We also compared the regional differences and the effects of lake browning on the reliance of zooplankton on heterotrophic microbial pathways and the mass fractions of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in zooplankton. Lake browning was associated with increasing phytoplankton biomass and concentrations of EPA and DHA in both temperate and boreal lakes, but the seston n−3/n−6 ratio was lower in temperate than boreal lakes, most likely due the differences in phytoplankton community composition. The browning-induced increase in phytoplankton biomass was associated with increased reliance of zooplankton on a heterotrophic microbial pathway for both cladocerans and copepods in boreal and temperate lakes. This increased reliance on the heterotrophic microbial diet was correlated with a decrease in the EPA and DHA mass fractions in temperate copepods and a decrease in the n−3/n−6 ratio in boreal cladocerans and copepods. Our results indicate that although phytoplankton responses to lake browning were similar across regions, this did not directly cascade to the next trophic level, where zooplankton responses were highly taxa- and region-specific. These results indicate that lake browning should be considered as an overarching moderator that is linked to, e.g., nutrient increases, which have more immediate consequences on trophic interactions at the phytoplankton–zooplankton interface.The effects of lake browning on trophic functioning of planktonic food webs are not fully understood. We studied the effects of browning on the response patterns of polyunsaturated fatty acids and The online version contains supplementary material available at 10.1007/s00442-022-05301-w. Northern hemisphere lakes are browning due to higher loadings of dissolved organic carbon (DOC), and associated iron (Fe) from their watersheds , including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)  caught from high DOC lakes in comparison to perch from low DOC lakes  and Norway spruce (Picea abies) on mineral soil (40 to 65% of watershed areas) and peatlands (3 to 47% of watershed areas). Lake mean DOC ranged from 2.8 to 18.9 mg L−1, chlorophyll a (Chl-a) ranged from 1.3 to 9.5 μg L−1, total phosphorus (TP) ranged from 1.6 to 28.8 μg L−1, and nitrogen (TN) ranged from 169 and 629 μg L−1 (ESM Table S1). The temperate watersheds are located in mixed-wood forests dominated by deciduous sugar maple (Acer saccharum), yellow birch , and beech (Fagus spp.), with smaller presences of coniferous white pine (Pinus strobus), balsam fir , and eastern hemlock (Tsuga canadensis). The proportion of wetlands in the temperate watersheds ranged from 0.5 to 45%. Lake mean DOC ranged from 2.6 to 9.8 mg L−1, Chl-a ranged from 1.0 to 12.5 μg L−1, TP ranged from 6.0 to 48.5 μg L−1, and TN and 200 to 640 μg L−1. The temperate lake data were published in Senar et al. . The mean depth of boreal and temperate lakes ranged from 1.8 to 12 m and from 2.3 to 24 m, respectively. The boreal watersheds are located in coniferous forests .Water samples: Concentrations of TP, TN, Chl-a, DOC, and Fe were analyzed in boreal and temperate lakes, except for the boreal Fe concentrations which were obtained from the Hertta databank (https://www.syke.fi/avointieto). Secchi depth, pH, and SUVA254 were also measured. We did not have direct concentration measurements or phytoplankton biomass data for the temperate lakes. Hence, we used Chl-a as a proxy for phytoplankton biomass for both the temperate and the boreal lakes  after which the samples were sieved through 80 µm to remove zooplankton. Seston samples were filtered on 0.45 µm filters, from which the cells were washed into tubes, frozen, and lyophilized  from the same site as the seston samples were collected. Samples were stored at −20 °C until analysis. In the laboratory, samples were briefly thawed and sorted to the following categories and taxa: filter-feeding cladocerans, predatory cladocerans: Bythotrephes longimanus  from the same site as the seston samples were collected. Zooplankton were immediately separated into filter-feeding cladocerans and copepods; one sample for both taxa per lake   and/or weight percentages (w%) of total fatty acids.Lipids were extracted with chloroform–methanol (2:1 by volume) and extracted lipids were transmethylated with 1% sulfuric acid in methanol at 90 °C for 90 min  and the overall phytoplankton biomass, expressed as Chl-a concentration. Seston fatty acid profiles were weighted by the Chl-a concentration as follows:We conducted a nonparametric Mann–Whitney a concentration and fatty acid w% are presented in the electronic supplementary materials (ESM Table S3). Only fatty acids that are generally abundant in phytoplankton were weighted by the Chl-a concentration, i.e., 14:0, 16:0, 18:0, 16:1n−7, 18:1n−9, 18:1n−7, 16:4n−3, 18:3n−3, 18:4n−3, 20:5n−3, 22:6n−3, 18:2n−6, 18:3n−6, 20:4n−6, and 22:5n−6. We excluded odd-numbered and branched fatty acids, as well as ≥ C20 SFA from the calculation. The proportion of the excluded fatty acids accounted for 4.5–10.8% of total fatty acids in the boreal lakes and 2.9–9.6% of total fatty acid in the temperate lakes. We acknowledge that bacteria and small heterotrophic flagellates and ciliates likely contribute to the pool of saturated fatty acid (SFA), monounsaturated fatty acid (MUFA), and, to some extent, maybe even PUFA, but their overall contribution to the total fatty acid pool is much lower than that of phytoplankton  were strongly correlated with the Chl-a weighed indices for PUFA, MUFA and SFA in general (ESM Table S4), and EPA and DHA in specific  in boreal and temperate lake. Phytoplankton Chl-a weighed fatty acid data were log(x + 1) transformed prior to the analyses, and Euclidean distance was used as a resemblance matrix. We conducted stepwise model selection and used AIC as model fit criteria. The results are visualized with dbRDA. Continuous predictors were DOC and Fe concentration, SUVA254, concentrations of TP and TN, coordinates, Secchi depth, pH, lake area, and lakes are as % of watershed. Chl-a was not included as a predictor, because we used Chl-a weighed fatty acids as response variables. Predictor values were normalized prior to the analyses. The concentrations of DOC and Fe, Secchi depth and SUVA254 value were grouped as ‘Browning’ indicators, TP and TN concentrations were grouped as ‘Nutrient’ indicators, coordinates were grouped as ‘Region’ indicators, and lake area and lake area % of watershed were grouped as ‘Morphometry’ indicators. The browning and nutrient predictors were correlated, but we decided to keep these separate, because the slopes differed between the temperate and boreal lakes (P = 0.002). Furthermore, the correlation coefficients for the ‘Nutrient’ and ‘Browning’ predictors were 0.55–0.65, i.e., well below the cut-off level (0.95) for colinear predictors recommended by Anderson et al. (r = −0.66), pH did not correlate with DOC concentration in the temperate lakes. Lake pH value is affected by several local characteristics and activities which may or may not be related to lake browning . Additionally, we included ‘Sampling method’ as a categorical predictor to account for any differences due to sampling method, including seston collection and/or calculation of the Chl-a weighed values.We performed a distance-based linear model (DistLM) to analyze the effect of environmental factors on the phytoplankton fatty acid concentrations  and bacterial fatty acids (BAFA). BAFA represent the sum of odd-chained and branched fatty acids. We used BAFA w% in zooplankton to estimate their reliance on the heterotrophic pathway. We used DistLM to analyze the explanatory power of environmental drivers on, (a) fatty acid quality indices, i.e., the content of EPA and DHA, and the n−3/n−6 in zooplankton and (b) the index for evaluating the relative importance of the heterotrophic microbial pathway for zooplankton, i.e., BAFA w% in zooplankton. We chose these distinct response factors because EPA and DHA are important nutrients for upper trophic level consumers, including fish, and impaired trophic transfer of EPA and DHA may transpire at the algae-zooplankton interface , and the n−3/n−6 ratio and BAFA% were arcsine square-root transformed. We analyzed the Pearson correlations, using bootstrapping (1000 iterations), between the Chl-a concentrations and the reliance of zooplankton on the heterotrophic pathway. We also correlated the reliance of zooplankton to heterotrophic pathway with the n−3/n−6 ratio and the mass fractions of EPA and DHA in zooplankton. We present the 95% confidence interval (bias corrected) of the bootstrapped correlations. All multivariate analyses were conducted with the Primer 6 software equipped with PERMANOVA + add-on. Univariate analyses were done with IBM SPSS Statistics 27.We calculated the mass fractions of major fatty acid groups in zooplankton: SFA, MUFA, and PUFA with 18–24 carbons in the acyl chain . Also, the SUVA254 values were higher in the boreal than in the temperate lakes , indicating differences in the quality and molecular composition of dissolved organic matter (ESM Table S1). Ranges for Secchi depth, pH, and concentrations of Chl-a, TP, and TN did not differ between boreal and temperate lakes. Chl-a increased with increasing DOC concentrations in the studied lakes, but the slope was steeper (p < 0.001) in the temperate than in the boreal lakes . The DOC concentration was higher in boreal lakes than in temperate lakes  explained about 57% of the variation in the phytoplankton fatty acids in the study lakes. Location, i.e., sampling coordinates, was a significant predictor in the model; after the effects of lake browning were considered, the explanatory power of location was about 9%. In both regions, the Chl-a weighed PUFA concentrations increased with increasing lake browning. Although the total concentration of fatty acids was driven by browning indicators, the relative contributions of n-3 and n-6 PUFA were correlated with dbRDA 2 and was driven more by ‘location’ than by ‘browning’. In general, temperate lakes were more enriched with n-6 PUFA, and boreal lakes were more enriched with n−3 PUFA  vs. boreal lakes (5.7 ± 0.9) (ESM Table S3). The marginal test showed that sampling method alone could explain only 7.7% of the variation in the data set, but this was not significant (P = 0.056). Furthermore, sampling method did not significantly improve the model. However, we do not completely rule out that sampling method may have a minor effect, because it was strongly confounded by ‘Location’, which was a significant predictor.The best distance-based linear model of the Chl-ds Table . Model rds Table . Lake brn−3) proportions were found in filter-feeding cladocerans from Lake Kermajärvi, ~ 13% of all fatty acids, while the mean for across other boreal lakes was 1.9% (ESM Table S5). We repeated the analysis with additional cladoceran samples from this lake to confirm that this was not an analytical artifact. Cladocerans from the temperate lakes also contained significant amounts of DHA, on average ~ 8% of total fatty acids.In general, cladocerans had a lower fatty acid content in boreal than in temperate lakes Fig.  and the Eudiaptomus and Heterocope, contained ~ 42% of total fatty acids) and temperate lakes  (ESM Tables S7 and S8). The highest PUFA content, ~ 55% of total fatty acids, of all analyzed taxa, was found in the calanoid copepod Limnocalanus macrurus (ESM Table S9). Limnocalanus contained also very-long-chain PUFA (C24 PUFA), which were not found in any other taxa  and ‘Location’ by 6% (P = 0.018). The best model was obtained for ‘Phytoplankton biomass’, ‘Taxa’ and ‘Location’ (r2 = 0.47). The marginal test showed that ‘Browning’ could explain 24% of the variation in the BAFA% in zooplankton, but ‘Browning’ did not improve the model after ‘Phytoplankton biomass’, ‘Taxa’, and ‘Location’ had been accounted for . Other significant predictors explaining the variation in n−3/n−6 in zooplankton were: ‘Phytoplankton biomass’ and ‘Location’  and phytoplankton biomass. Lake DOC and nutrient concentrations are tightly coupled Below is the link to the electronic supplementary material.


