UNASSIGNED: Numerous studies have developed or validated prediction models to estimate the likelihood of postoperative pneumonia (POP) in esophageal cancer (EC) patients. The quality of these models and the evaluation of their applicability to clinical practice and future research remains unknown. This study systematically evaluated the risk of bias and applicability of risk prediction models for developing POP in patients undergoing esophageal cancer surgery.
UNASSIGNED: PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), WanFang Database and Chinese Biomedical Literature Database were searched from inception to March 12, 2024. Two investigators independently screened the literature and extracted data. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was employed to evaluate both the risk of bias and applicability.
UNASSIGNED: A total of 14 studies involving 23 models were included. These studies were mainly published between 2014 and 2023. The applicability of all studies was good. However, all studies exhibited a high risk of bias, primarily attributed to inappropriate data sources, insufficient sample size, irrational treatment of variables and missing data, and lack of model validation. The incidence of POP in patients undergoing esophageal cancer surgery ranged from 14.60% to 39.26%. The most frequently used predictors were smoking, age, chronic obstructive pulmonary disease(COPD), diabetes mellitus, and methods of thoracotomy. Inter-model discrimination ranged from 0.627 to 0.850, sensitivity ranged between 60.7% and 84.0%, and specificity ranged from 59.1% to 83.9%.
UNASSIGNED: In all included studies, good discrimination was reported for risk prediction models for POP in patients undergoing esophageal cancer surgery, indicating stable model performance. However, according to the PROBAST checklist, all studies had a high risk of bias. Future studies should use the predictive model assessment tool to improve study design and develop new models with larger samples and multicenter external validation.
UNASSIGNED: https://www.crd.york.ac.uk/prospero, identifier CRD42024527085.

Radiotherapy has played an essential role in cancer treatment for over a century, and remains one of the best-studied methods of cancer treatment. Because of its close links with the physical sciences, it has been the subject of extensive quantitative mathematical modelling, but a complete understanding of the mechanisms of radiotherapy has remained elusive. In part this is because of the complexity and range of scales involved in radiotherapy - from physical radiation interactions occurring over nanometres to evolution of patient responses over months and years. This review presents the current status and ongoing research in modelling radiotherapy responses across these scales, including basic physical mechanisms of DNA damage, the immediate biological responses this triggers, and genetic-and patient-level determinants of response. Finally, some of the major challenges in this field, and potential avenues for future improvements are also discussed.

Modelling and prediction of air quality facilitates the drafting of efficient guidelines and, in turn, proper management of adversely affected areas. In order to depict the air pollutants in urban centres, this research analyses two modelling tools: AERMOD and CALINE4. Both technologies provide distinct capabilities in the modelling of air quality from vehicular and other emissions. CALINE4, a Gaussian dispersion model, specifies pollutant dispersion from mobile sources along roadways. It boasts a user-friendly interface and road-specific modelling capabilities, factoring in traffic speed and vehicle emissions. However, it simplifies intricate flow patterns and relies on primary meteorological data. On the other hand, AERMOD is a versatile model suitable for various emission sources, including both mobile and stationary sources. It excels at capturing diverse atmospheric processes but demands precise meteorological, terrain, and emission data. AERMOD is often preferred for regulatory compliance assessments, although it entails a steeper learning curve and higher computational requirements. The choice between CALINE4 and AERMOD hinges on study needs, data availability, and desired modelling precision. This review offers an overview to assist researchers in making informed model selections for assessing vehicle-related pollution, critical aspects of urban sustainability, and air quality management.

The HIV treatment landscape for adults has progressed dramatically in recent decades; however, paediatric populations continue to experience delayed and limited access to effective and safe antiretroviral therapy options. Despite current incentive programs, formulation research and development and approved drug dosing for children have been limited, particularly for neonates (aged <4 weeks). Regulatory approval of drug formulations and dosing in children may lag behind adult approvals by years. Formulation and trial design adjustments complicate paediatric drug development, all of which are vital to accommodate for physiological differences, organ maturation, and rapid weight gain, which are most significant in the youngest children. To facilitate more rapid anti-infective drug development for paediatric populations, regulatory agencies provide guidelines that include extrapolating efficacy and safety data from relevant populations; using pharmacokinetic (PK) bridging and modelling to reduce sample sizes and limit the number of PK studies needed before efficacy analyses; and enrolling age- or weight-based cohorts in parallel rather than sequentially for clinical trials. Ensuring access to approved drugs poses an additional challenge, as uncertainty in demand leads to manufacturing and supply complexity with potentially higher costs that can be a barrier to uptake. Here we summarize challenges in drug development for children living with HIV, which are not unique to antiretrovirals. We aim to propose strategies for how model-based approaches and global partnerships can overcome some of these barriers to accelerate paediatric drug development, with particular reference to HIV, and how lessons learnt from HIV could be extended to other anti-infectives.

