Bacteraemia is a critical condition that generally leads to substantial morbidity and mortality. It is unclear whether delayed antimicrobial therapy (and/or source control) has a prognostic or defervescence effect on patients with source-control-required (ScR) or unrequired (ScU) bacteraemia.
The multicenter cohort included treatment-naïve adults with bacteraemia in the emergency department. Clinical information was retrospectively obtained and etiologic pathogens were prospectively restored to accurately determine the time-to-appropriate antibiotic (TtAa). The association between TtAa or time-to-source control (TtSc, for ScR bacteraemia) and 30-day crude mortality or delayed defervescence were respectively studied by adjusting independent determinants of mortality or delayed defervescence, recognised by a logistic regression model.
Of the total 5477 patients, each hour of TtAa delay was associated with an average increase of 0.2% (adjusted odds ratio [AOR], 1.002; P < 0.001) and 0.3% (AOR 1.003; P < 0.001) in mortality rates for patients having ScU (3953 patients) and ScR (1524) bacteraemia, respectively. Notably, these AORs were augmented to 0.4% and 0.5% for critically ill individuals. For patients experiencing ScR bacteraemia, each hour of TtSc delay was significantly associated with an average increase of 0.31% and 0.33% in mortality rates for overall and critically ill individuals, respectively. For febrile patients, each additional hour of TtAa was significantly associated with an average 0.2% and 0.3% increase in the proportion of delayed defervescence for ScU (3085 patients) and ScR (1266) bacteraemia, respectively, and 0.5% and 0.9% for critically ill individuals. For 1266 febrile patients with ScR bacteraemia, each hour of TtSc delay respectively was significantly associated with an average increase of 0.3% and 0.4% in mortality rates for the overall population and those with critical illness.
Regardless of the need for source control in cases of bacteraemia, there seems to be a significant association between the prompt administration of appropriate antimicrobials and both a favourable prognosis and rapid defervescence, particularly among critically ill patients. For ScR bacteraemia, delayed source control has been identified as a determinant of unfavourable prognosis and delayed defervescence. Moreover, this association with patient survival and the speed of defervescence appears to be augmented among critically ill patients.

There have been speculations regarding rise in antimicrobial resistance (AMR) globally owing to indiscriminate antibiotic usage during the COVID-19 pandemic. To curb the menace through decisive policies, it is essential to assess the antibiotics, particularly the antibacterial agents. This systematic review and meta-analysis were performed to assess antibiotic use in COVID-19 patients. A thorough systematic search was undertaken in databases like PubMed, Cochrane library, Google Scholar, World Health Organization (WHO) database and clinicaltrials.gov by two independent reviewers for articles in English published from January 1, 2019 to October 31, 2020. Studies were included if they assessed confirmed COVID-19 cases and mentioned the use of antibiotics. The primary outcome was the proportion of COVID-19 patients subjected to specific antibacterial agents. An attempt to stratify the data based on study settings and disease severity was also performed. Of the total 6012 studies screened, 40 were eligible for qualitative review and 19 for meta-analysis. Specific antibacterial agents were mentioned in 23 studies (57.5%). In the random effect meta-analysis, pooled prevalence of azithromycin use was 24.5% (95% CI 22.9-26.2%) followed by cephalosporins as 26.6% (95% CI 24.9-28.4). None of the studies clearly specified indications for antibiotic use. Ten studies (25%) mentioned empirical use of antibiotics. Bacterial co-infections/secondary infections were documented in four studies with mean prevalence of infection of 1.9% (95% CI 1.2-2.8%). There is lack of data on use of specific antibacterial agents, indications for their use based on severity of infections and microbiological evidence of bacterial co-infections.

Hippodamia convergens-the convergent lady beetle, has been used extensively in augmentative biological control of aphids, thrips, and whiteflies across its native range in North America, and was introduced into South America in the 1950s. Overwintering H. convergens populations from its native western range in the United States are commercially collected and released across its current range in the eastern USA, with little knowledge of the effectiveness of its augmentative biological control. Here we use a novel ddRADseq-based SNP/haplotype discovery approach to estimate its range-wide population diversity, differentiation, and recent evolutionary history. Our results indicate (1) significant population differentiation among eastern USA, western USA, and South American populations of H. convergens, with (2) little to no detectable recent admixture between them, despite repeated population augmentation, and (3) continued recent population size expansion across its range. These results contradict previous findings using microsatellite markers. In light of these new findings, the implications for the effectiveness of augmentative biological control using H. convergens are discussed. Additionally, because quantifying the non-target effects of augmentative biological control is a difficult problem in migratory beetles, our results could serve as a cornerstone in improving and predicting the efficacy of future releases of H. convergens across its range.