Little is known of whether Internet use is associated with physical activity among socially isolated older adults during the coronavirus disease 2019 (COVID-19) pandemic. This study investigated the association between Internet use and physical activity, and whether this association differs depending on social isolation among community-dwelling Japanese older adults.A cross-sectional study was conducted with 1048 community-dwelling residents aged 65–90 years. Data were obtained using a self-reported questionnaire in August 2020. Physical activity was assessed using the International Physical Activity Questionnaire-Short Form. Multivariable logistic regression analyses were used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between Internet use and moderate-to-vigorous physical activity (MVPA).Internet use showed a significant association with MVPA  after adjusting for age, sex, self-reported socioeconomic status, and other health-related characteristics. When the results were stratified by social participation and living status, Internet use was associated with a significantly higher likelihood of MVPA among participants with no social participation  and living with family .Internet use was associated with sufficient physical activity, and this association may differ depending on the social isolation among community-dwelling older adults in Japan. Since the beginning of 2020, there have been concerns that the amount of physical activity older adults engage in is decreasing due to the lockdowns and physical and social distancing measures introduced to reduce the transmission of coronavirus disease 2019 (COVID-19). Recent studies have shown that, in Japan and worldwide, physical activity of all intensity levels  has decreased and daily sitting time has increased for older people compared to pre-pandemic period . In our Although vaccination has minimized, to some extent, the lifestyle restrictions the older adults due to COVID-19 , the epiA cross-sectional, community-based study was conducted in August 2020 in Eniwa, Hokkaido Prefecture, Japan . With reInternet use was assessed using the question, “Have you used the Internet or e-mail in the past year? Please include the frequency of use.” The following four response options were provided: “No, I do not use the Internet,” “Yes, I use the Internet a few times a month,” “Yes, I use the Internet two or three times a week,” and “Yes, I use the Internet almost every day.” We considered that the latter three answers indicate ICT users. The following five answers helped evaluate when the participants started using the Internet and e-mail: “more than 10 years ago,” “more than 5 years ago,” “3 to 4 years ago,” “1 to 2 years ago,” and “less than 1 year ago.” Furthermore, participants were asked about the purpose of their Internet use through the following response options: “gathering/searching information on health and medical care,” “gathering and searching for various types of information” , “communicating,” “purchasing products and services,” “conducting bank transactions,” “conducting stock and securities transactions,” “using social media service,” and “other.”r = 0.42–0.53) [Physical activity was assessed using the International Physical Activity Questionnaire-Short Form (IPAQ-SF) , The Jap42–0.53) . Partici42–0.53)  because A self-administered questionnaire was used to collect data, and the following variables were considered as covariates: age, sex, self-reported health status, smoking status, educational background, economic status, depression status, family structure, and social participation. Self-reported health status was categorized as “good” or “poor.” Smoking status was categorized “yes” or “no.” Education level was categorized as “high school or less” or “more than high school.” Economic status was assessed using a subjective measurement through the question, “How do you feel about your current economic situation?” The five response options were “excellent,” “good,” “normal,” “poor,” and “very poor.” We classified excellent, good, and normal as “normal to good,” and poor and very poor as “poor” . Depressp-value of < 0.05.The data are presented as the mean ± standard deviation and the percentage (number) of participants in that category. We examined the association between Internet use and MVPA using a logistic regression model. Further, we calculated the odds ratios and 95% confidence interval (CIs) of MVPA for Internet use using the following models, selected based on previous studies , 30, 31.In the overall sample, the mean age ± standard deviation was 74.4 ± 6.4 years, and 43.7% (52.6% men and 35.9% women) of all participants were Internet users. Most Internet users had used the Internet for more than a year, and 65.1% had used it for more than 10 years. The most common purpose of Internet use was to gather information, including medical and health information, accounting for 62.2% of all Internet users.Table p = 0.007) after adjusting for possible confounding factors, including age, sex, self-reported health, smoking, educational background, subjective economic status, and depression status (model 2). The odds ratio did not change significantly and was 1.42  even after further adjusting for social participation and living alone (model 3). The goodness of fit for the models by the likelihood ratio test were significant (P <  0.05) for each model.As Table p = 0.041) after adjusting for possible confounding factors, including age, sex, self-reported health, smoking, educational background, subjective economic status, and depression status (model 2). The odds ratio did not change and was 1.81  even after further adjusting for living alone (model 3). By contrast, Internet use was not associated with MVPA among participants with social participation . The goodness of fit for the models by the likelihood ratio test were significant (P <  0.05) for each model.Table p = 0.014) after adjusting for possible confounding factors, including age, sex, self-reported health, smoking, educational background, subjective economic status, and depression status (model 2). The odds ratio did not change much at 1.40  even after further adjusting for social participation (model 3). By contrast, Internet use was not associated with MVPA among participants living alone . The goodness of fit for the models by the likelihood ratio test were significant (P <  0.05) for each model.Next, Table This cross-sectional study showed that Internet use was associated with sufficient physical activity  among community-dwelling older adults in Japan. These associations were observed even after accounting for potentially confounding factors, such as age, sex, lifestyle, phycological factors, and socioeconomic factors. Additionally, Internet use was associated with MVPA only for participants who were not socially engaged and lived with family members. Therefore, our study highlighted that the association between the Internet and physical activity is different in socially isolated older adults.Many epidemiological studies suggest the benefit of Internet use for the health of older populations . Today, Many previous studies suggested that social participation promotes regular physical activity in older people , 13. HowSeveral prior studies have suggested that older people who live alone are less physically active than those who live with their families , 37. A pIn this study, 62.2% of the participants used the Internet to obtain information, including medical and health information. This result is similar to that of a previous study wherein more than half of the users perceived the Internet as an important source of health information . During The strengths of this study are the moderately large sample size and inclusion of people aged 65–90 years. In addition, we considered educational background and self-reported economic status. These are important determinants of Internet use, and the results remain significant even after adjusting for the confounding factors. Additionally, the response rate was acceptable (74.4%). However, this study also had several limitations. First, due to the cross-sectional design, we were unable to determine the causal nature of the association observed. Second, the measure of physical activity relied on a self-administered questionnaire, which could be susceptible to misclassification. Nevertheless, the questionnaire we used has been well-established, and many similar epidemiological studies have used it to assess physical activity , 47. FinOur data demonstrated the association between Internet use and sufficient physical activity, and this association differed depending on the social isolation of community-dwelling Japanese older adults. Future large-scale longitudinal studies are needed to determine the causal relationship between Internet use and an increase in physical activity, which may help provide public recommendations to promote physical activity among older people.


