Cannabis or its derivatives are widely used by patients with cancer to help with cancer symptoms and treatment side effects. However, cannabis has potent immunomodulatory properties. To determine if cannabis consumption during immunotherapy affects therapy outcomes, we conducted a prospective observatory study including 102 (68 immunotherapy and 34 immunotherapy plus cannabis) consecutive patients with advanced cancers who initiated immunotherapy. Cannabis consumption correlated with a significant decrease in time to tumor progression and overall survival. On the other hand, the use of cannabis reduced therapy-related immune-related adverse events. We also tested the possibility that cannabis may affect the immune system or the tumor microenvironment through the alteration of the endocannabinoid system. We analyzed a panel of serum endocannabinoids (eCBs) and eCB-like lipids, measuring their levels before and after immunotherapy in both groups. Levels of serum eCBs and eCB-like lipids, before immunotherapy, showed no significant differences between cannabis users to nonusers. Nevertheless, the levels of four eCB and eCB-like compounds were associated with patients’ overall survival time. Collectively, cannabis consumption has considerable immunomodulatory effects, and its use among cancer patients needs to be carefully considered due to its potential effects on the immune system, especially during treatment with immunotherapy.

The evaluation of an oil’s oxidation status during industrial production is highly important for monitoring the oil’s purity and nutritional value during production, transportation, storage, and cooking. The oil and food industry is seeking a real-time, non-destructive, rapid, robust, and low-cost sensor for nutritional oil’s material characterization. Towards this goal, a ¹H LF-NMR relaxation sensor application based on the chemical and structural profiling of non-oxidized and oxidized oils was developed. This study dealt with a relatively large-scale oil oxidation database, which included crude data of a ¹H LF-NMR relaxation curve, and its reconstruction into T₁ and T₂ spectral fingerprints, self-diffusion coefficient D, and conventional standard chemical test results. This study used a convolutional neural network (CNN) that was trained to classify T₂ relaxation curves into three ordinal classes representing three different oil oxidation levels (non-oxidized, partial oxidation, and high level of oxidation). Supervised learning was used on the T₂ signals paired with the ground-truth labels of oxidation values as per conventional chemical lab oxidation tests. The test data results (not used for training) show a high classification accuracy (95%). The proposed AI method integrates a large training set, an LF-NMR sensor, and a machine learning program that meets the requirements of the oil and food industry and can be further developed for other applications.

The food quality of edible oils is dependent on basic chemical and structural changes that can occur by oxidation during preparation and storage. A rapid and efficient analytical method of the different steps of oil oxidation is described using a time-domain nuclear magnetic resonance (TD-NMR) sensor for measuring signals related to the chemical and physical properties of the oil. The degree of thermal oxidation of edible oils at 80 °C was measured by the conventional methodologies of peroxide and aldehyde analysis. Intact non-modified samples of the same oils were more rapidly analyzed for oxidation using a TD-NMR sensor for 2D T₁-T₂ and self-diffusion (D) measurements. A good linear correlation between the D values and the conventional chemical analysis was achieved, with the highest correlation of R² = 0.8536 for the D vs. the aldehyde concentrations during the thermal oxidation of poly-unsaturated linseed oils, the oil most susceptible to oxidation. A good correlation between the D and aldehyde levels was also achieved for all the other oils. The possibility to simplify and minimize the time of oxidative analysis using the TD NMR sensors D values is discussed as an indicator of the oil’s oxidation quality, as a rapid and accurate methodology for the oil industry.

Cancer-related pain constitutes a dominant reason for admission to emergency services, and a significant patient and healthcare challenge. Evidence points to the rising prevalence of opioid misuse in this patient group. We sought to compare drug delivery in an oncology-dedicated emergency department (OED) and a general emergency department (GED) within the same hospital. As such, we obtained patient and drug-related data for OED and GED during a designated three-month period, and compared them using Fisher’s exact test, chi-square tests and the Mann-Whitney test. In total, 584 patients had 922 visits to emergency services (OED n = 479; GED n = 443), and were given 1478 drugs (OED n = 557; GED n = 921). Pain was a prominent chief complaint among visitors to the OED (17%) and GED (21%). Approximately a fifth of all drugs used were analgesics (OED—18.5%; GED—20.4%), however, in the GED, 51.6% (n = 97) were used for non-pain-related admissions, compared with 33.0% (n = 34) in OED. Opioid usage significantly differed between emergency settings. The GED administered three times as many intravenous opioids (p <0.001), a narrower spectrum of oral and intravenous drugs (p = 0.003) and no rapid-acting opioids, significantly fewer pain adjuvants (10.9% versus 18.7%, p < 0.001), and, finally, non-guideline-recommended drugs for pain, such as meperidine and benzodiazepines. Taken together, compared with the GED, the management of cancer-related pain in the OED was more personalized, and characterized by fewer intravenous opioids, enhanced diversity in drug type, route and method of delivery. Efforts should be directed toward reduction of disparities in the treatment of cancer pain in emergency settings.