Meta-análisis: Intervalos de confianza e Intervalos de predicción / Meta-analysis: Confidence intervals and Prediction intervals

En los informes meta-analíticos se suelen reportar varios tipos de intervalos, hecho que ha generado cierta confusión a la hora de interpretarlos. Los intervalos de confianza reflejan la incertidumbre relacionada con un número, el tamaño del efecto medio paramétrico. Los intervalos de predicción reflejan el tamaño paramétrico probable en cualquier estudio de la misma clase que los incluidos en un meta-análisis. Su interpretación y aplicaciones son diferentes. En este artículo explicamos su diferente naturaleza y cómo se pueden utilizar para responder preguntas específicas. Se incluyen ejemplos numéricos, así como su cálculo con el paquete metafor en R.(AU)

Several types of intervals are usually employed in meta-analysis, a fact that has generated some confusion when interpreting them. Confidence intervals reflect the uncertainty related to a single number, the parametric mean effect size. Prediction intervals reflect the probable parametric effect size in any study of the same class as those included in a meta-analysis. Its interpretation and applications are different. In this article we explain in de-tail their different nature and how they can be used to answer specific ques-tions. Numerical examples are included, as well as their computation with the metafor Rpackage.(AU)

Falls experienced by adult cancer survivors: a scoping review.

PURPOSE: Accidental falls among adult cancer survivors are a health concern. Falls impose economic burdens and detrimental consequences to cancer survivors. This review aimed to synthesize findings from published research to explore the relationship between falls and cancer diagnosis and treatment among cancer survivors. MATERIALS AND METHODS: A scoping review was conducted using four databases (Medline, EMBASE, CINAHL, and Scopus) for the years 2001-2021. A total of 425 abstracts were identified after removing duplicates. A second search for the years 2022-2023 was completed where 80 abstracts were identified. Abstract screening, full-text review, and data extraction were conducted. Study characteristics and key findings were extracted from full texts. Descriptive numerical summaries were presented, and narrative analyses were performed. RESULTS AND CONCLUSIONS: A total of 42 articles were included in the scoping review which demonstrated (1) an increased prevalence of falls among cancer survivors, (2) the presence of cancer-specific fall risk factors, (3) a lack of cancer-specific fall prediction tools, and (4) few fall prevention interventions as part of usual care among cancer survivors. Younger cancer survivors were underrepresented. Cancer survivors should be aware of their risk of falls, and health professionals should ensure that fall prevention is part of usual care.

Falls are associated with cancer survivorship and as there are more people living with and beyond cancer, falls are becoming more significant.There are cancer-specific fall risk factors relevant to cancer survivors which can contribute to increased fall risk.However, fall prevention may not be addressed in standard care for cancer survivors.This review suggests cancer-specific fall risk tools are needed, and that fall prevention should be part of oncologic care.

A proof of concept model to calculate white and grey matter AIS injuries in pedestrian collisions.

In the real world, the severity of traumatic injuries is measured using the Abbreviated Injury Scale (AIS) and is often estimated, in finite element human computer models, with the maximum principal strains (MPS) tensor. MPS can predict when a serious injury is reached, but cannot provide any AIS measures lower and higher from this. To overcome these limitations, a new organ trauma model (OTM2), capable of calculating the threat to life of any organ injured, is proposed. The OTM2 model uses a power method, namely peak virtual power, and defines brain white and grey matters trauma responses. It includes human age effect (volume and stiffness), localised impact contact stiffness and provides injury severity adjustments for haemorrhaging. The focus, in this case, is on real-world pedestrian brain injuries. OTM2 model was tested against three real-life pedestrian accidents and has proven to reasonably predict the post mortem (PM) outcome. Its AIS predictions are closer to the real-world injury severity than the standard maximum principal strain (MPS) methods currently used. This proof of concept suggests that OTM2 has the potential to improve forensic predictions as well as contribute to the improvement in vehicle safety design through the ability to measure injury severity. This study concludes that future advances in trauma computing would require the development of a brain model that could predict haemorrhaging.

Reducing diagnostic delays in acute hepatic porphyria using health records data and machine learning.