This study aimed to look at emotions perceived about the attributes, prevention, diagnosis, and treatment of infectious diseases related to coronavirus disease (COVID-19) that were widespread across the world and identify their relevance to knowledge about infectious diseases and preventative behaviors.Texts to measure emotional cognition were selected through a pre-test, and 282 people were chosen as participants based on the survey conducted for 20 days from August 19 to August 29, 2020, created with Google Forms. IBM SPSS Statistics 25.0 was used for the primary analysis, and the SNA package in R (version 4.0.2) was utilized to conduct the network analysis.It was found that universal negative emotions such as feeling “anxious” (65.5%), “afraid” (46.1%), and “scared” (32.7%) commonly appeared among most people. Also, they were found to be feeling both positive (“caring” [42.3%] and “strict” [28.2%]) and negative (“frustrating” [39.1%] and “isolated” [31.0%]) emotions about efforts to prevent and curb the spread of COVID-19. In terms of emotional cognition for the diagnosis and treatment of such diseases, “reliable” (43.3%) took the biggest ratio among the replies. The level of understanding about infectious diseases showed differences in emotional cognition, thereby affecting people’s emotions. However, no differences were found in the practice of preventative behaviors.Emotions associated with cognition in the context of pandemic infectious diseases have been found to be mixed. Furthermore, it can be seen that feelings vary depending on the degree of understanding of the infectious disease. The advancement of modern society, globalization, rapid demographic movement, changing human behaviors, and climatic and ecological changes on Earth have led to the development of symptoms and signs of new infectious diseases for which there are no vaccines or remedies, which has led them to spread widely globally –3.Korea has suffered from several new infectious respiratory diseases that spread through droplets and contacts, such as Severe Acute Respiratory Syndrome (SARS) in 2002, H1N1 in 2009, Ebola in 2014, Middle East Respiratory Syndrome-related Coronavirus (MERS-CoV) in 2015, and Coronavirus (COVID-19) in 2019. As the outbreak and spread of the diseases put Koreans in fear and threatened their safety, people began to take more interest in newly-created infectious respiratory diseases. Moreover, with the growing importance of managing and responding to respiratory-related infectious diseases, national disease control efforts entered a new phase [As infectious respiratory diseases become more prevalent, people’s concerns and fears over infection have become a complex issue that affects society as a whole, no longer being a mere medical problem. As COVID-19 continued to spread worldwide, the World Health Organization (WHO) declared it an epidemic on March 11, 2020 in consideration of its seriousness . Hence, The emotional discomfort that appeared upon the outbreak of COVID-19, such as anxiety, fear, sorrow, and anger varied among individuals. Korea has been identifying the movements of confirmed patients through epidemiological investigations and managing those subject to self-isolation when their movements overlap with confirmed patients. Confirmed and self-isolated people may experience fear of the consequences of COVID-19 infection, and patients in self-isolation may experience boredom, depression, loneliness, and anger . Liu et When there is a national crisis, such as an outbreak of COVID-19, people realize the severity of the disease through subjective judgment, which leads to behaviors to minimize and evade the impact. This includes the search for information and understanding . Jung, YMany studies report that taking emotional factors into account was necessary when it came to preventative behaviors for infectious diseases –18. AlsoHence, this study carried out text-mining, where the team first collected texts (words) exhibiting the emotional cognition of people toward COVID-19 and performed social network analysis using the graph theory to find out their relationship with emotional cognition, understanding, and preventative behaviors.Identify general characteristics of the subjects.Identify texts on emotional cognition for COVID-19 and find relevance among the texts.Identify correlation among emotional cognition, understanding, and preventative behaviors related to COVID-19.This research aimed to study emotional cognition that can affect COVID-19 and identify the relevance among words of emotional cognition and the relationship between those words and emotional cognition, understanding, and preventative behaviors related to COVID-19. The detailed objectives are as follows:This study is a descriptive research study designed to look at emotional cognition that can impact the management of COVID-19 and determine its correlation with emotional cognition, understanding, and preventative behaviors.The scope of the research was divided into three parts following the guidelines for COVID-19 announced by the KDCA in 2020 . Texts related to the three parts were selected, and closeness centrality was identified among the words by analyzing their usage frequency through the survey.This study focused on emotional cognition related to Severe Acute Respiratory Syndrome (SARS) in 2002, H1N1 in 2009, Middle East Respiratory Syndrome-related Coronavirus (MERS-CoV) in 2015, and Coronavirus diseases (COVID-19) in 2019, which are the representative coronavirus cases in Korea. Moreover, 20 most commonly used texts in thesis papers, newspapers, online news articles, and social media were extracted for the survey, and through the pre-survey of 30 adults aged 19 or older, 10 texts were finally selected. Five texts expressing feelings about COVID-19 among the selected texts were collected for each part—15 in total.This study included adults aged 19 or older, and the age distribution according to gender was similar. A total of 284 people were included in the final analysis, excluding five duplicate respondents.To measure the “emotional cognition” regarding understanding knowledge and preventative behaviors that can impact the management of infectious diseases, the subjects’ feelings about the attributes, prevention and spread control , and diagnosis and treatment of COVID-19 were studied. Considering that the response rate could be low when the survey takes an open format as perceived emotions vary from individual to individual, a pre-test was performed on major emotions. Open questions were given to 30 subjects, and 10 texts  that they most frequently replied to in areas of the diseases’ characteristics, prevention, spread control, diagnosis, and treatment were utilized for this study.The data were collected over a 20-day period through Google Forms from August 19 to August 29, 2020. The researchers explained the objective of the study to the participants and clarified that the results will not be used for other purposes and the provided information will be kept confidential. Furthermore, it was noted that there would be no disadvantage even if the participant withdrew from the study, and the decision to take part in the study was made voluntarily. Participants who indicated an intention to take part in the study filled out an online consent form and a questionnaire.IBM SPSS Statistics 25.0 was used for the frequency analysis of the participants’ characteristics as well as the characteristics of their emotions regarding prevention and spread control , and diagnosis and treatment of COVID-19. To examine the orders among the emotions felt about the characteristics, prevention, spread control diagnosis, and treatment of COVID-19, the SNA package of R (version 4.0.2) was used to perform the network analysis . The texThe majority of the participants were women (58.5%). In terms of age, 27.1%, 20.1%, 19%, and 6.7% were in their 30 s and 40 s, 20 s, 50 s, and 60 or older respectively.Regarding smoking status, which is a health behavior, "never smoked" represented the largest proportion (69.7%), which was followed by “currently smoking” 13.7%) and “smoked before but have quit” (16.5%). Regarding drinking status, “never drink” and “once a month” each accounted for 28.5%, followed by “once a week” (21.5%), “twice a week” (14.1%), and “three times or more a week” (7.4%). Furthermore, those who exercised three or more times per week accounted for 43.7%, and those who slept 6–7 h took the highest proportion of 37.7% (Table 3.7% and An average of 18.7 SD = 2.9) out of 25 questions on understanding infectious diseases were found to have been correctly answered. The items that showed the highest percentage of correct answers were “Washing hands with soap and water for 30 s can help prevent the spread of COVID-19” and “I cover my mouth with a sleeve if I don’t have a tissue or handkerchief.” Meanwhile, the items with 50% or more incorrect answers were “The incubation period for COVID-19 is 1–3 days,” “Plants are the main carriers for the transmission of COVID-19,” “You must wear a mask once you start taking medications, not beforehand when you have respiratory symptoms,” and “Certified health masks  can be used for more than 1 day,” implying that the participants’ understanding of the treatment or medical knowledge of COVID-19 was low (Table .9 out ofThe top three items for the management and practice of preventative behaviors for COVID-19 were “I turn my face away from other people when I cough” (3.71 ± 0.49), which was the highest, followed by “I avoid having close contact with people who have a fever or respiratory symptoms” (3.70 ± 0.48), and “I always wash my hands after returning home or working out to prevent infection” (3.69 ± 0.52). Meanwhile, items that the subjects least practiced were “I sleep for 7 or more hours on mean to maintain health” (2.77 ± 0.79) and “I work out on a regular basis for health, such as to boost immunity” (2.81 ± 0.80) (Table p = 0.525).In terms of the differences in practice depending on the level of understanding of COVID-19, no statistically significant difference was found, as the group with poor understanding recorded (3.31 ± 0.37) whereas the mean of the group with good understanding was 3.34 ± 0.32, “Scared” (32.7%), “Threatening” (25.4%), “Horrific” (24.6%), “Frustrating” (21.5%), “Depressed” (21.1%), “Perplexed” (16.5%), “Painful” (15.1%), and “Critical” (11.6%) , “Isolated” (31.0%), “Strict” (28.2%), “Sacrificing” (20.8%), “Miserable” (14.1%), “Tedious” (10.2%), “Doubtful” (7.7%), “Independent” (7.7%), and “Coercive” 5.3%) , “Responsible” (29.9%), “Complicated” 16.9%), “Hurtful” (16.2%), “Doubtful” (13%), “Suspicious” (11.3%), “Perilous” (8.8%), “Unruffled” (6.7%), and “Confident” (4.9%) , and “fear” (46.1%), and “scared” (32.7%). These three words were closely connected, so they were highly related to each other. These results are in the same context as previous studies that have shown that coronaviruses have caused disease and death worldwide and, relatedly, a lot of social damage and anxiety . In addiTherefore, it is important to ensure accurate information about infectious diseases; if negative emotions persist, psychological stress and tension can increase, leading to a wide range of mental illnesses, such as depression, anxiety disorder, panic disorder, remorse, post-traumatic stress disorder (PTSD), and delirium, and even suicide , 30.In this study, the group with particularly high understanding more commonly recognized that they felt negative emotions, such as "anxiety, hopelessness, and depression," while the group with a low understanding of infectious diseases was higher. Many studies have found that higher levels of education improve skills, cognition, and coping strategies, which all ultimately improve psychological health , 32.It is important to recognize that the characteristics of infectious diseases impact mental and psychological health . In partTo date, many studies have been conducted on COVID-19-related depression, anxiety, or mental stress from isolation, but there are no studies analyzing emotional recognition or preventive behaviors in relation to understandings of infectious diseases during the uncertain and tense COVID-19 era. This study is the first to consider this topic in terms of emotional recognition, and its strength is that it uses text mining to analyze the relationship and frequency between emotions.Regarding emotions related to respiratory-related infectious disease prevention, our study revealed interesting results.First, most subjects reported feeling a mix of positive and negative emotions about infectious disease prevention and quarantine. Negative emotions, such as "frustrated” (39.1%) and "isolated” (31.0%) were strongly related to each