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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.