BACKGROUND: Acute hepatic porphyria (AHP) is a group of rare but treatable conditions associated with diagnostic delays of 15 years on average. The advent of electronic health records (EHR) data and machine learning (ML) may improve the timely recognition of rare diseases like AHP. However, prediction models can be difficult to train given the limited case numbers, unstructured EHR data, and selection biases intrinsic to healthcare delivery. We sought to train and characterize models for identifying patients with AHP. METHODS: This diagnostic study used structured and notes-based EHR data from 2 centers at the University of California, UCSF (2012-2022) and UCLA (2019-2022). The data were split into 2 cohorts (referral and diagnosis) and used to develop models that predict (1) who will be referred for testing of acute porphyria, among those who presented with abdominal pain (a cardinal symptom of AHP), and (2) who will test positive, among those referred. The referral cohort consisted of 747 patients referred for testing and 99 849 contemporaneous patients who were not. The diagnosis cohort consisted of 72 confirmed AHP cases and 347 patients who tested negative. The case cohort was 81% female and 6-75 years old at the time of diagnosis. Candidate models used a range of architectures. Feature selection was semi-automated and incorporated publicly available data from knowledge graphs. Our primary outcome was the F-score on an outcome-stratified test set. RESULTS: The best center-specific referral models achieved an F-score of 86%-91%. The best diagnosis model achieved an F-score of 92%. To further test our model, we contacted 372 current patients who lack an AHP diagnosis but were predicted by our models as potentially having it (≥10% probability of referral, ≥50% of testing positive). However, we were only able to recruit 10 of these patients for biochemical testing, all of whom were negative. Nonetheless, post hoc evaluations suggested that these models could identify 71% of cases earlier than their diagnosis date, saving 1.2 years. CONCLUSIONS: ML can reduce diagnostic delays in AHP and other rare diseases. Robust recruitment strategies and multicenter coordination will be needed to validate these models before they can be deployed.

Three predictive scores compared in a retrospective multicenter study of nonunion tibial shaft fracture.

BACKGROUND: Delayed union, malunion, and nonunion are serious complications in the healing of fractures. Predicting the risk of nonunion before or after surgery is challenging. AIM: To compare the most prevalent predictive scores of nonunion used in clinical practice to determine the most accurate score for predicting nonunion. METHODS: We collected data from patients with tibial shaft fractures undergoing surgery from January 2016 to December 2020 in three different trauma hospitals. In this retrospective multicenter study, we considered only fractures treated with intramedullary nailing. We calculated the tibia FRACTure prediction healING days (FRACTING) score, Nonunion Risk Determination score, and Leeds-Genoa Nonunion Index (LEG-NUI) score at the time of definitive fixation. RESULTS: Of the 130 patients enrolled, 89 (68.4%) healed within 9 months and were classified as union. The remaining patients (n = 41, 31.5%) healed after more than 9 months or underwent other surgical procedures and were classified as nonunion. After calculation of the three scores, LEG-NUI and FRACTING were the most accurate at predicting healing. CONCLUSION: LEG-NUI and FRACTING showed the best performances by accurately predicting union and nonunion.

Predicting cerebrovascular age and its clinical relevance: Modeling using 3D morphological features of brain vessels.

Aging manifests as many phenotypes, among which age-related changes in brain vessels are important, but underexplored. Thus, in the present study, we constructed a model to predict age using cerebrovascular morphological features, further assessing their clinical relevance using a novel pipeline. Age prediction models were first developed using data from a normal cohort (n = 1181), after which their relevance was tested in two stroke cohorts (n = 564 and n = 455). Our novel pipeline adapted an existing framework to compute generic vessel features for brain vessels, resulting in 126 morphological features. We further built various machine learning models to predict age using only clinical factors, only brain vessel features, and a combination of both. We further assessed deviation from healthy aging using the age gap and explored its clinical relevance by correlating the predicted age and age gap with various risk factors. The models constructed using only brain vessel features and those combining clinical factors with vessel features were better predictors of age than the clinical factor-only model (r = 0.37, 0.48, and 0.26, respectively). Predicted age was associated with many known clinical factors, and the associations were stronger for the age gap in the normal cohort. The age gap was also associated with important factors in the pooled cohort atherosclerotic cardiovascular disease risk score and white matter hyperintensity measurements. Cerebrovascular age, computed using the morphological features of brain vessels, could serve as a potential individualized marker for the early detection of various cerebrovascular diseases.