other; meanwhile, positive attitudes for prevention and quarantine, such as "considerate” (42.3%) and "thorough” (28.2%) were strongly connected locally. Interestingly, participants with a high understanding of infectious diseases was aware of their positive emotions, such as feeling "considerate” (51.7%), while participants with a low understanding of infectious diseases were more aware of their negative emotions, such as feeling "frustrated” (40.4%) and “isolated” (33.1%). These results are consistent with previous studies that report that the lower one’s level of knowledge, the easier one may persuaded by irrational beliefs, conspiracy theories, or misinformation about quarantine and fall into negative emotions . In addiTherefore, for individuals who are not aware of their negative emotions, emotional cognition is an important cognitive process during crises, such as the pandemic, because it can prevent exaggerated preventive actions or unscientific quarantine.Regarding the third area, the diagnosis and treatment of infectious diseases, similar emotions were shown in the two groups regardless of the level of understanding.The highest percentage of emotions for the diagnosis and treatment of respiratory-related infectious diseases was "reliable” (43.3%). The linked emotions were "relieved” (32.7%) and "responsible" (29.9%).Negative emotions, such as “complex” (16.9%), “sick” (13.0%), and “suspicious” (11.3%), were also linked. Interestingly, this result was the same across different levels of understanding of infectious diseases. These results may be due to the timing of the study—the early stages of the COVID-19 pandemic; specifically, the participants may have felt confident about diagnosis or treatment because the number of vaccines or and treatments for severe patients was on the rise, but they may also have felt confused about and questioned treatment, isolation, and diagnostic methods. However, compared to the initial stage, the prolonged COVID-19 situation is believed to have increased trust in medical staff and the government as the death rate and incidence rate have decreased and infections are now controlled. Indeed, confidence in medical staff and the government regarding diagnosis and treatment is crucial in a pandemic situation, as previous studies have shown that trust in government agencies has reduced COVID-19 mortality . NotablyThis confidence is important because it leads to voluntary testing, reducing the spread of infectious diseases, and securing more treatment time. Therefore, the cultivation of a feeling of trust in the treatment of infectious diseases can improve the management of infectious diseases.In this study, the practice rate of preventive actions according to the level of understanding of infectious diseases was analyzed, but no statistically significant difference was found. Although educational intervention is generally known to be effective in improving an individual’s knowledge, attitude, and behavior, its effect on information provision is uncertain due to the nature of unpredictable and threatening new situations, such as COVID 19. This result aligns with that of study that reported that while such a situation may involve some behavioral changes, it can be difficult to continuously practice practice preventive actions . It is aTherefore, since our study did not find any difference in the practice of preventive actions depending on the level of understanding of infectious diseases, it suggests that information provision itself is more meaningful than the amount or level of information in the early stages of the pandemic. However, as the pandemic has continued for more than three years, and accurate information about the virus and preventive activities caused by vaccines are active, preventive actions are now considered to differ across levels of understanding, so further research is needed. Previous studies have also shown that the more knowledge an individual has about the characteristics, symptoms, and treatment of COVID-19, the less likely they are to have problems with their mental health . TherefoIn summary, this study revealed that a relationship between emotion and understanding during the COVID-19 pandemic—individuals with high levels of COVID-19 related knowledge tended to feel general negative emotions, while individuals with low levels of COVID-19 related knowledge tended to feel fear or extreme fear; in addition, it also found that perceptions of prevention behavior differed depending on level of understanding.This study’s findings are meaningful for managing mental and psychological health during the COVID-19 pandemic and also have a practical impact for the end of the pandemic. First, these findings suggest that effective communication strategies should be developed to check existing levels of understanding of diseases to improve personal quarantine during public health crises. Second, this study found that a lack of knowledge about COVID-19 reduces trust and yields negative emotions. In addition, it also found that people with negative emotions were more likely to demonstrate excessive or unscientific preventive actions. These findings suggest that it is necessary to decrease negative emotions, prevent unnecessary information, and alleviate fear.There are some limitations to this study. First, the causal relationship between understanding and emotion is uncertain when cross-sectional information is used.Second, to enhance emotional cognition, the study suggests including non-verbal elements, such as body gestures, paralanguage , and physical changes . As emotional cognition can be expressed either verbally or non-verbally, further research should be done on non-verbal emotional cognition.Third, the expressions of emotional cognition used in this study may differ across countries and cultures; further research needs to consider cultural contexts.


Escherichia coli clones isolated from populations evolving in response to 1000 days of different transfer volumes and resource-replenishment intervals. The evolution of mutation rates proceeded rapidly in response to demographic and/or environmental changes, with substantial bidirectional shifts observed as early as 59 generations. These results highlight the remarkable rapidity by which mutation rates are shaped in asexual lineages in response to environmental and population-genetic forces, and are broadly consistent with the drift-barrier hypothesis for the evolution of mutation rates, while also highlighting situations in which mutator genotypes may be promoted by positive selection.Ecological and demographic factors can significantly shape the evolution of microbial populations both directly and indirectly, as when changes in the effective population size affect the efficiency of natural selection on the mutation rate. However, it remains unclear how rapidly the mutation-rate responds evolutionarily to the entanglement of ecological and population-genetic factors over time. Here, we directly assess the mutation rate and spectrum of  E. coli proceeds rapidly in response to these factors with substantial bidirectional shifts.How rapidly the mutation rate responds evolutionarily to ecological and population-genetic factors over time is unclear. Here, the authors show that the evolution of mutation rates in  Furthermore, hypermutator alleles, or mutations that increase spontaneous mutation rates, can reach fixation in microbial populations evolved under stressful environments. For example, in glucose-limited media, Escherichia coli can evolve a higher mutation rate within ~2400 generations11.As organisms frequently encounter environmental changes, and mutation is the ultimate source of evolutionary material, studying the response of the mutation rate to population settings helps us better understand the fate and dynamics of populations in novel environments. Mutation rates are plastic and evolvable. For example, mutation rates of bacteria have been found to be plastically elevated in several different stressful environments, such as extreme starvation, exposure to antibiotics, and high population densities15. Alternatively, the evolution of elevated mutation rates might result from shrinkage of the effective population size (Ne) under a stressful environment17. Lower mutation rates are expected to be commonly favoured by natural selection due to the prevalence of deleterious mutations. As the power of natural selection to reduce mutation rates is constrained by the magnitude of genetic drift defined by Ne, populations with reduced Ne are expected to be unable to resist the accumulation of mutations mildly deleterious to replication fidelity19.Different theories have been proposed to explain the evolution of elevated mutation rates in stressful environments. One idea is that hypermutator alleles can hitchhike with beneficial mutations and reach fixation within a population adapting to a novel environment21. However, empirical studies on mutation-rate evolution in complex environments are lacking.Although prior studies on mutation-rate evolution tend to focus on homogeneous environments, natural environments tend to be spatially and temporally heterogeneous, as with fluctuating supplies of nutrients. A few theoretical studies considering heterogeneous environments suggest that the level of heterogeneity can affect mutation-rate evolution. For example, hypermutators are expected in theory to be the most prevalent in environments where conditions fluctuate at intermediate frequencies, leading to a non-monotonic relationship between mutation-rate evolution and environmental variationE. coli lines as a model system to understand the ecological effects of fluctuating resource availability on the dynamics of mutation-rate evolution. Specifically, the experimental evolution of E. coli was performed in rich media with three different resource-replenishing cycles  and S1 . The schemes L1, M1, and S1 allow comparison across different Ne . For each of the five transfer schemes, to further determine how initial mutational features affect subsequent mutation-rate evolution, within each transfer scheme, two different starting genetic backgrounds were examined: a wild-type (WT) strain, and a strain with an inactivated mismatch-repair (MMR-) pathway. With two replicate populations for each genetic background and transfer-scheme combination (2 × 2 × 5 = 20), and two additional WT L10 populations  rates in ancestral WT and MMR- progenitors are respectively 3.5 × 10ion Fig. . In addiion Fig. . These mP = 4.4 × 10−44) and 77.3-fold (P = 2.5 × 10−47) of the WT ancestral value, respectively. The mean structural variation mutation  rate for L10 WT clones is significantly increased by 1.7-fold (P = 4.3 × 10−5) of the ancestral value. Note that the clone L10-D1 shows a very different mutation rate compared to the other clone from the same population, L10-D2. Such intra-populational variation of mutation rates can arise from the recurrent input of mutation-rate variants during clonal evolution. For the MMR- background, clones from the L10 populations also generally show significant increases in SNM rates  and SVM rates  relative to the ancestral value , we found that the most extreme evolutionary changes in mutation rates were found in clones isolated from populations cultivated in L10. For the WT background, seven out of eight clones show apparently much higher mutation rates than the ancestor WT clones. Overall, the mean SNM and SIM rate significantly increased to be 121.4-fold  did not show highly increased mutation rates, and in the case of an MMR- background, they generally experienced reductions in their mutation rates after 1000 days of evolution. For example, the clones isolated from S1 populations show a significant reduction in the SNM rate by 41.6%  as candidate hypermutator alleles, including two independent cases of a 6-bp deletion that converted a triplet LALALA motif in MutL to a duplet LALA (populations L10-C and L10-E), a 17-bp deletion in the coding region of MutS (population L10-D), and a nonsynonymous substitution in MutS (population L10-F). Their earliest detected times are particularly short: only 99, 76, 59, and 99 generations for populations L10-C to F, respectively .Except for the mutation-rate evolution in the intermediately fluctuating environment, many of our results are consistent with expectations of the drift-barrier hypothesis. First, mutation-rate evolved to be higher in smaller WT populations (S1 and M1) and to be lower in MMR- populations, consistent with the prediction that the drift-barrier of mutation rates is prominently influenced by demography. Second, as the selection against hypermutator alleles is not immediate, but only takes place after sufficient time has elapsed for the development of a linked