Search Results (984)

Chemsex is a phenomenon involving the intentional use of psychoactive substances before or during sexual activity, especially among men who have sex with men (MSM). It is associated with various health risks, including substance dependence, risky sexual behaviors, and both mental and somatic disorders. Despite its growing prevalence and public health relevance, chemsex lacks a clear definition and is not recognized as a distinct diagnostic entity. This narrative review synthesizes clinical, epidemiological, and technological evidence on chemsex; argues for its classification as a form of mixed addiction; and preliminarily proposes diagnostic criteria for a potential entity in the International Classification of Diseases, 12th Revision (ICD-12). This paper highlights key psychotropic substances used in chemsex, patterns of use, and their neurobiological, psychological, and behavioral consequences. It explores the relationship between chemsex and compulsive sexual behavior disorder (CSBD), current diagnostic frameworks (ICD-10 and ICD-11), and challenges in clinical practice. Therapeutic strategies discussed include cognitive behavioral therapy (CBT), digital interventions, and emerging applications of artificial intelligence (AI) in prevention and treatment. Attention is also given to epidemiological trends, sociocultural influences, and barriers to seeking help. This review concludes by identifying research gaps and advocating for a more integrated, multidimensional approach to classifying and treating chemsex-related syndromes. Full article

The integration of nanotechnology into drug repurposing strategies is redefining the development landscape for diagnostic, therapeutic, and theranostic agents. In radiopharmacy, nanoplatforms are increasingly being explored to enhance or extend the use of existing radiopharmaceuticals, complementing earlier applications in other biomedical fields. Many of these nanoplatforms evolve into multifunctional systems by incorporating additional imaging modalities (e.g., MRI, fluorescence) or non-radioactive therapies (e.g., photodynamic therapy, chemotherapy). These hybrid constructs often emerge from the reformulation, repositioning, or revival of previously approved or abandoned compounds, generating entities with novel pharmacological, pharmacokinetic, and biodistribution profiles. However, their translational potential faces significant regulatory hurdles. Existing frameworks—typically designed for single-modality drugs or devices—struggle to accommodate the combined complexity of nanoengineering, radioactive components, and integrated functionalities. This review examines how these systems challenge current norms in classification, safety assessment, preclinical modeling, and regulatory coordination. It also addresses emerging concerns around digital adjuncts such as AI-assisted dosimetry and software-based therapy planning. Finally, the article outlines international initiatives aimed at closing regulatory gaps and provides future directions for building harmonized, risk-adapted frameworks that support innovation while ensuring safety and efficacy. Full article

Lean metabolic dysfunction-associated steatotic liver disease (lean MASLD) challenges longstanding views that link hepatic steatosis primarily to obesity. Emerging as a distinct and under-recognized clinical entity, lean MASLD affects individuals with a normal body mass index (BMI), yet carries risks of cardiovascular disease, hepatocellular carcinoma, and liver-related mortality comparable to obesity-associated MASLD. The absence of overt metabolic dysfunction complicates diagnosis, revealing critical limitations in current screening frameworks centered on BMI. This review synthesizes evolving clinical insights and epidemiological trends in lean MASLD, and delineates its unique pathophysiological mechanisms. Recent advances in metabolomics have uncovered disease-specific disruptions in lipid and amino acid metabolism, bile acid signaling, and gut microbiota-derived metabolites. By integrating evidence from metabolic, genetic, and epigenetic domains, we identified promising biomarkers, and therapeutic targets that may support earlier detection and precision-guided treatment strategies. Full article

