcin (r= -0.196, p=0.03), and CrossLaps (r= -0.193, p=0.034) in osteopenic subjects (N=165) and with P1NP (r= -0.249, p=0.027) in normal DXA (N=148). Conclusion. Osteopenic subjects (regardless tumor diagnosis) showed a direct age-Ca/Mg association, while the ratio was correlated with the bone turnover biomarkers distinctively across DXA sub-groups. Additionally, the ratio might reflect a more severe glucose profile.
Multi-target phytotherapeutic and nutraceutical interventions have been proposed as complementary strategies in metabolic dysfunction-associated steatotic liver disease (MASLD), yet the interpretation of treatment-associated changes in interleukin-11 (IL-11) remains uncertain. Historically associated with tissue protection and regeneration, IL-11 has sometimes been regarded as a potentially favorable biomarker; however, recent evidence challenges this interpretation. This Perspective critically reappraises the biological role of IL-11 in metabolic liver disease. A narrative critical appraisal was integrated with evidence-informed mapping of a representative twelve-component phytotherapeutic formula onto processes implicated in hepatic IL-11 induction. Current evidence links hepatic IL-11 signaling through the interleukin-11 receptor alpha (IL-11Rα)/gp130 complex to lipotoxic and oxidative stress, hepatocyte injury, hepatic stellate-cell activation, and fibrogenesis, while cross-sectional human data associate circulating IL-11 with fibrosis severity in MASLD. Four competing, not necessarily mutually exclusive, hypotheses are proposed: upstream suppression (H1), adaptive rise (H2), extrahepatic/off-axis effects (H3), and null/analytical variability (H4), generating testable predictions regarding IL-11 trajectories and biomarker relationships. A treatment-associated increase in circulating IL-11 should therefore not be interpreted a priori as a favorable regenerative response without independent evidence of reduced hepatic injury or fibrogenic activity. A staged validation strategy is proposed to prospectively distinguish among these interpretations.
Brain tumors may present themselves initially under the appearance of depression, anxiety, psychosis, apathy, personality change, or cognitive decline, and psychiatric comorbidity remains frequent throughout the course of the neuro-oncological disease. In this Perspective, we argue that psychiatric expertise should be embedded in neuro-oncology care from the patient's first presentation through survivorship and palliation. We draw on the literature and on two cases from our liaison psychiatry service, in which older adults referred for depressive symptoms were found to have intracranial tumors and continued to need psychotropic treatment after successful surgery. We compare how DSM-5-TR and ICD-11 classify psychiatric syndromes secondary to brain disease and propose a four-domain etiological formulation that separates direct tumor effects, treatment-related and endocrine effects, psychological reactions, and pre-existing vulnerability. This article also presents the most relevant red flags that should prompt neuroimaging, the distinction between apathy and depression, the burden of depression, distress, suicide risk, and postoperative delirium, and the psychiatric effects of corticosteroids, antiseizure medications, dopamine agonists, and cranial irradiation. We also address practical psychopharmacology, electroconvulsive therapy, decision-making capacity, cognitive rehabilitation, and mood assessment in aphasia, while highlighting the scarcity of controlled evidence in this population. Finally, a phase-based, multidisciplinary model of care is proposed, and an outline of research priorities is presented.
Coronary CT angiography (CCTA) is used as a triage test before invasive coronary angiography (ICA). In this study we assessed the level of agreement between the two methods in patients whose CCTA reported severe stenosis in the presence of a low coronary artery calcium (CAC) score. We retrospectively analyzed 124 patients selected from a total of 2,742 examinations who underwent both CCTA and ICA. A discordant group (n=33) had stenosis reported on CCTA together with a low CAC (<400 and <100 AU). Across the whole cohort, obstructive disease (≥50% in the left main coronary artery or ≥70% in the other segments) was present in 44.4% of patients. In the discordant subgroup, agreement between CCTA and ICA was markedly worse: ICA showed obstructive disease in only 6.1% of patients. These results suggest that, although CAC remains a stand-alone predictor of obstructive disease, its predictive value is limited in a population already selected by referral for catheterization. The substantial discrepancy observed in cases where CCTA reports severe disease in the presence of a low CAC score raises the possibility that CCTA overestimates stenosis severity. In patients showing this pattern of discordance, non-invasive functional testing before referral to ICA could therefore be useful.
