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.

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.

BACKGROUND: This review aims to synthesise the literature on the efficacy, evolution, and challenges of implementing Clincian Decision Support Systems (CDSS) in the realm of mental health, addiction, and concurrent disorders.
METHODS: Following PRISMA guidelines, a systematic review and meta-analysis were performed. Searches conducted in databases such as MEDLINE, Embase, CINAHL, PsycINFO, and Web of Science through 25 May 2023, yielded 27,344 records. After necessary exclusions, 69 records were allocated for detailed synthesis. In the examination of patient outcomes with a focus on metrics such as therapeutic efficacy, patient satisfaction, and treatment acceptance, meta-analytic techniques were employed to synthesise data from randomised controlled trials.
RESULTS: A total of 69 studies were included, revealing a shift from knowledge-based models pre-2017 to a rise in data-driven models post-2017. The majority of models were found to be in Stage 2 or 4 of maturity. The meta-analysis showed an effect size of -0.11 for addiction-related outcomes and a stronger effect size of -0.50 for patient satisfaction and acceptance of CDSS.
CONCLUSIONS: The results indicate a shift from knowledge-based to data-driven CDSS approaches, aligned with advances in machine learning and big data. Although the immediate impact on addiction outcomes is modest, higher patient satisfaction suggests promise for wider CDSS use. Identified challenges include alert fatigue and opaque AI models.
CONCLUSIONS: CDSS shows promise in mental health and addiction treatment but requires a nuanced approach for effective and ethical implementation. The results emphasise the need for continued research to ensure optimised and equitable use in healthcare settings.

Simulation models are used in various areas of agriculture to better understand the system and assist in decision making. In the beef production sector, a variety of simulation research focusing on various dimensions of the system is available. However, an overview of the available research is lacking. Therefore, a systematic review was conducted to provide an overview of simulation studies of beef production and create an understanding of the simulation approaches used. Scopus, Web of Science, and ProQuest Central research databases were used to search the relevant articles, with the last search conducted in June 2023. Studies that developed or used simulation strategies and used beef cattle as a primary focus of the study were included. The 105 studies included in this review were examined thoroughly to record the authors, year of publication, country of study, type of study, focus area of the study, simulated scenarios, validation methods, and software programs used. There has been growing research interest in simulating beef production systems worldwide, with most studies conducted in North America and Europe. Among these studies, the majority (84.76%, n = 89) are biophysical or bioeconomic study types and use deterministic approaches (n = 42). Additionally, most studies have a whole-farm scope (38.09%, n = 40) and focus on productivity (51.43%, n = 54). Since only less than half of the studies mentioned the validation techniques and software programs used, there is a need to improve the availability of this information to ensure that the models are adopted effectively in decision making.

Diverse modelling techniques in cholera epidemiology have been developed and used to (1) study its transmission dynamics, (2) predict and manage cholera outbreaks, and (3) assess the impact of various control and mitigation measures. In this study, we carry out a critical and systematic review of various approaches used for modelling the dynamics of cholera. Also, we discuss the strengths and weaknesses of each modelling approach. A systematic search of articles was conducted in Google Scholar, PubMed, Science Direct, and Taylor & Francis. Eligible studies were those concerned with the dynamics of cholera excluding studies focused on models for cholera transmission in animals, socio-economic factors, and genetic & molecular related studies. A total of 476 peer-reviewed articles met the inclusion criteria, with about 40% (32%) of the studies carried out in Asia (Africa). About 52%, 21%, and 9%, of the studies, were based on compartmental (e.g., SIRB), statistical (time series and regression), and spatial (spatiotemporal clustering) models, respectively, while the rest of the analysed studies used other modelling approaches such as network, machine learning and artificial intelligence, Bayesian, and agent-based approaches. Cholera modelling studies that incorporate vector/housefly transmission of the pathogen are scarce and a small portion of researchers (3.99%) considers the estimation of key epidemiological parameters. Vaccination only platform was utilized as a control measure in more than half (58%) of the studies. Research productivity in cholera epidemiological modelling studies have increased in recent years, but authors used diverse range of models. Future models should consider incorporating vector/housefly transmission of the pathogen and on the estimation of key epidemiological parameters for the transmission of cholera dynamics.

UNASSIGNED: The significance of this review lies in addressing the limitations of passive locomotion in capsule endoscopes, hindering their widespread use in medical applications. The research focuses on evaluating existing miniature in vivo remote-controlled capsule endoscopes, examining their locomotion designs, and working theories to pave the way for overcoming challenges and enhancing their applicability in diagnostic and treatment settings.
UNASSIGNED: This paper explores control methods and dynamic system modeling in the context of self-propelled remote-controlled capsule endoscopes with a two-mass arrangement. The literature search, conducted at Queen Mary University of London Library from 2000 to 2022, utilized a systematic approach starting with the broad keyword \'Capsule Endoscope\' and progressively narrowing down to specific aspects such as \'Capsule Endoscope Control\' and \'Self-propelled Capsule Endoscope\' using various criteria.
UNASSIGNED: Efficiently driving and controlling remote-controlled capsule endoscopes have the potential to overcome the current limitations in medical technology, offering a viable solution for diagnosing and treating gastrointestinal diseases. Successful control of the remote-controlled capsule endoscope, as demonstrated in this review paper, will lead to a step change in medical engineering, establishing the remote-controlled capsule endoscope as a swift standard in the field.

