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Background: Antibiotics alone are often insufficient to treat recurrent C. difficile infection (rCDI) because they have no activity against C. difficile spores that germinate within a disrupted microbiome. SER-109, an investigational, oral, microbiome therapeutic comprised of purified Firmicutes spores, was designed to reduce rCDI through microbiome repair. We report an integrated efficacy analysis through week 24 for SER-109 from phase 3 studies, ECOSPOR III and ECOSPOR IV. Methods: ECOSPOR III was a randomized, placebo-controlled phase 3 trial conducted at 56 US or Canadian sites that included 182 participants with ≥2 CDI recurrences, confirmed via toxin EIA testing. Participants were stratified by age (<65 years or ≥65 years) and antibiotic regimen (vancomycin, fidaxomicin) and were randomized 1:1 to placebo or SER-109 groups. ECOSPOR IV was an open-label, single-arm study conducted at 72 US or Canadian sites including 263 participants with rCDI enrolled in 2 cohorts: (1) rollover participants from ECOSPOR III who experienced on-study recurrence diagnosed by toxin EIA (n = 29) and (2) participants with ≥1 CDI recurrence (diagnosed by PCR or toxin EIA), inclusive of the current episode (n = 234). In both studies, the investigational product was administered orally as 4 capsules over 3 consecutive days following symptom resolution after standard-of-care antibiotics. The primary efficacy end point was rCDI (recurrent toxin-positive diarrhea requiring treatment) through week 8. Other end points included CDI recurrence rates and safety through 24 weeks. Results: These 349 participants received at least 1 dose of SER-109 in ECOSPOR III or ECOSPOR IV (mean age 64.2; 68.8% female). Overall, 77 participants (22.1%) enrolled with their first CDI recurrence. Four participants received blinded SER-109 in ECOSPOR III followed by a second dose of open-label SER-109 in ECOSPOR IV. Overall, the proportion of participants who received any dose of SER-109 with rCDI at week 8 was 9.5% (33 of 349; 95% CI, 6.6 %–13.0%), and the CDI recurrence rate remained low through 24 weeks (15.2%, 53 of 349; 95% CI, 11.6%–19.4%), corresponding to sustained clinical response rates of 90.5% (95% CI, 87.0%–93.4%) and 84.8% (95% CI, 80.6%–88.4%), respectively (Fig. 1). Most rollover participants (25 of 29, 86.2%) were from the placebo arm; 13.8% had rCDI by week 8. Conclusions: In this integrated analysis, the rates of rCDI were low and durable in participants who received the investigational microbiome therapeutic SER-109, with sustained clinical response rates of 90.5% and 84.8% at weeks 8 and 24, respectively. These data further support the potential benefit of microbiome repair with SER-109 following antibiotics for rCDI to prevent recurrence in high-risk patients.
Financial support: This study was funded by Seres Therapeutics.
To compare the long-term vaccine effectiveness between those receiving viral vector [Oxford-AstraZeneca (ChAdOx1)] or inactivated viral (CoronaVac) primary series (2 doses) and those who received an mRNA booster (Pfizer/BioNTech) (the third dose) among healthcare workers (HCWs).
We conducted a retrospective cohort study among HCWs (aged ≥18 years) in Brazil from January 2021 to July 2022. To assess the variation in the effectiveness of booster dose over time, we estimated the effectiveness rate by taking the log risk ratio as a function of time.
Of 14,532 HCWs, coronavirus disease 2019 (COVID-19) was confirmed in 56.3% of HCWs receiving 2 doses of CoronaVac vaccine versus 23.2% of HCWs receiving 2 doses of CoronaVac vaccine with mRNA booster (P < .001), and 37.1% of HCWs receiving 2 doses of ChAdOx1 vaccine versus 22.7% among HCWs receiving 2 doses of ChAdOx1 vaccine with mRNA booster (P < .001). The highest vaccine effectiveness with mRNA booster was observed 30 days after vaccination: 91% for the CoronaVac vaccine group and 97% for the ChAdOx1 vaccine group. Vacine effectiveness declined to 55% and 67%, respectively, at 180 days. Of 430 samples screened for mutations, 49.5% were SARS-CoV-2 delta variants and 34.2% were SARS-CoV-2 omicron variants.
Heterologous COVID-19 vaccines were effective for up to 180 days in preventing COVID-19 in the SARS-CoV-2 delta and omicron variant eras, which suggests the need for a second booster.
To determine risk factors for the development of long coronavirus disease 2019 (COVID-19) in healthcare personnel (HCP).
We conducted a case–control study among HCP who had confirmed symptomatic COVID-19 working in a Brazilian healthcare system between March 1, 2020, and July 15, 2022. Cases were defined as those having long COVID according to the Centers for Disease Control and Prevention definition. Controls were defined as HCP who had documented COVID-19 but did not develop long COVID. Multiple logistic regression was used to assess the association between exposure variables and long COVID during 180 days of follow-up.