Enhancing precision: A predictive model for 177Lu-DOTATATE treatment response in neuroendocrine tumors using quantitative 68Ga-DOTATATE PET and clinicopathological biomarkers.

Purpose: This study aims to elucidate the role of quantitative SSTR-PET metrics and clinicopathological biomarkers in the progression-free survival (PFS) and overall survival (OS) of neuroendocrine tumors (NETs) treated with peptide receptor radionuclide therapy (PRRT). Methods: A retrospective analysis including 91 NET patients (M47/F44; age 66 years, range 34-90 years) who completed four cycles of standard 177Lu-DOTATATE was conducted. SSTR-avid tumors were segmented from pretherapy SSTR-PET images using a semiautomatic workflow with the tumors labeled based on the anatomical regions. Multiple image-based features including total and organ-specific tumor volume and SSTR density along with clinicopathological biomarkers including Ki-67, chromogranin A (CgA) and alkaline phosphatase (ALP) were analyzed with respect to the PRRT response. Results: The median OS was 39.4 months (95% CI: 33.1-NA months), while the median PFS was 23.9 months (95% CI: 19.3-32.4 months). Total SSTR-avid tumor volume (HR = 3.6; P = 0.07) and bone tumor volume (HR = 1.5; P = 0.003) were associated with shorter OS. Also, total tumor volume (HR = 4.3; P = 0.01), liver tumor volume (HR = 1.8; P = 0.05) and bone tumor volume (HR = 1.4; P = 0.01) were associated with shorter PFS. Furthermore, the presence of large lesion volume with low SSTR uptake was correlated with worse OS (HR = 1.4; P = 0.03) and PFS (HR = 1.5; P = 0.003). Among the biomarkers, elevated baseline CgA and ALP showed a negative association with both OS (CgA: HR = 4.9; P = 0.003, ALP: HR = 52.6; P = 0.004) and PFS (CgA: HR = 4.2; P = 0.002, ALP: HR = 9.4; P = 0.06). Similarly, number of prior systemic treatments was associated with shorter OS (HR = 1.4; P = 0.003) and PFS (HR = 1.2; P = 0.05). Additionally, tumors originating from the midgut primary site demonstrated longer PFS, compared to the pancreas (HR = 1.6; P = 0.16), and those categorized as unknown primary (HR = 3.0; P = 0.002). Conclusion: Image-based features such as SSTR-avid tumor volume, bone tumor involvement, and the presence of large tumors with low SSTR expression demonstrated significant predictive value for PFS, suggesting potential clinical utility in NETs management. Moreover, elevated CgA and ALP, along with an increased number of prior systemic treatments, emerged as significant factors associated with worse PRRT outcomes.

[Construction of a Predictive Model for Diabetes Mellitus Type 2 in Middle-Aged and Elderly Populations Based on the Medical Checkup Data of National Basic Public Health Service]. / åŸºäºŽå›½å®¶åŸºæœ¬å ¬å ±å«ç”ŸæœåŠ¡ä½“æ£€çš„ä¸­è€å¹´äºº2åž‹ç³–å°¿ç— é£Žé™©é¢„æµ‹æ¨¡åž‹æž„å»º.

