Broadening the capture of natural products mentioned in FAERS using fuzzy string-matching and a Siamese neural network.

Increased sales of natural products (NPs) in the US and growing safety concerns highlight the need for NP pharmacovigilance. A challenge for NP pharmacovigilance is ambiguity when referring to NPs in spontaneous reporting systems. We used a combination of fuzzy string-matching and a neural network to reduce this ambiguity. Our aim is to increase the capture of reports involving NPs in the US Food and Drug Administration Adverse Event Reporting System (FAERS). For this, we utilized Gestalt pattern-matching (GPM) and Siamese neural network (SM) to identify potential mentions of NPs of interest in 389,386 FAERS reports with unmapped drug names. A team of health professionals refined the candidates identified in the previous step through manual review and annotation. After candidate adjudication, GPM identified 595 unique NP names and SM 504. There was little overlap between candidates identified by each (Non-overlapping: GPM 347, SM 248). We identified a total of 686 novel NP names from FAERS reports. Including these names in the FAERS collection yielded 3,486 additional reports mentioning NPs.

Broadening the Capture of Natural Products Mentioned in FAERS Using Fuzzy String-Matching and a Siamese Neural Network.

Increased sales of natural products (NPs) in the US and growing safety concerns highlight the need for NP pharmacovigilance. A challenge for NP pharmacovigilance is ambiguity when referring to NPs in spontaneous reporting systems. We used a combination of fuzzy string-matching and a neural network to reduce this ambiguity. We aim to increase the capture of reports involving NPs in the US Food and Drug Administration Adverse Event Reporting System (FAERS). Gestalt pattern-matching (GPM) and Siamese neural network (SM) were used to identify potential mentions of NPs of interest in 389,386 FAERS reports with unmapped drug names. We refined the identified candidates through manual review and annotation by health professionals. After adjudication, GPM identified 595 unique NP names and SM 504. There was little overlap between candidates identified by the approaches (Non-overlapping: GPM 347, SM 248). In total, 686 novel NP names were identified in the unmapped FAERS reports. Including these names in the FAERS collection yielded 3,486 additional reports mentioning NPs.

The Role of Gut Microbiome Perturbation in Fatigue Induced by Repeated Stress from Chemoradiotherapy: A Proof of Concept Study.

OBJECTIVES: The objectives of this proof of concept study were to (a) examine the temporal changes in fatigue and diversity of the gut microbiome over the course of chemoradiotherapy (CRT) in adults with rectal cancers; (b) investigate whether there are differences in diversity of the gut microbiome between fatigued and nonfatigued participants at the middle and at the end of CRT; and (c) investigate whether there are differences in the relative abundance of fecal microbiota at the phylum and genus levels between fatigued and nonfatigued participants at the middle and at the end of CRT. METHODS: Stool samples and symptom ratings were collected prior to the inception of CRT, at the middle (after 12-16 treatments) and at the end (after 24-28 treatments) of the CRT. Descriptive statistics and Mann-Whitney U test were computed for fatigue. Gut microbiome data were analyzed using the QIIME2 software. RESULTS: Participants (N = 29) ranged in age from 37 to 80 years. The median fatigue score significantly changed at the end of CRT (median = 23.0) compared with the median score before the initiation of CRT for the total sample (median = 17.0; p ≤ 0.05). At the middle of CRT, the alpha diversity (abundance of Operational Taxonomic Units) was lower for fatigued participants (149.30 ± 53.1) than for nonfatigued participants (189.15 ± 44.18, t(23) = 2.08, p ≤ 0.05). At the middle of CRT, the alpha diversity (abundance of Operational Taxonomic Units) was lower for fatigued participants (149.30 ± 53.1) than for nonfatigued participants (189.15 ± 44.18, Proteobacteria, Firmicutes, and Bacteroidetes were the dominant phyla for fatigued participants, and Escherichia, Bacteroides, Faecalibacterium, and Oscillospira were the most abundant genera for fatigued participants. CONCLUSION: CRT-associated perturbation of the gut microbiome composition may contribute to fatigue.