A continuous challenge in nanotoxicology is the interaction of nanoparticles with the soil components. In the present study, we compare the toxicity of silver nanoparticles (AgNM300K) on earthworms across 4 different soils, exploring which among the total-, soil solution-, or worm tissue-Ag-concentrations that enables the best prediction of toxicity across the soils. We exposed the earthworm Eisenia fetida to AgNM300K for 56 days to assess survival, reproduction, and bioaccumulation. These endpoints were related to measurements of Ag-ions and -nanoparticles in soil, soil solution, and in the worm tissue. Tested soils included the standard OECD, LUFA 2.2, Hygum, and RefSol 01A soils. Toxicity was strongly dependent on the soil type, highly correlated with the organic matter, clay, and Cation Exchange Capacity (CEC). CEC provided the best correlation with the internal silver concentrations across the soils. The soil solution did not provide useful predictions across the soils.

The Baltic Sea is a severely disturbed marine ecosystem previously used as a dumping ground for chemical warfare agents (CWA), which are now known to enter its food web. We have performed a modelling exercise using a calibrated and validated Central Baltic Ecopath with Ecosim (EwE) model to recreate the potential environmental pathways of the infamous Clark I (diphenylchlorarsine). Observations from modelling timestamps covering recent times correspond with in situ detections in sediments and Atlantic cod (Gadus morhua). Under applied modelling conditions and scenarios, there is an active transfer of Clark I from sediments through the Baltic Sea food-web. According to our results, Clark I bioaccumulates within the Baltic Sea food web exclusively throughout the detritus-based food chain. The EwE model for the Central Baltic Sea also allows the simulation of changes in the food web under multiple anthropogenic stressors and management efforts, including recommendations from the Helsinki Commission Baltic Sea Action Plan (HELCOM BSAP). Among all investigated scentarios and factors, the commercial fishing is the most impactful on Clark I accumulation rate and contamination transfer within the Baltic Sea food web. The study indicates the need to extend the existing monitoring approach by adding additional species representing a broader range of ecological niches and tiers within the food chains. From the environmental perspective, the remediation of Chemical Weapons by removal should be considered as part of the integrated management of the Baltic Sea.

暂无摘要。

Measuring dairy cattle methane (CH4) emissions using traditional recording technologies is complicated and expensive. Prediction models, which estimate CH4 emissions based on proxy information, provide an accessible alternative. This review covers the different modelling approaches taken in the prediction of dairy cattle CH4 emissions and highlights their individual strengths and limitations. Following the guidelines set out by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA); Scopus, EBSCO, Web of Science, PubMed and PubAg were each queried for papers with titles that contained search terms related to a population of \"Bovine,\" exposure of \"Statistical Analysis or Machine Learning,\" and outcome of \"Methane Emissions\". The search was executed in December 2022 with no publication date range set. Eligible papers were those which investigated the prediction of CH4 emissions in dairy cattle via statistical or machine learning methods and were available in English. 299 papers were returned from the initial search, 55 of which, were eligible for inclusion in the discussion. Data from the 55 papers was synthesised by the CH4 emission prediction approach explored, including mechanistic modelling, empirical modelling, and machine learning (ML). Mechanistic models were found to be highly accurate, yet they require difficult to obtain input data, which, if imprecise, can produce misleading results. Empirical models remain more versatile by comparison, yet suffer greatly when applied outside of their original developmental range. The prediction of CH4 emissions on commercial dairy farms can utilise any approach, however the traits they use must be procurable in a commercial farm setting. Milk fatty acids (MFA) appear to be the most popular commercially accessible trait under investigation, however, MFA-based models have produced ambivalent results and should be consolidated before robust accuracies can be achieved. ML models provide a novel methodology for the prediction of dairy cattle CH4 emissions through a diverse range of advanced algorithms, and can facilitate the combination of heterogenous data types via hybridisation or stacking techniques. In addition to this, they also offer the ability to improve dataset complexity through imputation strategies. These opportunities allow ML models to address the limitations faced by traditional prediction approaches, as well as enhance prediction on commercial farms.

