Molly

The purified sample DNA prepared as described above was mixed with MBD-Fc protein A beads in a ratio of 1 µg of sample DNA to 160 µL of beads. The sample DNA was directly added to the bead slurry and incubated for 15 minutes at room temperature with gentle rotation. The incubated mixture was placed on a magnetic rack at room temperature until the supernatant was clear and beads were collected on the wall of the tube (2–5 minutes). The supernatant, containing enriched microbial DNA was carefully removed with a pipette without disturbing the beads, purified by 1.8× volume of Agencourt AMPure® XP beads (Beckman Coulter #A63880) according to the manufacturer’s instructions and the DNA was eluted in 150 µL 1× TE buffer (pH 7.5). Volumes for this procedure were scaled directly depending on the amount of input DNA.
For the samples described in this study, the following specific input amounts used for the microbial DNA enrichment experiments were as follows: saliva microbiome enrichment, 12 µg of input DNA was enriched using 2 mL of MBD-Fc Protein A paramagnetic beads; black molly microbiome enrichment, 9.12 µg of input DNA was enriched using 1.5 mL of MBD-Fc Protein A paramagnetic beads; and for Plasmodium falciparum DNA enrichment, 2 µg Plasmodium falciparum/human DNA mixture was enriched using 320 µL of MBD-Fc Protein A paramagnetic beads.

We used manual content analysis to evaluate the responses in addition to automated algorithms. Two clinicians and 4 other members of the research team (SS, JYC, LYA, Molly Cosgrove, RW, and MR-M) created initial manual content analysis categories. One clinician and 5 other members of the research team (SS, LYA, Molly Cosgrove, RW, MR-M, and JSS) annotated 4 small practice batches and met after each to discuss ambiguities and refine categories and definitions. In total, 36 categories and definitions were agreed upon and sorted into 6 overarching themes: mental health, physical health, social factors, career and finances, society (including government, community, or both), and other. A complete list of the categories is presented in Multimedia Appendix 1. Free responses were divided among the 4 coders (SS, MR-M, RW, and JSS), and each response was reviewed and scored by 2 randomly selected coders. Each coder labeled the responses based on their content as belonging to ≥1 manual categories. The responses were annotated in 5 batches. For the fifth batch, with a date range from October 11, 2020, to May 5, 2021, LYA annotated instead of MR-M and the category “Vaccines” was added based on a consensus of the coders after noting changes in the themes of responses. Responses such as “No,” “NA,” and “Nothing to report” were not categorized by any coder and were classified as nonresponses that were removed from subsequent analyses. Clinicians (SS and JYC) reviewed responses marked as clinically significant to evaluate severity.
To assess agreement between coders, interrater reliability (IRR) was calculated using the irrCAC package to find both the Fleiss κ and Gwet AC1 statistic [56 ]. The 2 methods were chosen to complement each other because the κ statistic is very commonly used for IRR, whereas the Gwet AC1 statistic overcomes some of the κ statistic’s weakness with data with low variability [57 (link),58 (link)]. For both measures, we evaluated agreement using the 1991 Altman interpretation of the κ statistic, in which <0.2 is poor, 0.2 to 0.4 is fair, 0.4 to 0.6 is moderate, 0.6 to 0.8 is good, and 0.8 to 1.0 is very good agreement [59 ].

All participants who accessed care through BRITE via our drop-in telehealth or in-person clinical visits between 1 February 2021 through 30 May 2022 and were prescribed buprenorphine were included in a retrospective chart review. The Electronic Health Record (EHR) was utilized to abstract data from physician notes, psychologist notes and laboratory values. The procedure consisted of recording initial and follow up appointment dates with the physician, prescription order records (including doses and dates) and UDS results. Types of substances used by the participant were identified within physician notes, which included participant self-report or UDS results, depending on the modality of the encounter. The UDS tested for cocaine, methamphetamine, amphetamine, 3,4-methylenedioxy-methamphetamine (MDMA), cannabis, methadone, buprenorphine, benzodiazepines, morphine, oxycodone, phencyclidine and ethyl glucuronide. Many BRITE participants self-reported injecting ‘molly’, but recent findings suggests this substance is likely a synthetic cathinone [35 (link)]. UDS were used to facilitate diagnosis and management and were not used punitively. UDS allowed physicians to assess response to treatment and need for intensification of support services and confirm the participant was taking the buprenorphine.
Florida’s Prescription Drug Monitoring Program (PDMP) was utilized to collect data on the dates when buprenorphine prescriptions were picked up at the pharmacy by either the participant or SSP staff and to confirm the doses of these prescriptions. SSP staff only called refills into the pharmacy at the request of participants. Only buprenorphine prescribed by SSP physicians were included. MOUD initiation was defined as first collection of buprenorphine from the pharmacy. Measurement of buprenorphine dosage prescribed was tracked utilizing the PDMP to identify trends in escalation. Daily dosages of buprenorphine were 8 mg, 16 mg, 24 mg or 32 mg per day, and escalation was defined as prescription of a higher dose after the baseline visit.
Sociodemographic data and engagement in telehealth services were abstracted from clinical notes, the BRITE clinic enrollment form and the IDEA SSP administrative database. Engagement with a provider via telehealth was defined as having at least one MD visit via telehealth at baseline or follow-up visit (yes/no). HIV and HCV status were abstracted by cross referencing physician notes and laboratory values in the EHR with the SSP administrative database. All abstracted data were de-identified and recorded in REDCap for data management and the trained data abstracters met on a weekly basis to enhance data quality [36 (link)].

Rhythm aptitude was measured with the rhythm section of the computer-based version of the Primary Measures of Music Audiation (Gordon, 1979 (link)). In each trial, the children were presented with two monotonic melodic rhythmic excerpts that were either identical or slightly varied in their temporal pattern, and then they were asked to decide whether the two rhythms were the same or different. The task consisted of two practice trials with one same and one different trial and 40 test trials with 20 same and 20 different trials. Children were given feedback about the correctness of their answers only in the practice phase and not in the testing phase. Same and different trials appeared in the same order for each child. The task was implemented in a game format in which a dog named Molly gets one step closer to her home with every answer. Depending on their computer abilities, children responded verbally or by selecting the option with a computer mouse. We used d′, the difference between z-scored hit and false alarm rate as a measure of rhythm discrimination.