The study undertook a comprehensive review and comparative analysis of natural language processing techniques for news article classification, with a particular focus on Java language libraries. The dataset comprised an excess of 200,000 items of news metadata sourced from The Huffington Post. The traditional algorithms based on mathematical statistics and deep machine learning were evaluated. The libraries chosen for tests were Apache OpenNLP, Stanford CoreNLP, Waikato Weka, and the Huggingface ecosystem with the Pytorch backend. The efficacy of the trained models in forecasting specific topics was evaluated, and diverse methodologies for the feature extraction and analysis of word-vector representations were explored. The study considered aspects such as hardware resource management, implementation simplicity, learning time, and the quality of the resulting model in terms of detection, and it examined a range of techniques for attribute selection, feature filtering, vector representation, and the handling of imbalanced datasets. Advanced techniques for word selection and named entity recognition were employed. The study compared different models and configurations in terms of their performance and the resources they consumed. Furthermore, it addressed the difficulties encountered when processing lengthy texts with transformer neural networks, and it presented potential solutions such as sequence truncation and segment analysis. The elevated computational cost inherent to Java-based languages may present challenges in machine learning tasks. OpenNLP model achieved 84% accuracy, Weka and CoreNLP attained 86% and 88%, respectively, and DistilBERT emerged as the top performer, with an accuracy rate of 92%. Deep learning models demonstrated superior performance, training time, and ease of implementation compared to conventional statistical algorithms. Full article

The absent decline in cancer mortality rates is primarily due to moderate therapeutic efficacy and intrinsic or acquired tumor cell resistance toward treatments. Combining different oncology treatments increases therapy success and decreases the chance of refractory tumor cells. Therefore, combination cancer treatments are the principal paradigm of 21st-century oncology. Physical modalities such as radiotherapy have a long-standing tradition in such combination treatments. In the last decade, another physical principle emerged as a promising anticancer agent: cold gas plasma. This partially ionized gas, operated at about body temperature, emits multiple bioactive components, including reactive oxygen and nitrogen species (ROS/RNS). This technology’s multi-ROS/RNS nature cannot be phenocopied by other means, and it capitalizes on the vulnerability of tumor cells within metabolic and redox signaling pathways. Many cancer models exposed to mono or combination gas plasma treatments have shown favorable results, and first cancer patients have benefited from cold gas plasma therapy. The main findings and proposed mechanisms of action are summarized. Considering the specific application modes, this review identifies promising gas plasma combination therapies within guideline-directed treatment schemes for several tumor entities. In conclusion, gas plasmas may become a potential (neo)adjuvant therapy to existing treatment modalities to help improve the efficacy of oncological treatments. Full article

Objectives: Atrial fibrillation (AF) is associated with high risk of ischaemic stroke (IS). Oral anticoagulant therapy (OAT) is the standard of care for stroke prevention, even though its management remains challenging in clinical practice. An emerging problem is embolic events occurring on adequately conducted OAT, the so-called resistant stroke (RS). We aimed to describe pre-stroke prevention therapy, management on hospital discharge, and therapy at follow-up in all patients with AF hospitalized for IS and in the RS subgroup. Methods: We conducted a retrospective monocentric study of patients with known AF hospitalized for an IS. A subgroup with RS was identified. We recorded information on prevention therapy at home, recommended therapy at discharge, and data on outcome and prevention therapy at follow-up. Results: We identified 226 patients, 61% females, median age 84.04 years. Preventive therapy at home was performed in 121 (53.5%) (119 OAT and 2 Left Atrial Appendage Occlusion). At hospital discharge OAT was prescribed to 78.2% of patients. RS was diagnosed in 33 patients whose management at discharge was: same OAT in 12, shift to another Direct Oral Anticoauglant (DOAC) in 5, from DOAC to Vitamin K Antagonist (VKA) and vice versa in 11, non-specified OAT in 4. At final, follow-up of 208 days (range 85–443) 23.3% (34/146) did not assume OAT. OAT was significantly associated with survival probability (p < 0.001). Conclusions: Our findings confirm a scarce adoption of guidelines for AF-related embolic events, even in the absence of absolute contraindication to OAT. RS remains an underexplored clinical entity with empirical management, highlighting the need for targeted research and tailored therapeutic strategies. Full article