load of deleterious mutations, a substantial wandering  of mutation rate over evolutionary time is predicted, with variance maintained within populations as well as over generations. As expected, the evolved clones show relatively high levels of within-population variation , and parallel populations can evolve to different paths but the same direction on mutation-rate evolution  are descendants of PFM5 24. The long-term cultures consist of three transfer-size treatments, differing by 1 mL , 1uL  and 1nL  daily transfer and maintained in 10 mL LB broth shaking at 175 rpm at 37 °C. In addition, the large transfer-size treatment was run with 10- (L10) and 100-day (L100) replenishment cycles to yield different starvation regimes. At each transfer condition, WT and MMR- were both applied with four parallel evolution experiments, generating 5 × 2 × 4 = 40 populations. To maintain and trace the historical record of populations, 1 mL of copies of cultures were collected at days 0, 90 (or 100 for 100-day populations), 200, and then every 100 days, frozen with liquid nitrogen, and stored at −80 °C for sequencing. In addition, we froze samples every 20, 30, or 40 days for 1-day and 10-day populations until day 400 to revive populations in the case of contamination or extinction. Specifically, to avoid a case of a whole population extinction in 100-day populations, we maintained 100-day populations in triplicate (1 main culture and 2 backup cultures) with only the main culture being used to seed new triplicate cultures every 100 days. One backup culture would replace the main culture if it extincted and the experiment would continue. In this study, whole-population extinction events only occurred during the first 200-days. During the experimental evolution, L10 and L100 populations did show smaller population sizes before the transfer than L1 populations, but the difference is small22.All populations originated from two ancestral strains with different initial mutation-rates. Ancestral wild-type (WT) are descendants of PFM2, a prototrophic derivative of 10 per tube. Theoretically, the effective population size is the harmonic mean of fluctuated census population sizes45. Therefore, for L1 populations, the dilution factor = 10−1, Ne is about−4, and Ne is about 7.5 × 106. For S1 populations, the dilution factor = 10−7, and Ne is ∼1.3 × 103.During the experimental evolution, we observed that the carrying capacities of the populations are always ∼1022, and sequenced as paired-end reads to a target depth of 100× on an Illumina HiSeq 2500 or an Illumina NextSeq 500. Available sequencing profiles were identified using Breseq46 from trimmomatic47 trimmed reads. For each population, at each sample point, we quantified the genomic divergence by summation of derived allele frequencies of all detected SNM rates. Then, we performed linear regression by the function “lm” in R with formula “genomic divergence ~ guaranteed generations + 0” for estimating the rate of genomic evolution. The estimated generations per day are 3.3, 13.3, 23.3, 0.33, and 0.25 for L1, M1, S1, L10, and L100 populations, respectively25.We extracted DNA of long-term evolved populations from cultures every 100 days until day 90024. The offspring of three clones  grew so slowly that transfers were done every other day and had to be terminated early .We labelled MMR- parallel populations as A or B, and WT populations as C, D, E or F. After 1000 days of evolution, each combination of transfer conditions and initial mutation rate was assessed in quadruplicate with two clones assessed from each of two representative populations, except for WT L10, where two additional populations were also investigated because they exhibited extremely high evolutionary rates and fluctuation tests suggested these populations may have evolved uniquely high mutation rates  per passage were estimated by CFU counts of dissolved and diluted colonies of focal plates as follow:G0, G30 and GN are number of generations respectively estimated at days 0, 30, and the last day of the entire MA experiment. Typically G ranges from 18 to 24. N is number of days of the entire MA experiment.The number of generations  after propagation for an average of ∼1500 generations.Genomic DNA samples of MA ancestral clones and MA lines were extracted using the Wizard DNeasy UltraClean Microbial Kit (Qiagen), Wizard Genomic DNA Purification Kit (Promega), or the MagMax DNA Multisample Ultra 2.0 Kit (Applied Biosystems). Fresh DNA from MA lines or progenitor clones was submitted to the Beijing Genomics Institution (BGI-Hongkong) and sequenced on a DNBseq-G400 platform.47, we removed residual adapters and trimmed low quality sequences. Cleaned reads were then mapped to the reference genome of E. coli K-12 str. MG1655 (NC_000913.3) with BWA48. Only paired reads were allowed to map. After the mapping, 15 MA lines were discarded from the following analysis because of their low depths (<50×) or low coverages (<30%), which are considered as contaminated lines during the MA experiments. In the end, on average, 99.6% of genomic positions were covered by the high quality reads (Q30), displaying an average depth of 149×.Using trimmomatic49. Base quality scores were corrected by corresponding MA progenitor. The candidate variants with read depths <4 or phred scores <10 were discarded. Ancestral variants were also discarded; the ancestral variants were identified from the differences between the reference genome and the MA progenitors, which are expected to be supported by at least four reads with phred scores higher than ten. In addition, only the indels <= 4 bp (SIM) were retained. The variants that were consensus among MA lines were also discarded; consensus SNM calls (or SIM) were identified if present in more than two (or four) MA lines with the same MA ancestor. To detect structural variations (SVs), including deletions, duplications, inversions, and insertions, we used CNVnator50, Lumpy51 and BreakDancer52. The redundant predictions were discarded. Among the maximum 500 bp upstream and downstream extended regions of the predicted SVs, we first removed the redundant predictions of CNVnator from the Lumpy predictions. We then removed the redundant predictions of Breakdancer if they are present in the Lumpy/CNVnator predictions. For each MA experiment, we removed SVs that were either called in the MA ancestral clone or found in at least three parallel MA lines, allowing a maximum 500 bp extension or substraction at both ends.We then removed duplicated reads from filtered alignments, and called candidate SNM and SIM using GATK4u denotes the mutation rate, m represents the number of SNM or SIM observed, G is the total number of generations elapsed per lineage, and n is the number of ancestral sites covered by sequencing reads with high quality.SNM and SIM rates (per genomic site per generation) were calculated for each MA line by the equation:m represents the observed SV numbers.SVM rates (per genome per generation) were calculated for each MA line by the equation:Z-score > 2.5:u is the mutation rate of a MA line, U and S are the mean and standard deviation of mutation rates for all lines related to the same MA ancestor. We also adopted this cutoff for filtering odd SIM and SVM rates in analysis. All filtered mutations in MA experiment are listed in Supplementary Data Before analyzing SNM rates, we removed several MA lines with odd rates using a cutoff of absolute http://geneontology.org/). The temporal trajectories of allele frequencies for these putative mutations were directly from the detected mutations of previous longitudinal sequencing data following previous methodology . Here, we focused on genes involving DNA replication and repair processes, including GO-terms of “0006298,” “0006281,” “0006260,” “0006285,” “0006284,” “0006307,” “0000731,” “0007059,” “0051103,” “0006310,” “0009432,” “0045004,” “0006261,” and “0006271” . The targeted PCR products are 900 and 700 bp for mutS and mutL, respectively. Below are the primer sequences used.To validate mutational trajectories of candidate hypermutators in mutS-F: 5’-TCTGGCACGTCTGGCTTTAC-3’;mutS-R: 5’-AGATGCGATCGATAGGTCCAA-3’;mutL-F: 5’-TCAGGTCTTACCGCCACAAC-3’;mutL-R: 5’-GCCAGCGCTTGTTCAAGAAA-3’.mutS primers and 68 °C for the mutL primers. At the end, a final extension was performed 2 min at 72 °C. PCR products were then purified and collected by GeneJET PCR Purification Kit (Thermo Fisher Scientific). Purified DNAs were submitted to the KED Genomics Core at Arizona State University for Sanger sequencing.To estimate the allele frequency of candidate hypermutators during the experimental evolution, we streaked frozen stocks for WT-L10-C, D, E, and F onto LB plates and cultured overnight at 37 °C. We used frozen samples collected at days 0, 90, 180, 200, 230, 300 and then every 100 days. To prepare samples for PCR, we picked 20 single colonies from each stock randomly and then transferred each of them to a 1.5 mL tube containing 0.6 mL of LB broth for overnight culture. Following overnight incubation, 1 uL of each revived colony was taken to perform PCR using Q5 High-Fidelity 2X Master Mix (NEB). The PCR conditions were starting with 1 min at 98 °C for denaturation, then subjected to 30 cycles of amplification . The annealing temperatures are 66 °C for the By testing PCR products of 20 single colonies from each frozen stock, we found that several colonies from as early as day 300 of WT-L10-C and WT-L10-F, day 230 of WT-L10-D, and day 180 of WT-L10-E contain hypermutators. Thus, the occurrence times of hypermutator candidates were estimated to be between days 230–300, 200–230, 90–180, and 230–300 for WT-L10-C, D, E, and F, respectively.53. To study whether the candidate hypermutators affect mutation rates, we used the fluctuation test with rifampicin resistance for clones isolated from L10-C, D, E and F at days 90, 200, 300, and 400, as well as the MA ancestor. Whether the clones with or without the candidate was examined by PCR as mentioned above. Three independent clones with the hypermutator and/or three independent clones without the hypermutator were used for each L10 population at each time-point.Luria-Delbrück fluctuation tests can be used for the determination of the spontaneous mutation rate using a known molecular marker, such as antibiotic resistance7 dilution) and grown for 24–48 h in a 96-well culture plate containing 1 mL of LB broth per well. Four were diluted and spread on LB agar for total cell counts; 40 were plated on LB + Rifampin agar (100 mg/L) for resistance selection. The number of resistant mutants in CFU/mL were converted to an estimated mutation rate by the “newton.LD” function in the R package “rSalvador”54.During the fluctuation test, each targeted clone was first overnight cultured, and 44 replicates of the targeted clones were then transferred , we also reported the mutation contexts with 96 classes constituted by six SNM classes, plus the flanking 5’ and 3’ A, G, C, and T bases. To further extract orthogonal mutation patterns from the evolved populations, we constructed mutation tables with 96-class mutation contexts and identified the “mutation patterns (MPs)” using the non-negative matrix factorization (NMF) function in the R package “MutationalPatterns”Bacillus subtilis NCIB 361056 (NZ_CM000488.1), Corynebacterium glutamicum ATCC 1303257 (NC_003450.3), Deinococcus radiodurans R159 , Mycobacterium smegmatis MC2 15560 (NC_018289.1), Pseudomonas fluorescens SBW2559 (NC_012660.1), Vibrio Cholerae 2740-8061  and N1696162 , and Vibrio Fischeri ES11461 . All bacterial genomes were downloaded from NCBI and used to estimate 96-class spectra. The MMR-defect mutation spectrum of Caenorhabditis elegans was obtained from ref .63. The mutation data for Arabidopsis thaliana MMR- lines were obtained from ref. 64, and we downloaded the reference genome from The Arabidopsis Information Resource (https://www.arabidopsis.org/) to estimate 96-class spectra.We obtained mutations from several MMR deficiency related MA studies, including https://cancer.sanger.ac.uk/cosmic/signatures), seven of which are associated with defective DNA mismatch repair: SBS6, SBS14, SBS15, SBS20, SBS21, SBS26, and SBS4428. These mutation patterns are involved in 67, 33, 220, 83, 13, 37, and 246 cancer genomes across 13, 4, 12, 7, 6, 11, and 11 cancer types, respectively.A number of Single Base Substitution (SBS) patterns extracted from human cancer samples were obtained from COSMIC (Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


