Since its identification in April 2009 an A(H1N1) virus containing a unique combination of gene segments from both North American and Eurasian swine lineages has continued to circulate in humans. The 2009 A(H1N1) virus is distantly related to its nearest relatives, indicating that its gene segments have been circulating undetected for an extended period. Low genetic diversity among the viruses suggests the introduction into humans was a single event or multiple events of similar viruses. Molecular markers predicted for adaptation to humans are not currently present in 2009 A(H1N1) viruses, suggesting previously unrecognized molecular determinants could be responsible for the transmission among humans. Antigenically the viruses are homogeneous and similar to North American swine A(H1N1) viruses but distinct from seasonal human A(H1N1).

ImportanceThe rapid expansion of virtual health care has caused a surge in patient messages concomitant with more work and burnout among health care professionals. Artificial intelligence (AI) assistants could potentially aid in creating answers to patient questions by drafting responses that could be reviewed by clinicians.ObjectiveTo evaluate the ability of an AI chatbot assistant (ChatGPT), released in November 2022, to provide quality and empathetic responses to patient questions.Design, Setting, and ParticipantsIn this cross-sectional study, a public and nonidentifiable database of questions from a public social media forum (Reddit’s r/AskDocs) was used to randomly draw 195 exchanges from October 2022 where a verified physician responded to a public question. Chatbot responses were generated by entering the original question into a fresh session (without prior questions having been asked in the session) on December 22 and 23, 2022. The original question along with anonymized and randomly ordered physician and chatbot responses were evaluated in triplicate by a team of licensed health care professionals. Evaluators chose “which response was better” and judged both “the quality of information provided” (very poor, poor, acceptable, good, or very good) and “the empathy or bedside manner provided” (not empathetic, slightly empathetic, moderately empathetic, empathetic, and very empathetic). Mean outcomes were ordered on a 1 to 5 scale and compared between chatbot and physicians.ResultsOf the 195 questions and responses, evaluators preferred chatbot responses to physician responses in 78.6% (95% CI, 75.0%-81.8%) of the 585 evaluations. Mean (IQR) physician responses were significantly shorter than chatbot responses (52 [17-62] words vs 211 [168-245] words; t = 25.4; P < .001). Chatbot responses were rated of significantly higher quality than physician responses (t = 13.3; P < .001). The proportion of responses rated as good or very good quality (≥ 4), for instance, was higher for chatbot than physicians (chatbot: 78.5%, 95% CI, 72.3%-84.1%; physicians: 22.1%, 95% CI, 16.4%-28.2%;). This amounted to 3.6 times higher prevalence of good or very good quality responses for the chatbot. Chatbot responses were also rated significantly more empathetic than physician responses (t = 18.9; P < .001). The proportion of responses rated empathetic or very empathetic (≥4) was higher for chatbot than for physicians (physicians: 4.6%, 95% CI, 2.1%-7.7%; chatbot: 45.1%, 95% CI, 38.5%-51.8%; physicians: 4.6%, 95% CI, 2.1%-7.7%). This amounted to 9.8 times higher prevalence of empathetic or very empathetic responses for the chatbot.ConclusionsIn this cross-sectional study, a chatbot generated quality and empathetic responses to patient questions posed in an online forum. Further exploration of this technology is warranted in clinical settings, such as using chatbot to draft responses that physicians could then edit. Randomized trials could assess further if using AI assistants might improve responses, lower clinician burnout, and improve patient outcomes.

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The rapid emergence and subsequent spread of the novel 2009 Influenza A/H1N1 virus (2009 H1N1) has prompted the World Health Organization to declare the first pandemic of the 21st century, highlighting the threat of influenza to public health and healthcare systems. Widespread resistance to both classes of influenza antivirals (adamantanes and neuraminidase inhibitors) occurs in both pandemic and seasonal viruses, rendering these drugs to be of marginal utility in the treatment modality. Worldwide, virtually all 2009 H1N1 and seasonal H3N2 strains are resistant to the adamantanes (rimantadine and amantadine), and the majority of seasonal H1N1 strains are resistant to oseltamivir, the most widely prescribed neuraminidase inhibitor (NAI). To address the need for more effective therapy, we evaluated the in vitro activity of a triple combination antiviral drug (TCAD) regimen composed of drugs with different mechanisms of action against drug-resistant seasonal and 2009 H1N1 influenza viruses. Amantadine, ribavirin, and oseltamivir, alone and in combination, were tested against amantadine- and oseltamivir-resistant influenza A viruses using an in vitro infection model in MDCK cells. Our data show that the triple combination was highly synergistic against drug-resistant viruses, and the synergy of the triple combination was significantly greater than the synergy of any double combination tested (P<0.05), including the combination of two NAIs. Surprisingly, amantadine and oseltamivir contributed to the antiviral activity of the TCAD regimen against amantadine- and oseltamivir-resistant viruses, respectively, at concentrations where they had no activity as single agents, and at concentrations that were clinically achievable. Our data demonstrate that the TCAD regimen composed of amantadine, ribavirin, and oseltamivir is highly synergistic against resistant viruses, including 2009 H1N1. The TCAD regimen overcomes baseline drug resistance to both classes of approved influenza antivirals, and thus may represent a highly active antiviral therapy for seasonal and pandemic influenza.

We measured gluconeogenesis (GNG) in rats by mass isotopomer distribution analysis, which allows enrichment of the true biosynthetic precursor pool (hepatic cytosolic triose phosphates) to be determined. Fractional GNG from infused [3-13C]lactate, [1-13C]lactate, and [2-13C]glycerol was 88 +/- 2, 89 +/- 3, and 87 +/- 2%, respectively, after 48 h of fasting. [2-13C]Glycerol was the most efficient label and allowed measurement of rate of appearance of intrahepatic triose phosphate (Ra triose-P), by dilution. IV fructose (10-15 mg/kg/min) increased absolute GNG by 81-147%. Ra triose-P increased proportionately, but endogenous Ra triose-P was almost completely suppressed, suggesting feedback control. Interestingly, 15-17% of fructose was directly converted to glucose without entering hepatic triose-P. IV glucose reduced GNG and Ra triose-P. 24-h fasting reduced hepatic glucose production by half, but absolute GNG was unchanged due to increased fractional GNG (51-87%). Reduced hepatic glucose production was entirely due to decreased glycogen input, from 7.3 +/- 1.8 to 1.1 +/- 0.2 mg/kg/min. Ra triose-P fell during fasting, but efficiency of triose-P disposal into GNG increased, maintaining GNG constant. Secreted glucuronyl conjugates and plasma glucose results correlated closely. In summary, GNG and intrahepatic triose-P flux can be measured by mass isotopomer distribution analysis with [2-13C]glycerol.