Efficient emergency load transfer is crucial for ensuring the power system’s safe operation and reliable power supply. However, traditional load transfer methods that rely on human experience have limitations, such as slow response times and low efficiency, which make it difficult to address complex and diverse fault scenarios effectively. Therefore, this paper proposes an emergency load transfer method based on knowledge graphs to achieve intelligent management and efficient retrieval of emergency knowledge. Firstly, a named entity recognition model based on ERNIE-BiGRU-CRF is constructed to automatically extract key entities and relationships from the load transfer plan texts, obtaining information such as fault names, fault causes, and operation steps. Secondly, a power system emergency load transfer knowledge graph is constructed based on the extracted structured knowledge, which is efficiently stored using a graph database and enables the visualization and interactive query of knowledge. Finally, real power system fault cases prove that the proposed method can effectively improve the retrieval efficiency of fault knowledge and provide intelligent support for online emergency load transfer decisions. Full article

Background: Young-onset colorectal cancer (YO-CRC) has emerged as a distinct clinical entity, often presenting at advanced stages. Despite the increasing incidence, the molecular and clinical underpinnings of YO-CRC remain underexplored. This study aims to characterize the clinical and molecular features of YO-CRC and to evaluate their impact on OS. Methods: We reviewed 110 patients diagnosed with YO-CRC at our institution who underwent next-generation sequencing. Demographic, clinical, and molecular data, including age, gender, race, tumor location, cancer stage, and mutation status (KRAS, NRAS, BRAF, POLE, ERBB-2/HER2, microsatellite status), were collected by reviewing electronic medical records. For OS analysis, we focused on patients diagnosed with de novo stage IV. Cox proportional hazards regression and Kaplan–Meier survival analysis were utilized to assess the association of these factors with OS, with statistical significance determined by a p-value threshold of <0.05. Results: Among 110 patients, n = 44 (40%) presented with local disease (stage 1–3), while n = 66 (60%) presented with de novo metastatic disease at the time of diagnosis. The median age at diagnosis was 44.5 years. The cohort consisted of 64% males and 36% females, with 84% of patients identified as White. Most tumors were left-sided (77%), including the distal colon/sigmoid (44%) and rectum (33%). KRAS and BRAF mutations were present in 36% and 5.5%, respectively. ERBB-2/HER2 amplification and microsatellite instability were observed in 4.5% and 6.4%, respectively. Tumor mutation burden (TMB) was <10 in 57% of patients, with 14% having TMB > 20. CNV analysis revealed that 14% of patients had copy gains, 12% had concurrent gains/losses, and 31% had copy losses. Among 66 patients with de novo metastatic disease, 44% had died by the time of analysis, with a median overall survival (OS) of 43.6 months (95% CI, 28.7—not reached). KRAS mutations were found to be significantly associated with worse survival outcomes. Cox regression analysis reveals the prognostic significance of KRAS status, with a hazard ratio (HR) of 3.52 (95% CI: 1.59–7.76, p = 0.002), indicating a significantly higher risk of death for KRAS-mutant YO-CRC patients. Conclusions: Patients with YO-CRC are more likely to present with de novo metastatic disease and left-sided tumors with distinct molecular characteristics. KRAS mutations are a key prognostic factor in YO-CRC, highlighting the need for therapeutic interventions to improve outcomes in this high-risk group. Full article

Digital Twin (DTw) technology is a cornerstone of Industry 4.0, enabling real-time monitoring, predictive maintenance, and performance optimization across diverse industries. A key requirement for effective DTw implementation is high geometric fidelity—ensuring the digital model accurately represents the physical counterpart. Fidelity metrics provide a quantitative means to assess this alignment in terms of geometry, behavior, and performance. Among these, 3D shape descriptors play a central role in evaluating geometric fidelity, offering computational tools to measure shape similarity between physical and digital entities. This paper presents a comprehensive review of 3D shape descriptor methods and their applicability to geometric fidelity assessment in DTw systems. We introduce a structured taxonomy encompassing classical, structural, texture-based, and deep learning-based descriptors, and evaluate each in terms of transformation invariance, robustness to noise, computational efficiency, and suitability for various DTw applications. Building upon this analysis, we propose a conceptual fidelity metric that maps descriptor properties to the specific fidelity requirements of different application domains. This metric serves as a foundational framework for shape-based fidelity evaluation and supports the selection of appropriate descriptors based on system needs. Importantly, this work aligns with and contributes to the emerging ISO 30138 standardization initiative by offering a descriptor-driven approach to fidelity assessment. Through this integration of taxonomy, metric design, and standardization insight, this paper provides a roadmap for more consistent, scalable, and interoperable fidelity measurement in digital twin environments—particularly those demanding high precision and reliability. Full article