For the conditions, neuropathic pain/peripheral neuropathy had the highest rate of perceived benefit (66.6%), followed by Parkinson’s disease (60.9%), multiple sclerosis (60.0%), migraine (43.8%), chronic pain syndrome (42.1%), and spondylosis (40.0%). For the indications, medicinal cannabis had the greatest perceived effect on sleep (80.0%), followed by pain (51.5%), and muscle spasm (50%). Oral oil preparations of balanced delta-9-tetrahydrocannabinol/cannabidiol  were mainly prescribed. Somnolence was the most frequently reported side effect (21%). This study supports medicinal cannabis’ potential to safely treat non-cancer chronic conditions and indications.Research describing patients using medicinal cannabis and its effectiveness is lacking. We aimed to describe adults with non-cancer diagnoses who are prescribed medicinal cannabis via a retrospective medical record review and assess its effectiveness and safety. From 157 Australian records, most were female . Most patients had neurological (58.0%) or musculoskeletal (24.8%) conditions. Medicinal cannabis was perceived beneficial by 53.5% of patients. Mixed-effects modelling and post hoc multiple comparisons analysis showed significant changes overtime for pain, bowel problems, fatigue, difficulty sleeping, mood, quality of life (all  Cannabis (Cannabaceae) has been used medicinally since 400 AD for its analgesic, appetite enhancement, and myorelaxant properties ,2,3. EmeIn Australia, impressive public support instigated legislative changes in 2016 allowing medical doctors to prescribe cannabis-based medicines for chronically ill patients subject to the approval from the Therapeutic Goods Administration  which isRapidly increasing public interest in medicinal cannabis necessitates research into the conditions/symptoms it can potentially treat, and its safety profile and this has prompted the Australian government to fund clinical trials to ascertain its place in mainstream medicine . Whilst This retrospective study will aim to answer the questions: (1) What are the characteristics of people with non-cancer diagnoses using medicinal cannabis? and (2) Does medicinal cannabis provide effective relief for patient conditions and symptoms? Secondary objectives include determining dosage, side effects, and reasons for cessation of treatment.A retrospective medical record review was conducted at Wolper Jewish Hospital in Sydney, Australia, which is a stand-alone, not-for-profit clinic specializing in rehabilitation and palliative care. A consent form was provided which talks about the risk of psychosis and schizophrenia. Medical records of patients prescribed medicinal cannabis between 1 February 2018 and 30 November 2021 were reviewed using the inclusion criteria: (1) 18 years or older; and (2) non-cancer diagnosis. This study was approved by the St Vincent’s Hospital Sydney Human Research Ethics Committee .Patient demographic data , medical data (condition(s), primary and secondary indication(s)), administration route, cannabinoid ratio, dose, number of cannabis-based medicine(s), and previous cannabis use were recorded. Side effects, treatment continuation, and reasons for cessation were quantitatively described.The effectiveness of medicinal cannabis treatment was examined by the following: (1) The patient’s own perceived benefit reported as ‘beneficial’, ‘not beneficial’ or ‘unclear’; this data was used as a proxy to determine overall and condition-specific perceived benefit. (2) The Palliative Care Outcomes Collaboration (PCOC) Symptom Assessment Scale (SAS) scores. This validated tool reliably measures patient-reported symptom distress amongst those with advanced disease and includes pain, fatigue, breathing problems, bowel problems, appetite, nausea, difficulty sleeping, and mood. Each symptom is rated out of 10 . Scores 0–3 depict mild distress, 4–7 moderate distress, and 8–10 severe distress . Patientt-test compared the highest doses of cannabidiol (CBD) and delta-9-tetrahydrocannabinol (Δ-9-THC). Statistical significance was set at p < 0.05. Statistical tests were performed using GraphPad Prism .De-identified patient data were entered into an Excel spreadsheet and analyzed descriptively. To account for missing data, patient scores for each SAS symptom were grouped into baseline and three-month blocks, averaged, and analyzed by fitting a mixed-effects model . Post hoTwo-hundred-and-seven patients met the study inclusion criteria. Fifty patients were excluded due to missing files/poor data, resulting in 157 patients analyzed.Patients were predominantly female (63.7%), aged 63.0 years, and from the Greater Sydney region (86.6%). Most patients were born in Australia (66.9%) and partnered (57.3%), with 17.8% stating previous cannabis use .Patients reported an array of conditions  and indip < 0.0001) than Δ-9-THC (16.9 ± 17.23 mg/day) , an oral oil CBM (99.4%) with balanced Δ-9-THC and CBD (84.7%). The mean highest CBD dose (34.8 ± 48.04 mg/day) was significantly higher ( mg/day) .n = 75) compared to those aged ≥65   perceived medicinal cannabis as being ‘beneficial’ regardless of their condition/indication a. Patien n = 82) b.The conditions and indications with ≥10 patients were analyzed further. For the conditions, neuropathic pain/peripheral neuropathy had the highest rate of perceived benefit (66.6%), followed by Parkinson’s disease (60.9%), multiple sclerosis (60.0%), migraine (43.8%), chronic pain syndrome (CPS) (42.1%), and spondylosis (40.0%) c. For thIn patients with spondylosis, improved trends were observed for ‘fatigue’, ‘difficulty sleeping’, ‘mood’, and ‘QoL’ scores to 6 months; and ‘pain’ and ‘bowel problems’ to 9 months a. Mixed-In patients with Parkinson’s disease, improved trends were observed for scores to 3 months (‘QoL’ to 6 months), with general stabilization over time b. Mixed-In patients with chronic pain syndrome, improved trends were observed for the scores to 3 months which generally continued to 9 months c. Mixed-In patients with neuropathic pain/peripheral neuropathy, improved trends were observed for ‘fatigue’ and ‘difficulty sleeping’ scores to 3 months; ‘pain’ and ‘QoL’ to 6 months; ‘bowel problems’ to 9 months, and mood to 12 months d. Mixed-In patients with migraine, improved trends were observed for ‘mood’ to 3 months; ‘fatigue’, ‘bowel problems’, ‘difficulty sleeping’, and ‘QoL’ scores to 6 months, and ‘pain’ to 18 months e. Mixed-There are downward trends for ‘pain’  and ‘bowel problems’  scores to 12 months, and ‘fatigue’, ‘difficulty sleeping’, and ‘mood’  to 15 months, with improvement in ‘QoL’ to 6 months in patients with multiple sclerosis f. A WilcIn patients with pain, improved trends were observed for all symptom scores to 3 months (‘QoL’ to 6 months) with general stabilization over time a. Mixed-In patients with muscle spasm, initial improved trends were observed for all symptom scores to 3 months; ‘pain’, ‘fatigue’, and ‘bowel problems’ to 12 months; and ‘difficulty sleeping’, ‘QoL’, and ‘mood’ to 15 months b. Mixed-In patients with sleep issues, improved trends were observed for ‘fatigue’, ‘bowel problems’, and ‘mood’ scores to 3 months; ‘difficulty sleeping’ to 6 months; and ‘pain’ and ‘QoL’ scores  to 9 months c. Wilcoxn = 157) were somnolence (21.0%), dry mouth (7.6%), disorientation/confusion (3.8%), intoxication (3.8%), constipation (2.5%), and dizziness (2.5%)  a. Unstean = 157). Thirty-nine reasons were reported for treatment cessation, with the main being inefficacy (51.3%), followed by side effects (25.6%), and relief from other medications/treatment (12.8%)  b. One paThis retrospective medical record review describes the population characteristics of patients using medicinal cannabis at a clinic in Sydney, Australia and provides data on the effectiveness and safety of medicinal cannabis treatment on patient conditions and indications. In our study, the mean age of patients was higher than in international studies (40–50 years) ,17,18,19It difficult to ascertain the actual numbers of patients that had used cannabis before attending the clinic as patients are under no obligation to state previous cannabis use. However, the results here are similar to another Australian study which showed that 14.1% had not used cannabis before using it for medical reasons .In this study, most patients were prescribed oral oil cannabis-based medicines of balanced Δ-9-THC:CBD ratios reflecting their availability for prescription in Australia . This coLiterature shows that therapeutic doses of Δ-9-THC (5–20 mg) tend to be smaller than CBD (50–1500 mg)  and our Most patients in our study sought treatment for neurological and musculoskeletal conditions, with pain as the main indication. This agrees with data indicating that most approvals in Australia are for chronic pain, and pain ranks as the number one indication in several studies ,14,16,21The presence of symptoms such as pain, fatigue, sleep difficulties, and poor mood in this study indicates high symptom burden across these chronic diseases ,28. For When comparing age groups, 56% of patients under <65 years found medicinal cannabis to be beneficial compared to 51.2% of patients aged ≥65. A study of patients with cancer indicated that younger age was associated with the therapeutic success of medicinal cannabis, which could indicate more tolerance to higher doses, and this may be reflected in the present study .p < 0.001), as well as significant improvements in QoL (p < 0.05) [In terms of patient-perceived benefit, the results suggest that medicinal cannabis is more effective at treating neuropathic pain conditions compared to nociceptive pain conditions such as spondylosis , findings congruent with available evidence on different pain conditions ,30,31,32 < 0.05) . These r < 0.05) ,16. With < 0.05) .Medicinal cannabis was beneficial to around 60% of patients with Parkinson’s disease. A systematic review of the effects of cannabis on Parkinson’s disease, including RCTs and non-RCTs, reported improvements in sleep and pain, which agree with our findings . p < 0.0001) [p < 0.001) [For migraine, our study revealed that under half of the patients believed treatment to be beneficial, with significant SAS scores for ‘fatigue’ and a downward trend for ‘difficulty sleeping’. One retrospective chart review analyzing migraine showed that 85.1% of patients reported a decrease in mean migraine headache frequency from 10.4 to 4.6 at follow-up ( 0.0001) . Similar< 0.001) . Another< 0.001) . ®  effectiveness on multiple sclerosis pain and spasticity, with improvements in sleep and QoL [Symptoms such as fatigue, pain, sleep, and mood disorders are also prevalent in multiple sclerosis . In this and QoL . Another and QoL , which aPoor sleep is an important symptom in chronic disease as it coincides with poor symptom control and impacts QoL . Many stMedicinal cannabis was generally safe as shown by the follow-up SAS scores to 12 months for many of the conditions/indication. Moreover, dosing was tailored to each patient  by the prescribing medical doctor, thus minimizing dose-related toxicities . A scopiThe strength of this study is that patient data up to 30 months can be assessed which is longer than prospective and clinical trials ,15. AlthOne limitation relates to the location of the study (undertaken at one clinic) which can introduce locality bias, and retrospective design of the study which limits the ability to evaluate the causality of medicinal cannabis use and improvements in symptom scores. Confounding factors may have influenced patient SAS scores. For example, a patient with spondylosis may have a higher score for their pain distress due to another developing issue. Whilst the PCOC SAS can accurately and reliably measure symptom distress in a palliative population, it lacks specific measures for many condition-specific indications e.g., mobility for Parkinson’s disease , number This study indicates that medicinal cannabis, in a balanced formulation, may address a variety of non-cancer conditions and indications concurrently and can be safely prescribed by a medical doctor. The results can be used to guide future clinical trials which could investigate the conditions and indications identified here as sensitive to treatment. It is recommended that ‘pain’ and ‘difficulty sleeping’ (which may improve fatigue) be investigated further as treating these two symptoms may have reciprocating effects on each other, on mood, and QoL. Future studies should address how cannabinoids interact in the body and with one another in differing ratios and formulations, and include relevant patient-reported outcome measures and longer-term safety studies. It is recommended that patients are assessed individually to determine whether medicinal cannabis is an appropriate treatment option, considering the associated safety risks to potential therapeutic benefit. Understanding how medicinal cannabis can be used in mainstream medicine is crucial as it has the potential to positively impact millions of lives around the world.


