Assessing Gender Differences for Non-Predictable Breakthrough Cancer Pain Phenomenon: A Secondary Analysis from IOPS-MS Study.

Purpose: Breakthrough cancer pain (BTcP) is a temporary exacerbation of pain that "breaks through" a phase of adequate pain control by an opioid-based therapy. The non-predictable BTcP (NP-BTcP) subtype occurs in the absence of any specific activity. Evidence showed that gender differences exist in pain response sensitivity and clinical pain risk. This analysis aimed to signify the gender differences for the NP-BTcP phenomenon. Patients and Methods: This is a secondary analysis of the Italian Oncologic Pain multiSetting-Multicentric Survey (IOPS-MS), the largest study on BTcP. The subset of NP-BTcP cases for non-gender-specific cancer was considered. Univariable and multivariate analyses were conducted to identify gender differences for the NP-BTcP profile about its intensity, number of episodes per day, and type. A metastatic status-stratified analysis was performed to compare gender with the main clinical variables among the population with NP-BTcP. Results: Males exhibited a higher occurrence of BTcP in the thorax region compared to females (15% vs 11%, respectively, p = 0.03). Males also had a higher onset of BTcP, a higher BTcP therapy dosage (33% vs 28%, p = 0.04, mean: 201 vs 186, p = 0.02) and a lower Karnofsky score (mean: 46.9 vs 49.2, p = 0.03) compared to females. Similar gender differences were found for metastatic patients in the BTcP site (14% vs 8.5%, respectively; p = 0.01), peak onset (33% vs 27%, p = 0.02), BTcP therapy dosage (199 vs 185, p=0.04), and Karnofsky score (mean 47.5 vs 50.4, p = 0.009). Phenotype 2 was more characterized by non-metastatic males (41% vs 23%, p = 0.020) while non-metastatic females presence was predominant among others. Conclusion: In this study, gender differences according to site, onset and dosage of BTcP were found. The phenotype characterization of BTcP needs to be further investigated for a possible useful function in the management of cancer-related pain in non-metastatic patients.

Role of Nutrition in the Management of Patients with Chronic Musculoskeletal Pain.

Chronic musculoskeletal pain (CMP), defined as persistent discomfort in musculoskeletal tissues persisting for over 3 months, afflicts an estimated 1.71 billion people globally, leading to significant functional impairments and psychological distress, thereby detrimentally affecting individuals' quality of life. The objective of this narrative review is to elucidate the complex relationship among dietary habits, sarcopenia, and gut microbiota composition, with an eye toward enhancing patient management and outcomes. Given the burgeoning interest in the influence of diet on CMP, a detailed examination of the current literature is warranted. Nutritional intake is a critical determinant of the gut microbiota profile, which, in turn, is linked to musculature integrity and performance, potentially leading to sarcopenia. The development of sarcopenia can aggravate CMP owing to diminished muscular strength and functionality. Additionally, disruptions in the gut microbiota may directly modulate nociception, intensifying CMP manifestations. Thus, nutritional optimization emerges as a viable approach to CMP management. Emphasizing a diet conducive to a healthy gut microbiome could forestall or mitigate sarcopenia, thereby attenuating CMP intensity. Nevertheless, the domain calls for further empirical exploration to unravel the nuances of these interactions and to forge efficacious dietary strategies for individuals with CMP. Beyond mere analgesia, comprehensive patient care for CMP requires acknowledgment of the complex and multifactorial nature of pain and its foundational elements. Embracing an integrative treatment model allows healthcare practitioners to promise better patient prognoses, enriched life quality, and a decrease in the sustained healthcare costs associated with CMP.

