Evaluation of viral infection as an etiology of ME/CFS: a systematic review and meta-analysis.

BACKGROUND: Myalgic encephalitis/chronic fatigue syndrome (ME/CFS) is a long-term disabling illness without a medically explained cause. Recently during COVID-19 pandemic, many studies have confirmed the symptoms similar to ME/CFS in the recovered individuals. To investigate the virus-related etiopathogenesis of ME/CFS, we conducted a systematic assessment of viral infection frequency in ME/CFS patients. METHODS: We conducted a comprehensive search of PubMed and the Cochrane Library from their inception through December 31, 2022, using selection criteria of viral infection prevalence in ME/CFS patients and controls. Subsequently, we performed a meta-analysis to assess the extent of viral infections' contribution to ME/CFS by comparing the odds ratio between ME/CFS patients and controls (healthy and/or diseased). RESULTS: Finally, 64 studies met our eligibility criteria regarding 18 species of viruses, including a total of 4971 ME/CFS patients and 9221 control subjects. The participants included healthy subjects and individuals with one of 10 diseases, such as multiple sclerosis or fibromyalgia. Two DNA viruses (human herpes virus (HHV)-7 and parvovirus B19, including their co-infection) and 3 RNA viruses (borna disease virus (BDV), enterovirus and coxsackie B virus) showed odds ratios greater than 2.0 compared with healthy and/or diseased subjects. Specifically, BDV exceeded the cutoff with an odds ratio of ≥ 3.47 (indicating a "moderate association" by Cohen's d test) compared to both healthy and diseased controls. CONCLUSION: This study comprehensively evaluated the risk of viral infections associated with ME/CFS, and identified BDV. These results provide valuable reference data for future studies investigating the role of viruses in the causation of ME/CFS.

Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction.

OBJECTIVE: The purpose of our study is to develop a spoken dialogue system (SDS) for pain questionnaire in patients with spinal disease. We evaluate user satisfaction and validated the performance accuracy of the SDS in medical staff and patients. METHODS: The SDS was developed to investigate pain and related psychological issues in patients with spinal diseases based on the pain questionnaire protocol. We recognized patients' various answers, summarized important information, and documented them. User satisfaction and performance accuracy were evaluated in 30 potential users of SDS, including doctors, nurses, and patients and statistically analyzed. RESULTS: The overall satisfaction score of 30 patients was 5.5 ± 1.4 out of 7 points. Satisfaction scores were 5.3 ± 0.8 for doctors, 6.0 ± 0.6 for nurses, and 5.3 ± 0.5 for patients. In terms of performance accuracy, the number of repetitions of the same question was 13, 16, and 33 (13.5%, 16.8%, and 34.7%) for doctors, nurses, and patients, respectively. The number of errors in the summarized comment by the SDS was 5, 0, and 11 (5.2%, 0.0%, and 11.6 %), respectively. The number of summarization omissions was 7, 5, and 7 (7.3%, 5.3%, and 7.4%), respectively. CONCLUSION: This is the first study in which voice-based conversational artificial intelligence (AI) was developed for a spinal pain questionnaire and validated by medical staff and patients. The conversational AI showed favorable results in terms of user satisfaction and performance accuracy. Conversational AI can be useful for the diagnosis and remote monitoring of various patients as well as for pain questionnaires in the future.