The global burden of lower respiratory infections (LRIs) and corresponding risk factors in children older than 5 years and adults has not been studied as comprehensively as it has been in children younger than 5 years. We assessed the burden and trends of LRIs and risk factors across all age groups by sex, for 204 countries and territories.In this analysis of data for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we used clinician-diagnosed pneumonia or bronchiolitis as our case definition for LRIs. We included International Classification of Diseases 9th edition codes 079.6, 466–469, 470.0, 480–482.8, 483.0–483.9, 484.1–484.2, 484.6–484.7, and 487–489 and International Classification of Diseases 10th edition codes A48.1, A70, B97.4–B97.6, J09–J15.8, J16–J16.9, J20–J21.9, J91.0, P23.0–P23.4, and U04–U04.9. We used the Cause of Death Ensemble modelling strategy to analyse 23 109 site-years of vital registration data, 825 site-years of sample vital registration data, 1766 site-years of verbal autopsy data, and 681 site-years of mortality surveillance data. We used DisMod-MR 2.1, a Bayesian meta-regression tool, to analyse age–sex-specific incidence and prevalence data identified via systematic reviews of the literature, population-based survey data, and claims and inpatient data. Additionally, we estimated age–sex-specific LRI mortality that is attributable to the independent effects of 14 risk factors.Globally, in 2019, we estimated that there were 257 million  LRI incident episodes in males and 232 million (217–248) in females. In the same year, LRIs accounted for 1·30 million (95% UI 1·18–1·42) male deaths and 1·20 million (1·07–1·33) female deaths. Age-standardised incidence and mortality rates were 1·17 times (95% UI 1·16–1·18) and 1·31 times (95% UI 1·23–1·41) greater in males than in females in 2019. Between 1990 and 2019, LRI incidence and mortality rates declined at different rates across age groups and an increase in LRI episodes and deaths was estimated among all adult age groups, with males aged 70 years and older having the highest increase in LRI episodes (126·0% [95% UI 121·4–131·1]) and deaths (100·0% [83·4–115·9]). During the same period, LRI episodes and deaths in children younger than 15 years were estimated to have decreased, and the greatest decline was observed for LRI deaths in males younger than 5 years (–70·7% [–77·2 to –61·8]). The leading risk factors for LRI mortality varied across age groups and sex. More than half of global LRI deaths in children younger than 5 years were attributable to child wasting , and more than a quarter of LRI deaths among those aged 5–14 years were attributable to household air pollution . PAFs of male LRI deaths attributed to smoking were 20·4% (95% UI 15·4–25·2) in those aged 15–49 years, 30·5% (24·1–36·9) in those aged 50–69 years, and 21·9% (16·8–27·3) in those aged 70 years and older. PAFs of female LRI deaths attributed to household air pollution were 21·1% (95% UI 14·5–27·9) in those aged 15–49 years and 18·2% (12·5–24·5) in those aged 50–69 years. For females aged 70 years and older, the leading risk factor, ambient particulate matter, was responsible for 11·7% (95% UI 8·2–15·8) of LRI deaths.The patterns and progress in reducing the burden of LRIs and key risk factors for mortality varied across age groups and sexes. The progress seen in children younger than 5 years was clearly a result of targeted interventions, such as vaccination and reduction of exposure to risk factors. Similar interventions for other age groups could contribute to the achievement of multiple Sustainable Development Goals targets, including promoting wellbeing at all ages and reducing health inequalities. Interventions, including addressing risk factors such as child wasting, smoking, ambient particulate matter pollution, and household air pollution, would prevent deaths and reduce health disparities.Bill & Melinda Gates Foundation. Evidence before this studyThe burden of lower respiratory infections (LRI) among children younger than 5 years has been studied extensively by several groups, including the WHO Maternal and Child Epidemiology Estimation group and the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). We searched PubMed for the terms (“lower respiratory infection*”OR “LRI”)AND (“burden” OR “estimates”) AND (“age” OR “sex” OR “gender”) AND (“differenc*” OR “discrepan*” OR “disparit*”), with no language restrictions, for publications from Jan 1, 1980, to July 22, 2022. Our search identified 21 studies that reported population-based LRI morbidity and mortality estimates. Of these studies, 15 focused on either a single location or a subset of countries or regions, and six studies reported the LRI estimates at the global level. None of those studies reported the burden of LRIs attributable to risk factors for people older than 5 years by age and sex. We also did not find any studies reporting risk-deleted LRI mortality estimates. GBD 2017 estimated 2·56 million  LRI deaths among all ages and 0·80 million (0·75–0·87) LRI deaths in children younger than 5 years in 2017. The GBD 2017 LRI paper evaluated the risk factors and interventions that have affected the burden of LRIs among children younger than 5 years in 195 countries and territories.Added value of this studyGBD 2019 included new data sources on LRI mortality and morbidity and used an enhanced standardised approach to adjust data from different sources (using different case definitions or measurement methods) to improve the comparability of data. We assessed the LRI burden for all age groups by sex for 204 countries and territories. We also assessed, for the first time, the burden of LRIs attributable to risk factors for children aged 5–14 years, as well as different adult age groups. Lastly, for the first time, we provided the risk-deleted mortality estimates that represent the LRI mortality rates that would have been observed if the combined effects of all evaluated risk factors were removed.Implications of all the available evidenceOur study provides a comprehensive assessment of the LRI burden and risk factors across different age groups by sex. We identify the regions, countries, and age–sex groups with the highest LRI incidence and mortality to inform targeted interventions. By analysing the LRI burden by time, and identifying the leading risk factors by age groups separately for males and females, we provide insight into policy planning and resource prioritisation for addressing the uneven progress in reducing the LRI burden.Streptococcus pneumoniae and Haemophilus influenzae type b and viruses such as influenza and respiratory syncytial virus, are a leading cause of death globally, killing more than 2 million people every year.Lower respiratory infections (LRIs), mainly caused by bacteria such as Although measuring the burden of LRIs is a crucial input in policy decision making, the assessment of modifiable risk factors for LRIs can inform preventive interventions. With the ageing of populations, it is increasingly important to assess LRI risk factors, especially those for which exposure is not declining, such as ambient particulate matter air pollution, and compare them to risk factors for which exposure is decreasing, such as household air pollution.Global Health Data Exchange.Detailed methods for GBD 2019 have been published elsewhere.We used clinician-diagnosed pneumonia or bronchiolitis as our case definition for LRIs. We included International Classification of Diseases 9th edition codes 079.6, 466–469, 470.0, 480–482.8, 483.0–483.9, 484.1–484.2, 484.6–484.7, and 487–489, and International Classification of Diseases 10th edition codes A48.1, A70, B97.4–B97.6, J09–J15.8, J16–J16.9, J20–J21.9, J91.0, P23.0–P23.4, and U04–U04.9 .online data visualisation tool.The GBD Cause of Death database collates all available data from vital registration systems, surveillance systems, and verbal autopsy studies. Input data for LRI mortality estimation included 23 109 site-years (the number of years for which data are available for a particular location) of vital registration data, 825 site-years of sample vital registration data , 1766 site-years of verbal autopsy data, and 681 site-years of mortality surveillance data. Country-specific data sources and citations are available on the Global Health Data Exchange. Vital registration data were adjusted for completeness and garbage coding.We used the Cause of Death Ensemble modelling (CODEm) strategyTo estimate age–sex-specific incidence and prevalence of LRIs, we used data identified via systematic reviews of the literature. Additionally, we used population-based survey data, claims data, and inpatient data to estimate incidence and prevalence .Our inclusion criteria for scientific literature included a study duration of at least 1 year to avoid bias in the seasonal timing of LRIs and a sample size of at least 100 people (the sample size threshold was chosen arbitrarily). Survey data were adjusted for seasonality by fitting a generalised additive mixed-effects model with a forced periodicity for each GBD region, accounting for the year of the survey and the case definition used. The percentage difference between the monthly model-fit LRI prevalence and the corresponding regional mean LRI prevalence was computed to adjust survey data by month and geography. The mean duration of LRIs was 7·79 days ; this was determined on the basis of a systematic review and meta-analysis,Detailed methods for GBD risk factor estimation have been published elsewhere.We computed 95% UIs based on 1000 draws from the posterior distribution of each stage in the estimation process using the 2·5th and 97·5th percentiles of the 1000 ordered values.We used the GBD world population age standardThe funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.On a global scale, in 2019, the total number of LRI incident episodes was 257 million (95% UI 240–275) for males and 232 million (217–248) for females, reflecting an increase of 20·0% (95% UI 15·8–24·5) for males and 15·8% (11·9–19·7) for females since 1990 . The ageBetween 1990 and 2019, children younger than 5 years saw the greatest improvement, with a decrease in incidence rate per 100 000 population of 51·7% (95% UI 50·0–53·5) for