Objective: To establish a universally applicable logistic risk prediction model for diabetes mellitus type 2 (T2DM) in the middle-aged and elderly populations based on the results of a Meta-analysis, and to validate and confirm the efficacy of the model using the follow-up data of medical check-ups of National Basic Public Health Service. Methods: Cohort studies evaluating T2DM risks were identified in Chinese and English databases. The logistic model utilized Meta-combined effect values such as the odds ratio (OR) to derive ß, the partial regression coefficient, of the logistic model. The Meta-combined incidence rate of T2DM was used to obtain the parameter α of the logistic model. Validation of the predictive performance of the model was conducted with the follow-up data of medical checkups of National Basic Public Health Service. The follow-up data came from a community health center in Chengdu and were collected between 2017 and 2022 from 7602 individuals who did not have T2DM at their baseline medical checkups done at the community health center. This community health center was located in an urban-rural fringe area with a large population of middle-aged and elderly people. Results: A total of 40 cohort studies were included and 10 items covered in the medical checkups of National Basic Public Health Service were identified in the Meta-analysis as statistically significant risk factors for T2DM, including age, central obesity, smoking, physical inactivity, impaired fasting glucose, a reduced level of high-density lipoprotein cholesterol (HDL-C), hypertension, body mass index (BMI), triglyceride glucose (TYG) index, and a family history of diabetes, with the OR values and 95% confidence interval (CI) being 1.04 (1.03, 1.05), 1.55 (1.29, 1.88), 1.36 (1.11, 1.66), 1.26 (1.07, 1.49), 3.93 (2.94, 5.24), 1.14 (1.06, 1.23), 1.47 (1.34, 1.61), 1.11 (1.05, 1.18), 2.15 (1.75, 2.62), and 1.66 (1.55, 1.78), respectively, and the combined ß values being 0.039, 0.438, 0.307, 0.231, 1.369, 0.131, 0.385, 0.104, 0.765, and 0.507, respectively. A total of 37 studies reported the incidence rate, with the combined incidence being 0.08 (0.07, 0.09) and the parameter α being -2.442 for the logistic model. The logistic risk prediction model constructed based on Meta-analysis was externally validated with the data of 7602 individuals who had medical checkups and were followed up for at least once. External validation results showed that the predictive model had an area under curve (AUC) of 0.794 (0.771, 0.816), accuracy of 74.5%, sensitivity of 71.0%, and specificity of 74.7% in the 7602 individuals. Conclusion: The T2DM risk prediction model based on Meta-analysis has good predictive performance and can be used as a practical tool for T2DM risk prediction in middle-aged and elderly populations.

[Establishment and Validation of a Predictive Model for Gallstone Disease in the General Population: A Multicenter Study]. / èƒ†ç»“çŸ³æ‚£ç— é£Žé™©é¢„æµ‹æ¨¡åž‹çš„æž„å»ºå’Œå¤šä¸­å¿ƒéªŒè¯ç ”ç©¶.