Chronic musculoskeletal pain (CMP) is long-lasting pain in the bones, muscles, and joints, and it's a common problem affecting over 1.7 billion people worldwide. This kind of pain can really disrupt someone's daily life, making it hard to do everyday things and causing a lot of stress. This review is like a deep dive into how eating habits, muscle loss, and the tiny organisms living in our guts all connect to this pain. As we are becoming more curious about how food affects CMP, it's important to look closely at what we already know. What we eat can change the tiny bugs in our guts, which has a big impact on our muscles. If our muscles get weak and shrink (a condition called sarcopenia), it can make CMP worse because weak muscles cannot support our bodies well. Also, if the balance of bugs in our gut is off, it might even make us feel pain more intensely. So, changing what we eat might be a good way to tackle CMP. Eating foods that keep our gut bugs happy might help prevent muscle loss and reduce pain. But we still need to do more research to understand this better and to figure out the best foods to eat for this purpose. Treating CMP is not just about getting rid of the pain; it's about looking at the whole picture and all the things that contribute to the pain. By considering everything ­ from our diet to our muscles to our gut bugs ­ doctors can help improve the lives of people with CMP, making them more comfortable and possibly reducing medical costs in the long run.

Association of opioid use with survival in patients with cancer treated with immune checkpoint inhibitors: it is time for evidence-based behaviors.

Cancer is a leading cause of morbidity and mortality worldwide, with pain experienced by most patients undergoing cancer treatment. Opioids are the recommended treatment for cancer pain management, but recent studies suggest a negative association between opioid use and survival rates among patients undergoing immunotherapy. However, conclusions cannot be drawn regarding causality from these observational data. Immunotherapy, which boosts the body's immune system to fight cancer cells, has emerged as a promising treatment option for all types of cancer. Immune checkpoint inhibitors (ICIs) can activate the anticancer function of exhausted T cells and have shown remarkable survival benefits in patients with multiple malignancies. However, a recent systematic review and meta-analysis suggested that the use of opioids during ICI treatment has an adverse effect on patient prognosis, while the use of NSAIDs is not significantly associated with the prognosis in patients treated with ICIs. These reviews have major limitations due to the retrospective nature of the studies and the multiple factors that can influence the phenomenon. Therefore, caution is required when interpreting results from retrospective data on drug interactions. The findings of this study are alarming and potentially harmful to patients with cancer suffering from pain or other symptoms requiring opioid drugs.

Cannabinoids for Cancer-related Pain Management: An Update on Therapeutic Applications and Future Perspectives.

Pain is a debilitating phenomenon that dramatically impairs the quality of life of patients. Many chronic conditions, including cancer, are associated with chronic pain. Despite pharmacological efforts that have been conducted, many patients suffering from cancer pain remain without treatment. To date, opioids are considered the preferred therapeutic choice for cancer-related pain management. Unfortunately, opioid treatment causes side effects and inefficiently relieves patients from pain, therefore alternative therapies have been considered, including Cannabis Sativa and cannabinoids. Accumulating evidence has highlighted that an increasing number of patients are choosing to use cannabis and cannabinoids for the management of their soothing and non-palliative cancer pain and other cancer-related symptoms. However, their clinical application must be supported by convincing and reproducible clinical trials. In this review, we provide an update on cannabinoid use for cancer pain management. Moreover, we tried to turn a light on the potential use of cannabis as a possible therapeutic option for cancer-related pain relief.

Medicinal Cannabis: Extended Stability of Cannabis Extracts Produced Using a New Ethanol-based Extraction Method.

Cannabis as a therapeutic agent is increasing in popularity all around the globe, particularly in Western countries, and its potential is now well assessed. On the other hand, each country has its own regulation for the preparation of cannabis macerated oils; in Italy, there are only a few preparation methods allowed. With this work, we aim to perform a stability study of cannabis oils produced with a novel method for the extraction of cannabinoids from cannabis inflorescence. Three different varieties of cannabis were used, with and without the adding of tocopherol acetate as an antioxidant. Cannabinoids were extracted using ethanol at room temperature; then, the solvent was evaporated under reduced pressure and the preparations reconstituted with olive oil. In this work, we assessed the stability of both cannabinoids and terpenes in these formulas over 8 months. Cannabinoid stability was assessed by monitoring the concentrations of THC and CBD, while terpene stability was assessed by monitoring ß-Caryophyllene and α-Humulene concentrations. Stability of the extracts was not influenced by the presence of tocopherol acetate, though refrigeration seems to be detrimental for a long storage of products, especially regarding THC concentrations. The improvements offered by this method reside in the flexibility in controlling the concentration of the extract and the ability to produce highly concentrated oils, alongside the possibility to produce standardized oils despite the variability of the starting plant material.