Well-designed, neuroadaptive mobile geographic information displays (namGIDs) could improve the lives of millions of mobile citizens of the mostly urban information society who daily need to make time critical and societally relevant decisions while navigating. What are the basic perceptual and neurocognitive processes with which individuals make movement decisions when guided by human- and context-adaptive namGIDs? How can we study this in an ecologically valid way, also outside of the highly controlled laboratory? We report first ideas and results from our unique neuroadaptive research agenda that brings us closer to answering this fundamental empirical question. We present our first implemented methodological solutions of novel ambulatory evaluation methods to study and improve Location-based System (LBS) displays, by critical examination of how perceptual, neurocognitive, psychophysiological, and display design factors might influence decision-making and spatial learning in pedestrian mobility across broad ranges of users and mobility contexts.

UNASSIGNED: The eradication rate of Helicobacter pylori infection with empirical therapy has decreased due to increased drug resistance. The latest guidelines recommend genotypic resistance-guided therapy, but its clinical efficacy remains unclear.
UNASSIGNED: The purpose of our study was to evaluate whether tailored therapy based on genotypic resistance is superior to empirical therapy for H. pylori infection.
UNASSIGNED: A systematic review and meta-analysis of randomized controlled trials (RCTs) comparing tailored therapy based on genotypic resistance with empirical therapy was performed.
UNASSIGNED: We retrieved relevant studies from PubMed, Embase, and the Cochrane Library. The primary outcome was H. pylori eradication rate and the adverse events (AEs) was the secondary outcome. A random-effect model was applied to compare pooled risk ratios (RRs) with related 95% confidence intervals (CIs).
UNASSIGNED: A total of 12 qualified RCTs containing 3940 patients were identified in our systematic review and meta-analysis. The pooled eradication rates of tailored therapy based on the detection of genotypic resistance were consistently higher than those in the empirical treatment group, with no statistical significance. In triple therapy, the eradication rate was significantly higher in the tailored group than in the empirical group by intention-to-treat analysis (ITT) and per-protocol analysis (PP) analysis (p < 0.0001, RR: 1.20; 95% CI: 1.12-1.29; p < 0.0001, RR: 1.20; 95% CI: 1.15-1.25). In quadruple therapy, the eradication rate was higher in the empirical group (p = 0.001, RR: 0.93; 95% CI: 0.89-0.97; p = 0.009, RR: 0.95; 95% CI: 0.92-0.99). And this result was true for both bismuth quadruple therapy (BQT) and non-BQT. Regarding total AEs, the pooled rate was 34% in the tailored group and 37% in the empirical group, and no difference between the two groups was found (p = 0.17, RR: 0.88; 95% CI: 0.74-1.06).
UNASSIGNED: In conclusion, tailored therapy based on molecular methods may offer better efficacy than empirical triple therapy, but it may not be superior to empirical quadruple therapy in eradicating H. pylori infection. Larger and more individualized RCTs are needed to aid clinical decision-making.
UNASSIGNED: CRD42023408688.

BACKGROUND: Over the past several decades, metrics have been defined to assess the quality of various types of models and to compare their performance depending on their capacity to explain the variance found in real-life data. However, available validation methods are mostly designed for statistical regressions rather than for mechanistic models. To our knowledge, in the latter case, there are no consensus standards, for instance for the validation of predictions against real-world data given the variability and uncertainty of the data. In this work, we focus on the prediction of time-to-event curves using as an application example a mechanistic model of non-small cell lung cancer. We designed four empirical methods to assess both model performance and reliability of predictions: two methods based on bootstrapped versions of parametric statistical tests: log-rank and combined weighted log-ranks (MaxCombo); and two methods based on bootstrapped prediction intervals, referred to here as raw coverage and the juncture metric. We also introduced the notion of observation time uncertainty to take into consideration the real life delay between the moment when an event happens, and the moment when it is observed and reported.
RESULTS: We highlight the advantages and disadvantages of these methods according to their application context. We have shown that the context of use of the model has an impact on the model validation process. Thanks to the use of several validation metrics we have highlighted the limit of the model to predict the evolution of the disease in the whole population of mutations at the same time, and that it was more efficient with specific predictions in the target mutation populations. The choice and use of a single metric could have led to an erroneous validation of the model and its context of use.
CONCLUSIONS: With this work, we stress the importance of making judicious choices for a metric, and how using a combination of metrics could be more relevant, with the objective of validating a given model and its predictions within a specific context of use. We also show how the reliability of the results depends both on the metric and on the statistical comparisons, and that the conditions of application and the type of available information need to be taken into account to choose the best validation strategy.