We present a novel derivation of the spacetime metric generated by matter, without invoking Einstein’s field equations. For static sources, the metric arises from a relativistic formulation of D’Alembert’s principle, where the inertial force is treated as a real dynamical entity that exactly compensates gravity. This leads to a conformastatic metric whose geodesic equation—parametrized by proper time—reproduces the relativistic version of Newton’s second law for free fall. To extend the description to moving matter—uniformly or otherwise—we apply a Lorentz transformation to the static metric. The resulting non-static metric accounts for the motion of the sources and, remarkably, matches the weak-field limit of general relativity as obtained from the linearized Einstein equations in the de Donder (or Lorenz) gauge. This approach—at least at Solar System scales, where gravitational fields are weak—is grounded in a new dynamical interpretation of the Equivalence Principle. It demonstrates how gravity can emerge from the relativistic structure of inertia, without postulating or solving Einstein’s equations. Full article

With the advent of AI- and robot-systems, the current Human–Computer Interaction (HCI) paradigm, which treats interaction as a transactional exchange, is increasingly insufficient for complex socio-technical systems. This paper argues for a shift toward an agential realist perspective, which understands interaction not as an exchange between separate entities, but as a phenomenon continuously enacted through dynamic, material-discursive practices known as ‘intra-actions’. Through a diffractive reading of agential realism, HCI, complex systems theory, and an empirical case study of a touring exhibition on skateboarding culture, this paper explores an alternative approach. A key finding emerged from a sound-recording workshop when a participant described the recordings not as “how it sounds,” but as “how it feels” to skate. The finding reveals the limits of traditional HCI and it illustrates how interacting parts are co-constituted through the intra-actions of entangled agencies. An argument is made that design for interactive complex systems should change from focusing on causal transactional interaction towards organizing relational complexity, which is staging the conditions for a rich scope of emergent encounters to unfold. The paper concludes by suggesting further research into non-causal explanation and computation. Full article

Pelvic inflammatory disease (PID) encompasses a broad range of infection-induced inflammatory disorders of the female upper genital tract, commonly caused by ascending sexually transmitted infections. Diagnosis is often challenging because of nonspecific or absent symptoms and the overlap with other pelvic pathologies. While clinical and laboratory assessments are essential, cross-sectional imaging plays a pivotal role, especially in complicated, atypical, or equivocal cases. This review focuses on the typical and atypical imaging features of PID and highlights the crucial roles of computed tomography (CT) and magnetic resonance imaging (MRI) in its diagnostic evaluation. CT is frequently employed in emergency settings because of its widespread availability and ability to detect acute complications such as tubo-ovarian abscesses (TOA), peritonitis, or Fitz-Hugh–Curtis syndrome. However, it is limited by ionizing radiation and suboptimal soft-tissue contrast. MRI provides superior tissue characterization and multiplanar imaging without radiation exposure. When combined with diffusion-weighted imaging (DWI), MRI achieves high diagnostic accuracy, particularly in differentiating PID from other entities such as endometriosis, adnexal tumors, and gastrointestinal or urinary tract diseases. This review also addresses PID in specific clinical contexts, including post-partum infection, post-assisted reproductive technologies (ART), intrauterine device (IUD) use, and chronic or recurrent forms. A comprehensive, multimodal imaging approach integrated with clinical findings is essential for timely diagnosis, effective treatment, and prevention of severe reproductive sequelae. Full article

