Hot and Cold Drugs: National Park Service Medication Stability at the Extremes of Temperature.

STUDY OBJECTIVE: National Park Service (NPS) Parkmedics provide medical care in austere environments. The objective of this study was to evaluate the stability of specific medications used by Parkmedics at extremes of temperatures likely to be faced in the field. METHODS: This is a bench research study conducted in the laboratory setting over a 4-week period. Parenteral medications were separated into 4 temperature exposure groups: A) 45°C (hot); B) -20°C (cold); C) hot then cold temperatures alternating weekly; and D) cold then hot temperatures alternating weekly. At study start and the end of each week, three aliquots from each group were sampled to determine the remaining drug concentration through liquid chromatography-quadrupole time-of-flight mass spectrometry (Agilent LC 1260- QTOF/MS 6550). Quantitative analysis was done using Agilent MassHunter Quantitative Analysis software. The mean drug concentration from triplicate aliquots was expressed as percentage of its baseline concentration to monitor the drug's stability during storage. RESULTS: Eight medications were analyzed (atropine, diphenhydramine, fentanyl, hydromorphone, midazolam, morphine, naloxone, ondansetron). Hydromorphone, morphine, and ondansetron showed the greatest stability, at above 90% of original concentration in all study arms. Diphenhydramine, fentanyl and midazolam showed heat independent degradation, degrading the same way regardless of heat exposure. By the end of the study period, 51-56% midazolam remained in all groups. Atropine and naloxone showed heat dependent degradation, degrading more when exposed to heat. Atropine had the most degradation, being undetectable after 4 weeks of heat exposure. CONCLUSIONS: We recommend that EMS providers replace atropine, naloxone, diphenhydramine, fentanyl, and midazolam frequently if they are practicing in low call volume or high-temperature environments. Further studies will be needed to determine if re-dosing midazolam, naloxone, and atropine is the appropriate clinical strategy in this setting if adequate clinical effect is not reached with a single dose.

Selecting precise reference normal tissue samples for cancer research using a deep learning approach.

BACKGROUND: Normal tissue samples are often employed as a control for understanding disease mechanisms, however, collecting matched normal tissues from patients is difficult in many instances. In cancer research, for example, the open cancer resources such as TCGA and TARGET do not provide matched tissue samples for every cancer or cancer subtype. The recent GTEx project has profiled samples from healthy individuals, providing an excellent resource for this field, yet the feasibility of using GTEx samples as the reference remains unanswered. METHODS: We analyze RNA-Seq data processed from the same computational pipeline and systematically evaluate GTEx as a potential reference resource. We use those cancers that have adjacent normal tissues in TCGA as a benchmark for the evaluation. To correlate tumor samples and normal samples, we explore top varying genes, reduced features from principal component analysis, and encoded features from an autoencoder neural network. We first evaluate whether these methods can identify the correct tissue of origin from GTEx for a given cancer and then seek to answer whether disease expression signatures are consistent between those derived from TCGA and from GTEx. RESULTS: Among 32 TCGA cancers, 18 cancers have less than 10 matched adjacent normal tissue samples. Among three methods, autoencoder performed the best in predicting tissue of origin, with 12 of 14 cancers correctly predicted. The reason for misclassification of two cancers is that none of normal samples from GTEx correlate well with any tumor samples in these cancers. This suggests that GTEx has matched tissues for the majority cancers, but not all. While using autoencoder to select proper normal samples for disease signature creation, we found that disease signatures derived from normal samples selected via an autoencoder from GTEx are consistent with those derived from adjacent samples from TCGA in many cases. Interestingly, choosing top 50 mostly correlated samples regardless of tissue type performed reasonably well or even better in some cancers. CONCLUSIONS: Our findings demonstrate that samples from GTEx can serve as reference normal samples for cancers, especially those do not have available adjacent tissue samples. A deep-learning based approach holds promise to select proper normal samples.

Evaluating cell lines as models for metastatic breast cancer through integrative analysis of genomic data.

Cell lines are widely-used models to study metastatic cancer although the extent to which they recapitulate the disease in patients remains unknown. The recent accumulation of genomic data provides an unprecedented opportunity to evaluate the utility of them for metastatic cancer research. Here, we reveal substantial genomic differences between breast cancer cell lines and metastatic breast cancer patient samples. We also identify cell lines that more closely resemble the different subtypes of metastatic breast cancer seen in the clinic and show that surprisingly, MDA-MB-231 cells bear little genomic similarities to basal-like metastatic breast cancer patient samples. Further comparison suggests that organoids more closely resemble the transcriptome of metastatic breast cancer samples compared to cell lines. Our work provides a guide for cell line selection in the context of breast cancer metastasis and highlights the potential of organoids in these studies.

Quantitative analysis of powdered caffeine products purchased from the Internet using liquid chromatography-quadrupole time-of-flight mass spectrometry.

CONTEXT: Powdered caffeine is sold on the Internet as a supplement. Severe toxicity and fatalities have been reported with use, but it is unclear if this toxicity was due to excessive dosing, mislabeled products, or adulterant stimulants. Our objective was to analyze the contents of commercially available powdered caffeine products in order to assess product purity and presence of additional ingredients, contaminants, or adulterants which may contribute to toxicity. METHODS: A sample of nine powdered caffeine products was purchased from the Internet. Two sample replicates of each caffeine product were analyzed. Liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) was used to identify and quantify substances in the purchased products and purity of the compounds were calculated. RESULTS: Comparison of actual mass versus labeled mass of caffeine demonstrated a mean purity of 88.25% (SD 13.41%) and median purity of 90.1%. The products studied contained 1.6-5.3 g per teaspoon. Labeling on these products had limited instructions regarding how to measure the recommended dose. CONCLUSIONS: Powdered caffeine products that are readily available on the Internet contained relatively pure caffeine with no additional detected stimulants. High purity, small serving size, and lack of clear dosing instructions may place users at risk of toxicity.