Lung cancer remains a significant global health challenge. The high mortality rate is primarily caused by late diagnoses and the limitations of conventional therapies. Photodynamic therapy (PDT), which uses photosensitizing compounds, specific wavelengths of light, and oxygen to generate cytotoxic reactive oxygen species (ROS) that selectively destroy cancer cells, has emerged as a promising, minimally invasive alternative. Despite its advantages, traditional PDT has limitations. These include the limited penetration depth of light and the hypoxic nature of the tumor microenvironment. Nanotechnology has transformed PDT by enabling the precise delivery of photosensitizers, improving their stability, overcoming physiological barriers, and allowing for deeper tissue targeting. This review analyzes the molecular mechanisms of PDT, the evolution of photosensitizer and nanoparticle design, strategies to overcome PDT limitations, and the impact of the tumor microenvironment. Additionally, the potential of combining PDT with other cancer therapies, such as chemotherapy, immunotherapy, targeted therapy, radiotherapy, and gene therapy, is being investigated. While preclinical successes are remarkable, clinical implementation of nanoparticle-based PDT faces complex regulatory pathways, manufacturing scalability challenges, and the need for robust long-term safety data. Integrating artificial intelligence (AI) and biomarker discovery will accelerate the development of personalized treatments and usher in a new era of targeted oncology for lung cancer patients.

Despite the high global prevalence of depressive and anxiety disorders, access to early clinical assessment remains limited for many. Although this field has grown rapidly, existing reviews have focused primarily on technical performance, with limited systematic attention to whether current models meet the prerequisites for clinical implementation. This scoping review mapped methodological approaches across this domain and evaluated clinical readiness using five predefined indicators: sample size adequacy, external validation, prospective data collection, real-world evaluation, and model explainability. Searches of PubMed, Web of Science, and IEEE Xplore (March 2026) identified 2463 records; 34 studies (37 dataset evaluations) were included. The majority of studies (91%) were published from 2022 onwards. Depression was the primary target in 91% of studies, while only one study addressed anxiety. Hand-crafted acoustic features were the most frequent (57%), while classical machine learning was the most common model type (32%). External validation was conducted in only 32% of evaluations and real-world testing in 8%. Clinical readiness was classified as Low in 24%, Moderate in 65%, and High in 11% of evaluations. No evaluation met all five indicators simultaneously. These findings apply primarily to voice-based depression screening; 36 of 37 evaluations targeted depression, and the evidence base for anxiety disorders is limited to a single evaluation, precluding comparable characterisation for that condition. The principal challenges to clinical implementation are insufficient external validation, reliance on laboratory conditions, narrow linguistic coverage, and inconsistent metric reporting. The framework applied in this scoping review provides a replicable structure for assessing the clinical validity of AI-driven psychiatric screening tools.

Schizophrenia is one of the most disabling of the psychiatric diseases. The Brief Psychiatric Rating Scale Extended (BRSE) is used to evaluate the severity of psychiatric symptoms. Long-acting injectable (LAI) antipsychotics are commonly used and are preferred over oral antipsychotic medications. A two-center-based cross-sectional study was performed on 130 patients diagnosed with schizophrenia or schizoaffective disorder based on the International Classification of Diseases 10 criteria. We studied the relation between the development of cardiovascular risk factors and the antipsychotic medication that was administered in these patients. Our study demonstrates strong links between several cardiovascular risk factors and the duration of psychosis; the duration of the LAI antipsychotic treatment; the duration between the onset of the disease and the start of LAI antipsychotic treatment; and the use of specific LAI antipsychotic medications.

Nanotechnology is rapidly advancing as a promising approach in ovarian cancer management, addressing key challenges such as late diagnosis, drug resistance, and systemic toxicity of conventional therapies. Nanoparticles—engineered at the 1–100 nm scale—possess unique physical and biological properties that make them well-suited for targeted drug delivery, imaging, and biomarker detection. In diagnostics, platforms such as gold nanoparticles, quantum dots, superparamagnetic iron oxide nanoparticles (SPIONs), and carbon-based nanomaterials have demonstrated the ability to improve sensitivity and specificity, enabling the detection of low-abundance biomarkers and enhancing imaging contrast. These advances could significantly improve the early-stage detection, where survival outcomes are most favorable. Therapeutically, nanoparticles offer controlled and sustained drug release, targeted delivery to specific tumor sites, and the ability to co-deliver multiple agents, including siRNA and mRNA, in order to overcome resistance pathways. Clinically, liposomal formulations such as Doxil, already demonstrate reduced toxicity and improved drug bioavailability, while polymeric, silica, gold, and magnetic nanoparticles continue to show encouraging results in preclinical and early clinical studies. Although challenges remain—including large-scale production, long-term safety evaluation, and regulatory complexity—the current body of evidence highlights nanotechnology’s transformative potential in ovarian cancer care. By enabling earlier detection, more precise targeting, and reduced systemic toxicity, nanomedicine represents a critical step toward improving both survival and quality of life in affected patients.