OBJECTIVE: Randomized controlled trials are the gold standard for evidence generation in medicine but are limited by their real-world generalizability, resource needs, shorter follow-up durations and inability to be conducted for all clinical questions. Decision analysis (DA) models may simulate trials and observational studies by using existing data and evidence- and expert-informed assumptions and extend analyses over longer time horizons, different study populations and specific scenarios, helping to translate population outcomes to patient-specific clinical and economic outcomes. Here, we present a scoping review and methodological primer on DA for cardiac surgery research.
METHODS: A scoping review was performed using the PubMed/MEDLINE, EMBASE and Web of Science databases for cardiac surgery DA studies published until December 2021. Articles were summarized descriptively to quantify trends and ascertain methodological consistency.
RESULTS: A total of 184 articles were identified, among which Markov models (N = 92, 50.0%) were the most commonly used models. The most common outcomes were costs (N = 107, 58.2%), quality-adjusted life-years (N = 96, 52.2%) and incremental cost-effectiveness ratios (N = 89, 48.4%). Most (N = 165, 89.7%) articles applied sensitivity analyses, most frequently in the form of deterministic sensitivity analyses (N = 128, 69.6%). Reporting of guidelines to inform the model development and/or reporting was present in 22.3% of articles.
CONCLUSIONS: DA methods are increasing but remain limited and highly variable in cardiac surgery. A methodological primer is presented and may provide researchers with the foundation to start with or improve DA, as well as provide readers and reviewers with the fundamental concepts to review DA studies.

This paper reviews the effectiveness of four types of front-of-pack nutrition labels (FoPLs) in influencing calorie purchases. The four FoPL types are poised for unified implementation across European countries. Further, this study extends its analysis to evaluate the impacts of the voluntary adoption of these FoPLs within 27 EU nations. Nutri-Score displays higher potential for yielding positive health and economic outcomes, compared with other FoPLs. Across EU countries, Nutri-Score is projected to avert nearly two million cases of non-communicable diseases, in total, between 2023 and 2050. Keyhole demonstrates effects of a similar magnitude but with no statistical significance. Nutri-Repere shows smaller impacts, while Nutri-Couleurs has non-significant effects. Nutri-Score is projected to significantly lower annual healthcare spending by 0.05%, whereas the other labels have negligible impacts. By reducing cases of disease, FoPLs have the potential to improve employment and work productivity. Nutri-Score surpasses the other labels with an estimated annual gain of 10.6 full-time equivalent workers per 100,000 individuals of working age across EU countries. In all, mandatory implementation of any of the four labels would lead to greater effects than those obtained with a voluntary implementation, providing evidence to inform legislation proposal for an EU-wide nutrition labelling system.

Sophisticated and modern crop improvement techniques can bridge the gap for feeding the ever-increasing population. Artificial intelligence (AI) refers to the simulation of human intelligence in machines, which refers to the application of computational algorithms, machine learning (ML) and deep learning (DL) techniques. This is aimed to generalise patterns and relationships from historical data, employing various mathematical optimisation techniques thus making prediction models for facilitating selection of superior genotypes. These techniques are less resource intensive and can solve the problem based on the analysis of large-scale phenotypic datasets. ML for genomic selection (GS) uses high-throughput genotyping technologies to gather genetic information on a large number of markers across the genome. The prediction of GS models is based on the mathematical relation between genotypic and phenotypic data from the training population. ML techniques have emerged as powerful tools for genome editing through analysing large-scale genomic data and facilitating the development of accurate prediction models. Precise phenotyping is a prerequisite to advance crop breeding for solving agricultural production-related issues. ML algorithms can solve this problem through generating predictive models, based on the analysis of large-scale phenotypic datasets. DL models also have the potential reliability of precise phenotyping. This review provides a comprehensive overview on various ML and DL models, their applications, potential to enhance the efficiency, specificity and safety towards advanced crop improvement protocols such as genomic selection, genome editing, along with phenotypic prediction to promote accelerated breeding.

Plant-mediated CH4 transport (PMT) is the dominant pathway through which soil-produced CH4 can escape into the atmosphere and thus plays an important role in controlling ecosystem CH4 emission. PMT is affected by abiotic and biotic factors simultaneously, and the effects of biotic factors, such as the dominant plant species and their traits, can override the effects of abiotic factors. Increasing evidence shows that plant-mediated CH4 fluxes include not only PMT, but also within-plant CH4 production and oxidation due to the detection of methanogens and methanotrophs attached to the shoots. Despite the inter-species and seasonal differences, and the probable contribution of within-plant microbes to total plant-mediated CH4 exchange (PME), current process-based ecosystem models only estimate PMT based on the bulk biomass or leaf area index of aerenchymatous plants. We highlight five knowledge gaps to which more research efforts should be devoted. First, large between-species variation, even within the same family, complicates general estimation of PMT, and calls for further work on the key dominant species in different types of wetlands. Second, the interface (rhizosphere-root, root-shoot, or leaf-atmosphere) and plant traits controlling PMT remain poorly documented, but would be required for generalizations from species to relevant functional groups. Third, the main environmental controls of PMT across species remain uncertain. Fourth, the role of within-plant CH4 production and oxidation is poorly quantified. Fifth, the simplistic description of PMT in current process models results in uncertainty and potentially high errors in predictions of the ecosystem CH4 flux. Our review suggest that flux measurements should be conducted over multiple growing seasons and be paired with trait assessment and microbial analysis, and that trait-based models should be developed. Only then we are capable to accurately estimate plant-mediated CH4 emissions, and eventually ecosystem total CH4 emissions at both regional and global scales.