Objective: Gallstone disease (GSD) is one of the common digestive tract diseases with a high worldwide prevalence. The effects of GSD on patients include but are not limited to the symptoms of nausea, vomiting, and biliary colic directly caused by GSD. In addition, there is mounting evidence from cohort studies connecting GSD to other conditions, such as cardiovascular diseases, biliary tract cancer, and colorectal cancer. Early identification of patients at a high risk of GSD may help improve the prevention and control of the disease. A series of studies have attempted to establish prediction models for GSD, but these models could not be fully applied in the general population due to incomplete prediction factors, small sample sizes, and limitations in external validation. It is crucial to design a universally applicable GSD risk prediction model for the general population and to take individualized intervention measures to prevent the occurrence of GSD. This study aims to conduct a multicenter investigation involving more than 90000 people to construct and validate a complete and simplified GSD risk prediction model. Methods: A total of 123634 participants were included in the study between January 2015 and December 2020, of whom 43929 were from the First Affiliated Hospital of Chongqing Medical University (Chongqing, China), 11907 were from the First People's Hospital of Jining City (Shandong, China), 1538 were from the Tianjin Medical University Cancer Institute and Hospital (Tianjin, China), and 66260 were from the People's Hospital of Kaizhou District (Chongqing, China). After excluding patients with incomplete clinical medical data, 35976 patients from the First Affiliated Hospital of Chongqing Medical University were divided into a training data set (n=28781, 80%) and a validation data set (n=7195, 20%). Logistic regression analyses were performed to investigate the relevant risk factors of GSD, and a complete risk prediction model was constructed. Factors with high scores, mainly according to the nomograms of the complete model, were retained to simplify the model. In the validation data set, the diagnostic accuracy and clinical performance of these models were validated using the calibration curve, area under the curve (AUC) of the receiver operating characteristic curve, and decision curve analysis (DCA). Moreover, the diagnostic accuracy of these two models was validated in three other hospitals. Finally, we established an online website for using the prediction model (The complete model is accessible at https://wenqianyu.shinyapps.io/Completemodel/, while the simplified model is accessible at https://wenqianyu.shinyapps.io/Simplified/). Results: After excluding patients with incomplete clinical medical data, a total of 96426 participants were finally included in this study (35876 from the First Affiliated Hospital of the Chongqing Medical University, 9289 from the First People's Hospital of Jining City, 1522 from the Tianjin Medical University Cancer Institute, and 49639 from the People's Hospital of Kaizhou District). Female sex, advanced age, higher body mass index, fasting plasma glucose, uric acid, total bilirubin, gamma-glutamyl transpeptidase, and fatty liver disease were positively associated with risks for GSD. Furthermore, gallbladder polyps, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and aspartate aminotransferase were negatively correlated to risks for GSD. According to the nomograms of the complete model, a simplified model including sex, age, body mass index, gallbladder polyps, and fatty liver disease was constructed. All the calibration curves exhibited good consistency between the predicted and observed probabilities. In addition, DCA indicated that both the complete model and the simplified model showed better net benefits than treat-all and treat-none. Based on the calibration plots, DCA, and AUCs of the complete model (AUC in the internal validation data set=74.1% [95% CI: 72.9%-75.3%], AUC in Shandong=71.7% [95% CI: 70.6%-72.8%], AUC in Tianjin=75.3% [95% CI: 72.7%-77.9%], and AUC in Kaizhou=72.9% [95% CI: 72.5%-73.3%]) and the simplified model (AUC in the internal validation data set=73.7% [95% CI: 72.5%-75.0%], AUC in Shandong=71.5% [95% CI: 70.4%-72.5%], AUC in Tianjin=75.4% [95% CI: 72.9%-78.0%], and AUC in Kaizhou=72.4% [95% CI: 72.0%-72.8%]), we concluded that the complete and simplified risk prediction models for GSD exhibited excellent performance. Moreover, we detected no significant differences between the performance of the two models (P>0.05). We also established two online websites based on the results of this study for GSD risk prediction. Conclusions: This study innovatively used the data from 96426 patients from four hospitals to establish a GSD risk prediction model and to perform risk prediction analyses of internal and external validation data sets in four cohorts. A simplified model of GSD risk prediction, which included the variables of sex, age, body mass index, gallbladder polyps, and fatty liver disease, also exhibited good discrimination and clinical performance. Nonetheless, further studies are needed to explore the role of low-density lipoprotein cholesterol and aspartate aminotransferase in gallstone formation. Although the validation results of the complete model were better than those of the simplified model to a certain extent, the difference was not significant even in large samples. Compared with the complete model, the simplified model uses fewer variables and yields similar prediction and clinical impact. Hence, we recommend the application of the simplified model to improve the efficiency of screening high-risk groups in practice. The use of the simplified model is conducive to enhancing the self-awareness of prevention and control in the general population and early intervention for GSD.

SABO-ILSTSVR: a genomic prediction method based on improved least squares twin support vector regression.

In modern breeding practices, genomic prediction (GP) uses high-density single nucleotide polymorphisms (SNPs) markers to predict genomic estimated breeding values (GEBVs) for crucial phenotypes, thereby speeding up selection breeding process and shortening generation intervals. However, due to the characteristic of genotype data typically having far fewer sample numbers than SNPs markers, overfitting commonly arise during model training. To address this, the present study builds upon the Least Squares Twin Support Vector Regression (LSTSVR) model by incorporating a Lasso regularization term named ILSTSVR. Because of the complexity of parameter tuning for different datasets, subtraction average based optimizer (SABO) is further introduced to optimize ILSTSVR, and then obtain the GP model named SABO-ILSTSVR. Experiments conducted on four different crop datasets demonstrate that SABO-ILSTSVR outperforms or is equivalent in efficiency to widely-used genomic prediction methods. Source codes and data are available at: https://github.com/MLBreeding/SABO-ILSTSVR.