males and 52·1% (50·7–53·7) for females. Other age groups did not show similar improvements . We estiIn 2019, we estimated that individuals aged 15–49 years had the lowest global incidence rate of LRI episodes per 100 000 population among all age groups: 4128·1 (95% UI 3726·8–4583·5) for males and 3944·6 episodes (3541·5–4421·4) for females . IndividGlobally, in terms of absolute numbers, LRIs accounted for 1·30 million (95% UI 1·18–1·42) deaths in 2019 among males and 1·20 million (1·07–1·33) deaths among females . The ageBetween 1990 and 2019, children younger than 5 years showed the greatest improvement in death rates for LRIs , with a In 2019, global mortality rates due to LRIs were highest in individuals aged 70 years and older; the mortality rate per 100 000 was 294·8 (95% UI 262·3–317·8) for males and 241·2 (202·6–266·2) for females . In contGlobally, in 2019, we estimated that 876 000 LRI deaths (95% UI 770 000–987 000) among males (PAF 67·6% [95% UI 62·9–72·1]) and 725 000 deaths (95% UI 629 000–826 000) among females (PAF 60·6% (95% UI 55·6–65·6) were attributable to all evaluated LRI risk factors . Globallvs 31·2% [21·3–41·5]; In 2019, the leading risk factor for LRI mortality in children younger than 5 years was child wasting, in both males (PAF 53·0% [95% UI 37·7–61·8]) and females  and the lowest PAF from high temperature . When all risk factors for LRIs were removed, the global age-standardised mortality rate per 100 000 population in 2019 in males was 13·5 (95% UI 11·4–15·7), and in females was 11·1  of those deaths were attributable to preventable risk factors. Over the past two decades, global initiatives to combat wasting, the leading LRI risk factor, have focused mainly on treating wasting children, particularly in humanitarian situations.In 2015, the era of the Millennium Development Goals (MDGs) ended and the global community unanimously adopted the Sustainable Development Goals (SDGs). Although substantial progress had been made towards the MDG goal of reducing under-5 mortality by two-thirds between 1990 and 2015,172·5) and higher COVID-19 cases and deaths.2·5, were non-compliant with WHO guidelines in many countries.In contrast to the progress seen in children younger than five years, little has been achieved in reducing the LRI burden among adults, indicating a need for initiatives that address LRI risk factors in the adult age groups. Ambient particulate matter pollution was a leading risk factor for LRI mortality in all adult age groups in 2019. Studies from 2020 have also suggested associations between elevated exposures to particulate matter with a diameter less than 2·5 μm , SDG 7 (affordable and clean energy), and SDG-10 .This study has several limitations. One of the key limitations is the availability of data. In the absence of data for a particular country, estimates were dependent on the regional patterns, covariates, and out-of-sample predictive validity assessment. The absence of data in a given country translated into wide intervals of uncertainty. Even in countries with data, delays in data reporting prevented their timely integration into the GBD estimation. The most recent years for which cause of death data were available were 2016 and 2017. We were able to validate our estimation method by comparing two sets of estimates produced for a particular year with and without using any data for that year. For example, GBD 2016 produced LRI mortality estimates for 2016 using data available up to 2013 and 2014; these estimates were compared with GBD 2019 estimates for the same year that were informed by empirical data for 2016. GBD 201653Although our results represent the LRI burden before the COVID-19 pandemic, the effect of the pandemic on LRIs needs to be investigated further. The pandemic was linked to a reduction of influenza and respiratory syncytial virus infections, probably as a result of mitigation measures, including mask wearing and social distancing.In conclusion, our results showed that despite an overall global decline in LRI incidence and mortality rates between 1990 and 2019, the pace of decline has been unequal across age groups. The observed progress in children younger than 5 years was clearly a result of targeted interventions, including improving vaccination and reducing exposure to risk factors. Similar interventions for other age groups could contribute to the achievement of multiple SDG targets, including promoting well-being at all ages and reducing health inequalities.To download the data used in these analyses, please visit the Global Health Data Exchange GBD 2019 website.V Abedi reports grants or contracts from Genentech/ROCHE Biotech company and the National Institutes of Health (NIH) (2R56HL116832-04) ending in 2021, outside the submitted work. S Afzal reports leadership or fiduciary roles in board, society, committee, or advocacy groups, paid or unpaid, as a member of the Corona Expert Advisory Group, a member of the Medical Microbiology and Infectious Diseases Society of Pakistan, and as secretary of the task force for integrated management of childhood illnesses, all outside the submitted work. E F Atia report grants or contracts from the NIH and National Heart, Lung, and Blood Institute (K23 HL129888) and participation on a data safety monitoring board for effectiveness of low-dose theophyline for biomass-associated chronic obstructive pulmonary disease study, all outside the submitted work. D Bryazka reports grants or contracts from Bloomberg outside the submitted work. B D Gessner is an employee of Pfizer Vaccines and holds stock options in Pfizer. J Jozwiak reports personal fees for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Teva, Amgen, Synexus, Boehringer Ingelheim, Zentiva, and Sanofi, all outside the submitted work. K Krishan reports non-financial support from the UGC Centre of Advanced Study, CAS II, Department of Anthropology, Panjab University, Chandigarh, India, all outside the submitted work. J A Loureiro reports support for the present manuscript from Scientific Employment Stimulus (CEECINST/00049/2018). A-F A Mentis reports grants or contracts from MilkSafe: a novel pipeline to enrich formula milk using omics technologies, a research co-financed by the European Regional Development Fund of the European Union and Greek national funds through the operational programme competitiveness, entrepreneurship and innovation, under the call research, create, innovate (T2EDK-02222), as well as from ELIDEK ; stock or stock options in a family winery; support from BGI Group as a scientific officer. L Monasta and L Ronfani report support for the present manuscript from the Italian Ministry of Health on project Ricerca Corrente 34/2017 and payments made to Institute for Maternal and Child Health IRCCS Burlo Garofolo. O Odukoya reports support from the present manuscript from the Fogarty International Center of the National Institutes of Health (K43TW020704) for protected time. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. M Postma reports stock or stock options from Pharmacoeconomics Advice Groningen and Health-Ecore, outside the submitted work. M Raad reports consulting fees from Fondation Mérieux, support for attending meetings from Fondation Mérieux, and is the CEO of an antibiotic prescription assistance company SMARTBIOTIC, all outside the submitted work. K E Rudd reports grants or contracts from the NIH National Institute of General Medical Sciences (1K23GM141463), outside the submitted work. C R Simpson reports grants or contracts from New Zealand Ministry of Business, Innovation and Employment, Health Research Council of New Zealand, UK Medical Research Council, and UK Chief Scientist Office, as research grants paid to their institution, outside the submitted work. J A Singh reports consulting fees from Crealta Horizon, Medisys, Fidia, PK Med, Two Labs, Adept Field Solutions, Clinical Care Options, Clearview Healthcare Partners, Putnam Associates, Focus Forward, Navigant Consulting, Spherix, MedIQ, Jupiter Life Science, UBM, Trio Health, Medscape, WebMD, and Practice Point Communications, the National Institutes of Health, and the American College of Rheumatology; payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing or educational events from Simply Speaking; support for attending meetings or travel from the steering committee of OMERACT; participation on a data safety monitoring board or advisory board with the US Food and Drug Administration Arthritis Advisory Committee; leadership or fiduciary role in board, society, committee or advocacy group, paid or unpaid, with OMERACT as a steering committee member, with the Veterans Affairs Rheumatology Field Advisory Committee as Chair (unpaid), and with the UAB Cochrane Musculoskeletal Group Satellite Center on Network Meta-analysis and editor and director (unpaid); stock or stock options in TPT Global Tech, Vaxart Pharmaceuticals, Atyu Biopharma, Adaptimmune Therapeutics, GeoVax Labs, Pieris Pharmaceuticals, Enzolytics, Seres Therapeutics, Tonix Pharmaceuticals and Charlotte's Web Holdings, and previously owned stock options in Amarin, Viking, and Moderna Pharmaceuticals; all outside the submitted work. E Upadhyay reports patents published for a system and method of reusable filters for anti-pollution mask and a system and method for electricity generation through crop stubble by using microbial fuel cells and filed for a system for disposed personal protection equipment (PPE) into biofuel through pyrolysis and method and a novel herbal pharmaceutical aid for formulation of gel and method thereof and a leadership or fiduciary role as part of the Joint Secretary of Indian Meteorological Society, Jaipur Chapter (India). A Zumla reports grants or contracts from Pan-African Network on Emerging and Re-Emerging Infections (https://www.pandora-id.net/) funded by the European and developing countries clinical trials partnership the EU horizon 2020 framework programme. Acknowledge support from EDCTP-Central Africa and East African Clinical Research Networks  and unpaid membership of the Scientific Advisory Committee of the EC-EDCTP-3 global health programme, Brussels with effect from March, 2022, all outside the submitted work. All other authors declare no competing interests.