To retrospectively evaluate clinical and microbiological outcomes after combined surgical and medical therapy for diabetic foot infections (DFIs), stratifying between the empirical versus the targeted nature, and between an empirical broad versus a narrow-spectrum, antibiotic therapy.
We retrospectively assessed the rate of ultimate therapeutic failures for each of three types of initial postoperative antibiotic therapy: adequate empirical therapy; culture-guided therapy; and empirical inadequate therapy with a switch to targeted treatment based on available microbiological results.
We included data from 332 patients who underwent 716 DFI episodes of surgical debridement, including partial amputations. Clinical failure occurred in 40 of 194 (20.6%) episodes where adequate empirical therapy was given, in 77 of 291 (26.5%) episodes using culture-guided (and correct) therapy from the start, and in 73 of 231 (31.6%) episodes with switching from empirical inadequate therapy to culture-targeted therapy. Equally, a broad-spectrum antibiotic choice could not alter this failure risk. Group comparisons, Kaplan-Meier curves and Cox regression analyses failed to show either statistical superiority or inferiority of any of the initial antibiotic strategies.
In this study, the microbiological adequacy of the initial antibiotic regimen after (surgical) debridement for DFI did not alter therapeutic outcomes. We recommend that clinicians follow the stewardship approach of avoiding antibiotic de-escalation and start with a narrow-spectrum regimen based on the local epidemiology.

Solar irradiation data is essential for the feasibility of solar energy projects. Notably, the intermittent nature of solar irradiation influences solar energy use in all forms, whether energy or agriculture. Accurate solar irradiation prediction is the only solution to effectively use solar energy in different forms. The estimation of solar irradiation is the most critical factor for site selection and sizing of solar energy projects and for selecting a suitable crop selection for the area. But the physical measurement of solar irradiation, due to the cost and technology involved, is not possible for all locations across the globe. Numerous techniques have been implemented to predict solar irradiation for this purpose. The two types of approaches that are most frequently employed are empirical techniques and artificial intelligence (AI). Both approaches have demonstrated good accuracy in various places of the world. To find out the best method, a thorough review of research articles discussing solar irradiation prediction has been done to compare different methods for solar irradiation prediction. In this paper, articles predicting solar irradiation using AI and empirical published from 2017 to 2022 have been reviewed, and both methods have been compared. The review showed that AI methods are more accurate than empirical methods. In empirical models, modified sunshine-based models (MSSM) have the highest accuracy, followed by sunshine-based (SSM) and non-sunshine-based models (NSM). The NSM has a little lower accuracy than MSSM and SSM, but the NSM can give good results in sunshine data unavailability. Also, the literature review confirmed that simple empirical models could predict accurately, and increasing the empirical model\'s polynomial order cannot improve results. Artificial neural networks (ANN) and Hybrid models have the highest accuracy among AI methods, followed by support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS). The increase in efficiency by hybrid models is minimal, but the complexity of models requires very sophisticated programming knowledge. ANN\'s most important input factors are maximum and minimum temperatures, temperature differential, relative humidity, clearness index and precipitation.

Antimicrobial or antioxidant active packaging (AP) is an emerging technology in which a bioactive antimicrobial or antioxidant agent is incorporated into the packaging material to protect the contained product during its shelf life from deterioration. The important issue in AP is making a balance between the deterioration rate of the food product and the controlled release of the bioactive agent. So, the AP fabrication should be designed in such a way that fulfills this goal. Modeling the controlled release is an effective way to avoid trial and error and time-consuming experimental runs and predict the release behavior of bioactive agents in different polymeric matrices and food/food simulants. To review the release of bioactive compounds from AP, in the first part of this review we present an introductory explanation regarding the release controlling approaches in AP. Then the release mechanisms are explained which are very important in defining the appropriate modeling approach and also the interpretation of the modeling results. Different release profiles that might be observed in different packaging systems are also introduced. Finally, different modeling approaches including empirical and mechanistic techniques are covered and the recent literature regarding the utilization of such approaches to help design new AP is thoroughly studied.

Objective: To evaluate the safety of megadose meropenem as empirical treatment of nosocomial sepsis. Materials & methods: Critically ill patients diagnosed with sepsis received either high-dose (2 g every 8 h) or megadose (4 g every 8 h) meropenem as an intravenous infusion over 3 h. Results: A total of 23 patients with nosocomial sepsis were eligible and included in the megadose (n = 11) or high-dose (n = 12) group. No treatment-related adverse events were observed during a 14-day follow-up. Clinical response was also comparable between the groups. Conclusion: Megadose meropenem may be considered for empirical treatment of nosocomial sepsis without serious concern regarding its safety.
As resistance to antibiotics is increasing among microbes, rational use of these drugs is important both in the community and in hospitals. Many infections with resistant microorganisms may be fatal. For a long time, carbapenems have been the last resort for treatment of resistant microorganisms. Unfortunately, resistance to these drugs is increasing. It appears that use of higher doses of antibiotics may help in some cases. However, the potential harm caused by higher doses is a problem. In this primary study, higher doses of meropenem, a common carbapenem, were found to be safe.