Brain tumors, despite the high mortality and morbidity, they are a rare type of heterogenous tumors that are highly dependent on sex, age, race, level of education, and socioeconomic status. Due to their high mortality rates, it is important to identify as many potential biomarkers for early detection as the earlier the tumor is discovered, the better the prognosis. One such early biomarker we propose in the current paper is the assessment of anxiety, depression, and cognitive changes. In most cancer patients, a certain degree of anxiety and depression is expected upon receiving the diagnosis as it triggers fears regarding the prognosis, possible side effects of the treatment, and even the possibility of the treatment failing. In this paper we analyzed the way anxiety, depression, and cognitive changes present themselves in the case of several types of tumors and whether these could be used as early markers. We have observed that most of the cognitive changes present are due to the location, size, and type of the tumor with some highly connected to anxiety and depression. Moreover, in the case of certain tumors, the removal of the mass has not improved the mood or cognitive function.

The increase in the frequency of psychiatric disorders is increasingly observed in patients diagnosed with cancer. Brain tumors are often associated with psychiatric symptoms such as mood symptoms, psychosis, memory problems, personality changes, anxiety, or depression. This study investigates what types of manifestations are present in patients with brain tumors, highlighting the connection with the location of the tumor. We conducted a clinical study on a sample of 91 patients diagnosed with primary brain tumors, hospitalized at the "Prof. Dr. Nicolae Oblu Emergency Clinical Hospital," Iași, Romania, between 2011 and 2023. Demographic data, type, and location of tumors were collected from the patients' medical records. Patients underwent detailed neuropsychological evaluations to identify psychiatric manifestations. Statistical analyses were performed to determine the prevalence of these manifestations and the correlations between tumor location and the severity of psychiatric symptoms. Our results show a significant prevalence of psychiatric disorders among patients with brain tumors, with notable differences between genders and types of manifestations. Organic personality disorders and mixed anxiety and depression disorders were among the most common. Additionally, a correlation was observed between the location of tumors in the frontal lobe and the severity of psychiatric symptoms. Conclusions: The research highlights the increased frequency of psychiatric symptoms in patients with brain tumors, especially when the tumor is located in the frontal lobe. These observations emphasize the relevance of a multidisciplinary approach in the management and treatment of these patients, including both cancer therapy and psychiatric support services.

Background/Objectives: Schizophrenia is a severe mental disorder frequently associated with cognitive impairments that negatively impact patients’ functional outcomes and quality of life. Standard pharmacological treatments primarily address positive symptoms, while cognitive deficits remain insufficiently targeted. This study aimed to assess the effectiveness of computer-assisted cognitive stimulation therapy (CCST) in improving cognitive performance, psychiatric symptoms, and quality of life in patients with schizophrenia. Methods: We conducted a prospective, comparative observational study involving two groups of clinically stable patients diagnosed with schizophrenia. The studied group (n=5) underwent a 6-week individualized CCST program using RehaCom software (12 sessions, twice weekly), while the control group (n=5) received no cognitive training and had no psychiatric conditions. Assessments were conducted at baseline and post-intervention using the Mini-Mental State Examination (MMSE), Brief Psychiatric Rating Scale (BPRS), Quality of Life Inventory (QOLI), and RehaCom cognitive screening across nine domains. Results: Participants in the studied group exhibited noticeable improvements in multiple cognitive domains (particularly in alertness, distributed attention, and working memory) as well as enhanced scores on psychiatric and quality of life assessments. Conclusions: Computer-assisted cognitive stimulation may serve as a promising complementary intervention to enhance cognitive function and psychosocial well-being in patients with schizophrenia. The individualized nature of the training supports its applicability in clinical settings. Further randomized studies with larger samples and extended follow-up periods are encouraged.