Of 7,051 HCP diagnosed with COVID-19, 1,933 (27.4%) who developed long COVID were compared to 5,118 (72.6%) who did not. The majority of those with long COVID (51.8%) had 3 or more symptoms. Factors associated with the development of long COVID were female sex (OR, 1.21; 95% CI, 1.05–1.39), age (OR, 1.01; 95% CI, 1.00–1.02), and 2 or more SARS-CoV-2 infections (OR, 1.27; 95% CI, 1.07–1.50). Those infected with the SARS-CoV-2 δ (delta) variant (OR, 0.30; 95% CI, 0.17–0.50) or the SARS-CoV-2 o (omicron) variant (OR, 0.49; 95% CI, 0.30–0.78), and those receiving 4 COVID-19 vaccine doses prior to infection (OR, 0.05; 95% CI, 0.01–0.19) were significantly less likely to develop long COVID.
Long COVID can be prevalent among HCP. Acquiring >1 SARS-CoV-2 infection was a major risk factor for long COVID, while maintenance of immunity via vaccination was highly protective.
Incorporating the dominant male sterile gene, Ms44, in new maize varieties results in 50% non-pollen producing (FNP) varieties. This makes the varieties more nitrogen efficient and increases yield directly by an average of 200 kg ha−1 across yield levels. However, as half of the plants do not shed pollen, the presence of Ms44 in an FNP variety is clearly visible. This technology can improve food production and security in the African maize-based agri-food systems, but only if accepted by farmers. Farmers were therefore invited to 11 on-farm, researcher managed trial sites of FNP varieties in Kenya over 2 years. They were asked to identify the traits they find important in evaluating maize varieties and to score the FNP varieties, as well as their conventional counterparts, on these criteria (including yield, resistance to pests, and cob size) and overall, using a five-point hedonic scale. In total, 2,697 farmers participated, of which 62% were women. Farmers mentioned many traits they find important, especially yield and related traits, early maturity, and drought resistance, but also tassel and pollen formation. In 2017, mid-season, participants scored FNP varieties lower than conventional varieties on tassel and pollen formation, indicating that farmers could distinguish the trait. FNP varieties still received higher scores for yield and overall evaluation. In mid-season 2018, participants no longer scored FNP varieties lower for pollen formation as they now understood the technology. In both years, at the end-season evaluation, scores for tassel formation were not different, but participants scored FNP varieties higher for yield and overall. We conclude that farmers recognized the FNP trait but did not mind it as they clearly favored its yield advantage. The FNP technology, therefore, has high potential not only to increase maize yields, food production, and food security in the agricultural systems of Africa but also to increase varietal turnover and the adoption of new, high-yielding, climate-smart maize hybrids.
While cannabis use is a well-established risk factor for psychosis, little is known about any association between reasons for first using cannabis (RFUC) and later patterns of use and risk of psychosis.
We used data from 11 sites of the multicentre European Gene-Environment Interaction (EU-GEI) case–control study. 558 first-episode psychosis patients (FEPp) and 567 population controls who had used cannabis and reported their RFUC.
We ran logistic regressions to examine whether RFUC were associated with first-episode psychosis (FEP) case–control status. Path analysis then examined the relationship between RFUC, subsequent patterns of cannabis use, and case–control status.
Controls (86.1%) and FEPp (75.63%) were most likely to report ‘because of friends’ as their most common RFUC. However, 20.1% of FEPp compared to 5.8% of controls reported: ‘to feel better’ as their RFUC (χ2 = 50.97; p < 0.001). RFUC ‘to feel better’ was associated with being a FEPp (OR 1.74; 95% CI 1.03–2.95) while RFUC ‘with friends’ was associated with being a control (OR 0.56; 95% CI 0.37–0.83). The path model indicated an association between RFUC ‘to feel better’ with heavy cannabis use and with FEPp-control status.
Both FEPp and controls usually started using cannabis with their friends, but more patients than controls had begun to use ‘to feel better’. People who reported their reason for first using cannabis to ‘feel better’ were more likely to progress to heavy use and develop a psychotic disorder than those reporting ‘because of friends’.
To identify central-line (CL)–associated bloodstream infection (CLABSI) incidence and risk factors in low- and middle-income countries (LMICs).
From July 1, 1998, to February 12, 2022, we conducted a multinational multicenter prospective cohort study using online standardized surveillance system and unified forms.
The study included 728 ICUs of 286 hospitals in 147 cities in 41 African, Asian, Eastern European, Latin American, and Middle Eastern countries.
In total, 278,241 patients followed during 1,815,043 patient days acquired 3,537 CLABSIs.