The promotion of local renewable energy consumption and stable power gird (the latter is referred to as PG) operation have emerged as the primary objectives of power system reform. The integration of multiple microgrids with distinct characteristics through the utilization of shared energy storage (the following is referred to as SES) facilitates coordinated operation. This approach enables the balancing of energy across temporal and spatial domains, contributing to the overall reliability and security of the energy network. The proposed model outlines a methodology for the coordinated operation of multiple microgrids and SES, with a focus on asymmetric price negotiation. Initially, cost and revenue models for microgrids and SES power plants are established. Secondly, an asymmetric pricing method based on the magnitude of each entity’s energy contribution is proposed. A profit optimization model is also established. The model can be decomposed into two distinct subproblems: the maximization of overall profit and the negotiation of transaction prices. The model can be solved by employing the alternating direction method of multipliers (ADMM). Finally, a series of case studies were conducted for the purpose of validating the operation optimization model that was previously constructed. These studies demonstrate that the model enhances collective operational efficiency by 44.69%, with each entity’s efficiency increasing by at least 12%. At the same time, cooperative benefits are distributed fairly according to each entity’s energy contribution. Full article

Substance-induced psychosis is a recognized clinical entity, commonly linked to cannabinoids, stimulants, hallucinogens, alcohol, and polysubstance use. These agents may provoke transient or persistent psychotic symptoms during intoxication or withdrawal. Opioids, however, constitute a noteworthy exception: psychosis is rarely observed during opioid intoxication, and emerging data suggest that opioid agonists might even exert antipsychotic-like effects. This article examines the paradoxical interaction between opioids and psychosis, with attention to clinical reports of psychotic symptoms arising following abrupt discontinuation of methadone or buprenorphine. In numerous cases, symptoms resolved swiftly after reintroduction of the opioid agonist, implying a neuromodulatory role. Opioids, unlike other substances of abuse, seem to lack intrinsic psychotogenic effects and may influence dopaminergic activity via kappa-opioid receptor antagonism and endorphinergic mechanisms. This challenges standard models of substance-induced psychosis and calls for a refined understanding of opioid pharmacodynamics in psychiatric contexts. In psychotic presentations among polysubstance users who also use opioids, restoring opioid agonist therapy should be prioritized, with antipsychotics reserved as second-line options—preferably agents with favorable receptor profiles. Where opioids are not involved, antipsychotics remain first-line, but should be applied judiciously, with efforts to taper when clinically appropriate. Full article

Motor Neuron Diseases (MNDs) such as Amyotrophic Lateral Sclerosis (ALS), Primary Lateral Sclerosis (PLS), Hereditary Spastic Paraplegia (HSP), Spinal Muscular Atrophy with Respiratory Distress Type 1 (SMARD1), Multisystem Proteinopathy (MSP), Spinal and Bulbar Muscular Atrophy (SBMA), and ALS associated to Frontotemporal Dementia (ALS-FTD), have traditionally been studied as distinct entities, each one with unique genetic and clinical characteristics. However, emerging research reveals that these seemingly disparate conditions converge on shared molecular mechanisms that drive progressive neuroaxonal degeneration. This narrative review addresses a critical gap in the field by synthesizing the most recent findings into a comprehensive, cross-disease mechanisms framework. By integrating insights into RNA dysregulation, protein misfolding, mitochondrial dysfunction, DNA damage, kinase signaling, axonal transport failure, and immune activation, we highlight how these converging pathways create a common pathogenic landscape across MNDs. Importantly, this perspective not only reframes MNDs as interconnected neurodegenerative models but also identifies shared therapeutic targets and emerging strategies, including antisense oligonucleotides, autophagy modulators, kinase inhibitors, and immunotherapies that transcend individual disease boundaries. The diagnostic and prognostic potential of Neurofilament Light Chain (NfL) biomarkers is also emphasized. By shifting focus from gene-specific to mechanism-based approaches, this paper offers a much-needed roadmap for advancing both research and clinical management in MNDs, paving the way for cross-disease therapeutic innovations. Full article