Mild Traumatic Brain Injury (mTBI) is a prevalent neurological condition that can lead to persistent cognitive impairments, disrupting memory, attention, and executive function. In this study, we explore the mechanisms of cognitive decline in mTBI through the lens of Markov blankets—a theoretical framework that delineates the statistical boundary between the brain’s internal and external states. By simulating the impact of mTBI on sensory, active, internal, and external states, we demonstrate how disruptions to the Markov blanket structure contribute to impairments in predictive coding and cognitive processing. Our simulation introduces noise and connectivity reductions that mimic the neurometabolic and synaptic changes following mTBI, revealing delayed sensory processing, impaired motor function, and cognitive instability. These findings highlight the importance of Markov blankets in maintaining cognitive integrity and offer novel insights into the pathophysiology of post-concussion syndrome. Understanding how mTBI disrupts the brain's functional architecture through Markov blanket disturbances may inform more effective diagnostic and therapeutic approaches.

Dental radiography is an essential component of modern dental practice, and its use has increased with the wider availability of panoramic imaging and cone-beam computed tomography (CBCT). Dental radiographic equipment is commonly described by manufacturers as emitting relatively low radiation doses, which has contributed to a widespread perception of safety in routine dental practice. Although occupational radiation exposure in dentistry is generally considered low, most published studies and radiation protection guidelines are based on dental clinics located in conventional healthcare buildings with appropriate structural shielding. In contrast, many industrial organizations, such as those in the petroleum, energy, and mining sectors, operate permanent medical and dental clinics within facilities constructed using lightweight or artificial wall materials, where radiation protection measures may be limited or inconsistently applied. This narrative review examines occupational radiation exposure in dental clinics operating within industrial facilities characterized by non-standard architectural designs, with particular attention to potential effects on thyroid health. The thyroid gland is highly sensitive to ionizing radiation, and long-term low-dose exposure, especially in confined spaces with inadequate shielding, may represent an underestimated occupational risk. Available evidence on occupational radiation doses in dental practice, scatter radiation associated with CBCT, biological mechanisms of thyroid radiosensitivity, and current radiation protection recommendations is reviewed and discussed. By addressing an under-recognized occupational setting, this review highlights important gaps in radiation safety related to architectural design, regulatory oversight, and dose monitoring in industrial dental clinics. The findings support the need for improved assessment of structural shielding, routine use of personal dosimetry, and targeted radiation safety training to better protect dental healthcare workers and other personnel working in proximity to dental imaging areas in these environments.

Neurocysticercosis (NCC), caused by the larval stage of Taenia solium, remains the most common parasitic infection of the central nervous system and a leading cause of acquired epilepsy worldwide. Despite substantial advances in neuroimaging, diagnosis and prognostic assessment remain challenging because of the heterogeneous clinical presentation and variable radiological features of the disease. Emerging developments in artificial intelligence (AI), radiomics, connectomics, and predictive computational neurology offer novel opportunities to improve diagnostic accuracy and individualised patient management. A structured literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar to identify peer-reviewed studies published between 2000 and 2025. The retrieved evidence was synthesised in a comprehensive narrative review focusing on the pathophysiology, neuroanatomical manifestations, neuroimaging characteristics and emerging computational applications in neurocysticercosis. Current evidence confirms the central role of computed tomography and magnetic resonance imaging in the diagnosis and staging of neurocysticercosis. Recent advances in radiomics, connectomics, machine learning, and deep learning demonstrate promising potential for improving lesion characterisation, differential diagnosis, quantitative imaging analysis, and prediction of neurological outcomes. Although disease-specific AI applications remain limited, computational methodologies successfully applied in neuroradiology, epilepsy, and neuro-oncology may be adapted to neurocysticercosis, supporting the development of predictive computational neurology and precision medicine. Neurocysticercosis represents a promising model for the future integration of neuroimaging, computational neuroscience, and artificial intelligence. However, current evidence remains limited by the scarcity of disease-specific datasets and the lack of large-scale clinical validation. Future multicenter studies integrating radiomics, connectomics, explainable AI, and standardised neuroimaging protocols will be essential for translating computational advances into clinically applicable diagnostic and prognostic tools capable of improving individualised neurological care.