Fentanyl in cancer pain management: avoiding hasty judgments and discerning its potential benefits.

Cancer pain is an important challenge in treatment and requires a rapid onset of action for its control. In particular, breakthrough cancer pain (BTcP) should be adequately controlled with a stable dose of a short-acting oral opioid. Fentanyl is a synthetic, highly selective opioid with many advantageous chemical properties, including high lipophilicity and distinct pharmacokinetic properties. It is recommended for pain management in a variety of settings, including acute pain, chronic pain and BTcP. To date, its variously designed formulations allow non-invasive administration; amongst others, sublingual fentanyl has proven useful in the management of BTcP and in improving the quality of life of patients with cancer. This review provides an update on the management of BTcP with fentanyl, with consideration of safety, as it remains an important tool in the treatment of cancer pain.

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives.

Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.

Investigating Functional Impairment in Chronic Low Back Pain: A Qualitative Study from the Patients and Specialists' Perspectives.

Prompt and appropriate treatment of chronic low back pain (cLBP) is of the utmost importance for preventing relevant disability, high burden of disease, and increasing costs for the healthcare system. Recently, the concept of functional impairment has been associated with any type of chronic pain, and mounting attention has been paid to extending the aims of treatment beyond mere pain remission, including restoration of working capacity, everyday functioning, mobility, and quality of life. Nevertheless, a shared definition of functionality is still lacking. Notably, different specialists involved in the treatment of cLBP such as general practitioners, orthopedists, pain therapists, and physiatrists, and patients themselves have different opinions on what "functional impairment" actually means. On these premises, a qualitative interview study was performed to investigate how the concept of "functionality" is interpreted by different specialists involved in the management of cLBP, and by patients. Overall, all different specialists agreed that functionality should be assessed in clinical practice. However, in spite of several instruments available for evaluating functionality, no homogeneity of behavior is observable.

Utilizing an artificial intelligence framework (conditional generative adversarial network) to enhance telemedicine strategies for cancer pain management.

BACKGROUND: The utilization of artificial intelligence (AI) in healthcare has significant potential to revolutionize the delivery of medical services, particularly in the field of telemedicine. In this article, we investigate the capabilities of a specific deep learning model, a generative adversarial network (GAN), and explore its potential for enhancing the telemedicine approach to cancer pain management. MATERIALS AND METHODS: We implemented a structured dataset comprising demographic and clinical variables from 226 patients and 489 telemedicine visits for cancer pain management. The deep learning model, specifically a conditional GAN, was employed to generate synthetic samples that closely resemble real individuals in terms of their characteristics. Subsequently, four machine learning (ML) algorithms were used to assess the variables associated with a higher number of remote visits. RESULTS: The generated dataset exhibits a distribution comparable to the reference dataset for all considered variables, including age, number of visits, tumor type, performance status, characteristics of metastasis, opioid dosage, and type of pain. Among the algorithms tested, random forest demonstrated the highest performance in predicting a higher number of remote visits, achieving an accuracy of 0.8 on the test data. The simulations based on ML indicated that individuals who are younger than 45 years old, and those experiencing breakthrough cancer pain, may require an increased number of telemedicine-based clinical evaluations. CONCLUSION: As the advancement of healthcare processes relies on scientific evidence, AI techniques such as GANs can play a vital role in bridging knowledge gaps and accelerating the integration of telemedicine into clinical practice. Nonetheless, it is crucial to carefully address the limitations of these approaches.