For the CLABSI rate, we used CL days as the denominator and the number of CLABSIs as the numerator. Using multiple logistic regression, outcomes are shown as adjusted odds ratios (aORs).
The pooled CLABSI rate was 4.82 CLABSIs per 1,000 CL days, which is significantly higher than that reported by the Centers for Disease Control and Prevention National Healthcare Safety Network (CDC NHSN). We analyzed 11 variables, and the following variables were independently and significantly associated with CLABSI: length of stay (LOS), risk increasing 3% daily (aOR, 1.03; 95% CI, 1.03–1.04; P < .0001), number of CL days, risk increasing 4% per CL day (aOR, 1.04; 95% CI, 1.03–1.04; P < .0001), surgical hospitalization (aOR, 1.12; 95% CI, 1.03–1.21; P < .0001), tracheostomy use (aOR, 1.52; 95% CI, 1.23–1.88; P < .0001), hospitalization at a publicly owned facility (aOR, 3.04; 95% CI, 2.31–4.01; P <.0001) or at a teaching hospital (aOR, 2.91; 95% CI, 2.22–3.83; P < .0001), hospitalization in a middle-income country (aOR, 2.41; 95% CI, 2.09–2.77; P < .0001). The ICU type with highest risk was adult oncology (aOR, 4.35; 95% CI, 3.11–6.09; P < .0001), followed by pediatric oncology (aOR, 2.51;95% CI, 1.57–3.99; P < .0001), and pediatric (aOR, 2.34; 95% CI, 1.81–3.01; P < .0001). The CL type with the highest risk was internal-jugular (aOR, 3.01; 95% CI, 2.71–3.33; P < .0001), followed by femoral (aOR, 2.29; 95% CI, 1.96–2.68; P < .0001). Peripherally inserted central catheter (PICC) was the CL with the lowest CLABSI risk (aOR, 1.48; 95% CI, 1.02–2.18; P = .04).
The following CLABSI risk factors are unlikely to change: country income level, facility ownership, hospitalization type, and ICU type. These findings suggest a focus on reducing LOS, CL days, and tracheostomy; using PICC instead of internal-jugular or femoral CL; and implementing evidence-based CLABSI prevention recommendations.
The occurrence of species in both polar regions (bipolarity) is a common phenomenon in the Antarctic flora. Considering the high morphological variation in polar regions due to extreme conditions, the use of molecular tools is indispensable for testing whether Arctic and Antarctic populations indeed belong to the same species. However, few phylogeographic studies of bipolar bryophytes have been conducted so far, especially when comparing molecular and morphological variation. Here, we assess the bipolarity and intraspecific variation of Roaldia revoluta, a strictly bipolar species of pleurocarpous mosses. Phylogenetic analyses based on ITS sequences clearly resolve R. revoluta as monophyletic and confirm its bipolar distribution pattern. Low intraspecific molecular variation in the markers ITS/26S and rpl16 was observed, and most specimens from both polar regions belong to a single haplotype, making it difficult to infer the origin and dispersal routes between both polar regions of R. revoluta. Morphometric analysis furthermore suggests that there are no significant morphological differences among populations from both polar regions and that morphological variation is mainly influenced by local environmental conditions. Our data do not unequivocally support the recent separation of the former intraspecific taxon Hypnum revolutum var. dolomiticum at the species level as Roaldia dolomitica.
Fast drying (~60 min) is useful for optimizing production processes by increasing productivity and reducing costs and environmental impacts, especially in red ceramic industries in Brazil. However, suitable clays are necessary and, currently, studies focused on the plastic behaviour of clays with compositions suitable for extrusion, especially for fast drying, are scarce. Therefore, in this study, three different clays from the same mineral deposit were studied for producing clay-based structural products via fast drying. The clays were characterized according to their chemical, mineralogical and thermal properties, particle size, cation-exchange capacity, specific surface area and open pore volume distribution. Ten formulations were developed using a simplex-centroid mixture design of experiments and their plasticity index (PI) values were determined. The response surfaces of the formulations were evaluated according to their PI, while the formation characteristics were determined according to their extrusion workability factor values. Formulations F5 (50.0 wt.% yellow clay and 50.0 wt.% green clay) and F8 (66.6 wt.% yellow clay, 16.7 wt.% grey clay and 16.7 wt.% green clay; PI = 15.5–16.6%) displayed optimal extrusion properties, followed by formulations F7 (33.3 wt.% yellow clay, 33.3 wt.% grey clay and 33.3 wt.% green clay) and F10 (16.7 wt.% yellow clay, 16.7 wt.% grey clay and 66.6 wt.% green clay; PI = 13.8–14.2%), which are within acceptable extrusion index values. Thus, the chosen formulations have significant potential for use in the manufacture of fast-drying red ceramics.
