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Some patients with schizophrenia fail to respond to standard antipsychotics and are considered treatment-resistant. In these cases, clozapine is the only antipsychotic with proven efficacy, but its use is complicated by severe adverse effects, complex monitoring requirements, and non-response. Variation within the CYP450 enzymes CYP1A2, CYP2D6, CYP3A4, and CYP2C19 has been linked to the differential metabolism of antipsychotics. Testing for CYP450 single nucleotide polymorphisms may be a useful predictor of treatment resistance and could inform pharmacogenetic recommendations to identify potential treatment non-responders. Nonetheless, it remains uncertain whether differential antipsychotic metabolism is directly related to treatment efficacy. This comprehensive narrative review endeavours to delve into the molecular and genetic basis of schizophrenia, and discuss the current treatments available. In particular, we aim to examine the aetiology of treatment resistance in schizophrenia through available literature and discuss current challenges within the field.
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The technological capability of artificial intelligence (AI) continues to advance with great strength. Recently, the release of large language models has taken the world by storm with concurrent excitement and concern. As a consequence of their impressive ability and versatility, their provide a potential opportunity for implementation in oncology. Areas of possible application include supporting clinical decision making, education, and contributing to cancer research. Despite the promises that these novel systems can offer, several limitations and barriers challenge their implementation. It is imperative that concerns, such as accountability, data inaccuracy, and data protection, are addressed prior to their integration in oncology. As the progression of artificial intelligence systems continues, new ethical and practical dilemmas will also be approached; thus, the evaluation of these limitations and concerns will be dynamic in nature. This review offers a comprehensive overview of the potential application of large language models in oncology, as well as concerns surrounding their implementation in cancer care.
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Inteligencia Artificial , Oncología Médica , Humanos , Oncología Médica/métodos , NeoplasiasRESUMEN
The delivery of comprehensive cancer care within a progressively intricate healthcare environment requires oncology providers to become well-versed in the integration of palliative care (PC). Moreover, as healthcare professionals are urged to prioritize the individual preferences of patients and their families who confront life-limiting illnesses, it has become evident that oncology patients and their families have identified their psychosocial care needs as multifaceted and distinct, calling for specialized attention from care providers. Nevertheless, this is a skill that can be acquired through learning and practice. The landscape of PC is rapidly changing, with paradigm shifting studies highlighting the importance of early concurrent palliative and oncology inpatient and outpatient care for those with new advanced cancer diagnosis. Early concurrent care can notably improve quality of life (QoL), symptom control, patient and caregiver satisfaction, reduce costs and even improve survival. There is no longer a question of if PC should be offered, but instead when referral should be completed, what is the optimal model for service delivery and what barriers are present to achieve concurrent care. Conceptual models have been identified for optimal integrated palliative and oncology care delivery. In order to provide the best integrated care however, multiple obstacles need to be overcome. This narrative review discusses the importance of early integrated oncology and PC for patients with advanced cancer diagnosis, as well as the barriers to the integration of these specialties and potential models for delivery.
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Neoplasias , Cuidados Paliativos , Humanos , Cuidados Paliativos/psicología , Calidad de Vida , Oncología Médica , Pacientes InternosRESUMEN
Gestational renal cell carcinoma (RCC) is an uncommon occurrence and presents a diagnostic and clinical challenge for healthcare providers. The manifestation of gestational RCC often lacks overt symptoms and can mimic physiological changes and disorders associated with pregnancy. Frequently, patients are asymptomatic, and the condition is detected during routine antenatal ultrasonography. However, the options for imaging modalities and treatment are limited due to the potential risks of harm to the developing fetus and interruption of pregnancy. Throughout the management of pregnant patients with RCC, both maternal and neonatal risks must be carefully considered, while respecting the patient's autonomy. Currently, there are no internationally or nationally recognized evidence-based guidelines for managing gestational RCC, highlighting the need for a multidisciplinary approach to treatment. Advances in surgical techniques have resulted in a shift from open surgeries to laparoscopic radical or partial nephrectomy procedures, with robotic-assisted approaches also gaining popularity. In cases of metastatic gestational RCC, termination of the pregnancy may be considered, and the appropriate treatment of RCC should be the priority. This article aims to provide a comprehensive review of the epidemiology, aetiology, clinical presentation, diagnosis, prognosis, and management of gestational RCC.