BACKGROUND: Sleeping sickness (gambiense human African trypanosomiasis, gHAT) is a vector-borne disease targeted for global elimination of transmission (EoT) by 2030. There are, however, unknowns that have the potential to hinder the achievement and measurement of this goal. These include asymptomatic gHAT infections (inclusive of the potential to self-cure or harbour skin-only infections) and whether gHAT infection in animals can contribute to the transmission cycle in humans.
METHODS: Using modelling, we explore how cryptic (undetected) transmission impacts the monitoring of progress towards and the achievement of the EoT goal. We have developed gHAT models that include either asymptomatic or animal transmission, and compare these to a baseline gHAT model without either of these transmission routes, to explore the potential role of cryptic infections on the EoT goal. Each model was independently calibrated to five different health zones in the Democratic Republic of the Congo (DRC) using available historical human case data for 2000-2020 (obtained from the World Health Organization\'s HAT Atlas). We applied a novel Bayesian sequential updating approach for the asymptomatic model to enable us to combine statistical information about this type of transmission from each health zone.
RESULTS: Our results suggest that, when matched to past case data, we estimated similar numbers of new human infections between model variants, although human infections were slightly higher in the models with cryptic infections. We simulated the continuation of screen-confirm-and-treat interventions, and found that forward projections from the animal and asymptomatic transmission models produced lower probabilities of EoT than the baseline model; however, cryptic infections did not prevent EoT from being achieved eventually under this approach.
CONCLUSIONS: This study is the first to simulate an (as-yet-to-be available) screen-and-treat strategy and found that removing a parasitological confirmation step was predicted to have a more noticeable benefit to transmission reduction under the asymptomatic model compared with the others. Our simulations suggest vector control could greatly impact all transmission routes in all models, although this resource-intensive intervention should be carefully prioritised.

Over the last century, many peatlands in northern Europe have been drained for forestry. Forest management with different harvesting regimes has a significant impact on soil water status and consequently on greenhouse gas emissions from peat soils. In this paper, we have used the process-based JSBACH-HIMMELI model to simulate the effects of alternative harvesting regimes, namely non-harvested (NH), selection harvesting (SH; 70 % of stem volume harvested) and clear-cutting (CC; 100 % of stem volume harvested), on soil CH4 and CO2 fluxes in peatland forests. We modified the model to account for the specific characteristics of peatland forests, where the water level (WL) is generally low and is regulated by the amount of aboveground vegetation through evapotranspiration. Multi-year measurements before and after the forest harvesting in a nutrient-rich peatland forest in southern Finland were used to constrain the model. The results showed that the modified model was able to reproduce the seasonal dynamics of water level, soil CH4 and soil CO2 fluxes under alternative harvesting regimes with reasonable accuracy. The averaged Pearson\'s r (Pearson correlation coefficient) and RMSE (Root Mean Square Error) between the model and the measurement were 0.75 and 7.3 cm for WL, 0.75 and 0.23 nmol m-2 s-1 for soil CH4 flux, 0.73 and 0. 88 μmol m-2 s-1 for soil CO2 flux. The modified model successfully reproduced soil CH4 uptake at both NH and SH sites and soil CH4 emission at the CC site, as observed in the measurements. Our study showed that increasing harvesting intensity (NH → SH → CC) in the model increased soil CH4 emission and decreased soil CO2 emission on an annual basis, but the magnitude of the decreased soil CO2 emission was much larger than that of the increased soil CH4 emission when comparing their global warming potentials. Therefore, in the short term as in our study (first three years after the harvest), the climate impacts of the soil GHG was reduced more in CC than in SH, which yet can be fundamentally different when considering in the long term.