We build an agent-based model (ABM) of how senior politicians navigate the complex governance cycle using relatively simple heuristics. They first test whether they can form a single party minority government. If not, they seek coalition partners and negotiate with these. They treat “Gamson’s Law” – government parties get perks payoffs in proportion to their seat shares – as common knowledge. When different politicians attach different importance to the same issue, "logrolling" allows them to realize gains from trade and agree a joint policy position even when they have divergent policy preferences. We allow for the realistic possibility that multiple proposals for government are under consideration at the same time. Nonetheless, there may often be a “Condorcet winner” among the set of proposals, which beats all others in pairwise comparisons. Finally, we specify a model of government survival, which assumes incumbent governments are subject to a stream of unbiased random shocks which may perturb model parameters so much that legislators now prefer some alternative to the incumbent. For any given government, our model allows us to estimate the probability of this happening.
We set out the case for computational social science as opposed to traditional “pencil and paper” formal methods. The substantive theme of this book is the governance cycle in parliamentary democracies, but the ideas we put forward can be applied to many other areas of study.
We first calibrate and then analyze our ABM using suites of Monte Carlo simulations, applied to a representative set of training cases of government formation in European parliamentary democracies. For each to the twenty training cases, we execute 1,000 model runs, randomizing model parameters for each run as follows. For each observable parameter, for each model run for each training case, we take the empirically observed value and perturb this with parameterized random noise. For unobservable model parameters, we randomly sample from the full range of possible values. The 1,000 runs for each case thus yield a distribution of model-predicted outcome for that case. We calibrate unobservable model parameters by selecting ranges of these associated with empirically accurate model predictions. We analyze the (calibrated and uncalibrated) model by summarizing the mapping of model inputs into model outputs in the artificial data generated by the set of Monte Carlos, using theoretically informed logistic regressions. This is the computational analogue of analyses based on deductive “comparative statics” generated by traditional formal theorists.
We set out an alternative, “top down”, approach to agent-based modeling. We develop an artificial intelligence (AI) algorithm to navigate the governance cycle using what we can think of as computational game theory. AI models have had formidable success in solving games like Chess, Go, and especially a bluffing game like Poker, suggesting they also have the potential to attack difficult political games. Addressing a simplified version of the government formation process as a noncooperative game, the AI algorithm deploys Monte Carlo Counterfactual Regret (MCCFR). During in massively repeated self-play, it samples paths though the vast game tree to relentlessly learn near optimal strategies.
We describe the institutional environment for the governance cycle in parliamentary democracies and the preferences of senior politicians over key political payoffs. We are not concerned here with electoral politics, so treat an election as a “black box” which, in expectation, administers unbiased random shock to party seat shares. Elections trigger government formation. The government, once formed is subject to a steam of unbiased shocks, some of which may perturb either the environment or the preferences of senior politicians sufficiently to cause them now to prefer some alternative to the incumbent government. The more susceptible an incumbent to such shocks, according to the model, the less stable it is likely to be. Politicians’ policy preferences are described in terms of their ideal positions on a large number of binary issues, and the relative importance they attach to each issue. The utility they derive from any government is described as a convex combination of the distance between their policy preferences and the agreed government policy position, which may involve “agreeing to disagree” on some issues; and their share of the fixed perks of office.
While heavy-duty computational methods have revolutionized much empirical work in political science, computational analysis has yet to have much any impact on theoretical accounts of politics – in contrast to the situation in many of the natural sciences. We set here out to map a path forward in computational social science. Analyzing the complex and deductively intractable “governance cycle” that plays out in the high-dimensional issue spaces of parliamentary systems, we use two different computational approaches. One models functionally rational politicians who deploy rules of thumb to navigate their complex environment. The other deploys an artificial intelligence algorithm which systematic learns, from massively repeated self-play, to find near-optimal strategies. Future work made possible by greater computational firepower would enable better AI, more realistic ABMs, and the modeling of logrolling under the conditions of incomplete information which characterize most real-world bargaining and negotiation.
We use our calibrated ABM and our AI algorithm to make case-by-case predictions of outcomes in new out-of-sample test data. These predictions concern: the full partisan composition of the cabinets which form, participation by particular parties in the cabinets which form, and the observed durations of the cabinet which forms. Absent a baseline model of government formation in such complex settings against which we can evaluate our results, we compare success rates with those of a prediction of minimal winning coalitions which is common to a large number of existing studies. Bearing in mind that the ABM in particular generates probability distributions of predicted outcomes in each case, which we feel is substantively realistic, while only a single outcome can be observed, we are very satisfied with the predictive accuracy of the model. Successful predictions relating to cabinet durations are particularly distinctive to the model, deriving from the model-predicted number of issues tabled in formation negotiations, and the model-predicted likelihood than a random shock will create a situation in which a majority of legislators now prefer some alternative to the incumbent.