DKPE-GraphSYN: a drug synergy prediction model based on joint dual kernel density estimation and positional encoding for graph representation.

Introduction: Synergistic medication, a crucial therapeutic strategy in cancer treatment, involves combining multiple drugs to enhance therapeutic effectiveness and mitigate side effects. Current research predominantly employs deep learning models for extracting features from cell line and cancer drug structure data. However, these methods often overlook the intricate nonlinear relationships within the data, neglecting the distribution characteristics and weighted probability densities of gene expression data in multi-dimensional space. It also fails to fully exploit the structural information of cancer drugs and the potential interactions between drug molecules. Methods: To overcome these challenges, we introduce an innovative end-to-end learning model specifically tailored for cancer drugs, named Dual Kernel Density and Positional Encoding (DKPE) for Graph Synergy Representation Network (DKPEGraphSYN). This model is engineered to refine the prediction of drug combination synergy effects in cancer. DKPE-GraphSYN utilizes Dual Kernel Density Estimation and Positional Encoding techniques to effectively capture the weighted probability density and spatial distribution information of gene expression, while exploring the interactions and potential relationships between cancer drug molecules via a graph neural network. Results: Experimental results show that our prediction model achieves significant performance enhancements in forecasting drug synergy effects on a comprehensive cancer drug and cell line synergy dataset, achieving an AUPR of 0.969 and an AUC of 0.976. Discussion: These results confirm our model's superior accuracy in predicting cancer drug combinations, providing a supportive method for clinical medication strategy in cancer.

HUPA-UCM diabetes dataset.

This dataset provides a collection of Continuous Glucose Monitoring (CGM) data, insulin dose administration, meal ingestion counted in carbohydrate grams, steps, calories burned, heart rate, and sleep quality and quantity assessment ac- quired from 25 people with type 1 diabetes mellitus (T1DM). CGM data was acquired by FreeStyle Libre 2 CGMs, and Fitbit Ionic smartwatches were used to obtain steps, calories, heart rate, and sleep data for at least 14 days. This dataset could be utilized to obtain glucose prediction models, hypoglycemia and hyperglycemia prediction models, and research on the relationships among sleep, CGM values, and the rest of the mentioned variables. This dataset could be used directly from the preprocessed version or customized from raw data. The data set has been used previously with different machine learning algorithms to predict glucose values, hypo, and hyperglycemia and to analyze influences among the features and the quality and quantity of sleep in people with T1DM.

Nomogram to predict radicular grooves in maxillary lateral incisors in preoperative orthodontic population.

OBJECTIVES: This study aimed to develop and validate a predictive nomogram for diagnosing radicular grooves (RG) in maxillary lateral incisors (MLIs), integrating demographic information, anatomical measurements, and Cone Beam Computed Tomography (CBCT) data to diagnose the RG in MLIs based on the clinical observation before resorting to the CBCT scan. MATERIALS AND METHODS: A retrospective cohort of orthodontic patients from the School and Hospital of Stomatology, Wuhan University, was analyzed, including demographic characteristics, photographic anatomical assessments, and CBCT diagnoses. The cohort was divided into development and validation groups. Univariate and multivariate logistic regression analyses identified significant predictors of RG, which informed the development of a nomogram. This nomogram's performance was validated using receiver operating characteristic analysis. RESULTS: The study included 381 patients (64.3% female) and evaluated 760 MLIs, with RG present in 26.25% of MLIs. The nomogram incorporated four significant anatomical predictors of RG presence, demonstrating substantial predictive efficacy with an area under the curve of 0.75 in the development cohort and 0.71 in the validation cohort. CONCLUSIONS: A nomogram for the diagnosis of RG in MLIs was successfully developed. This tool offers a practical checklist of anatomical predictors to improve the diagnostic process in clinical practice. CLINICAL RELEVANCE: The developed nomogram provides a novel, evidence-based tool to enhance the detection and treatment planning of MLIs with RG in diagnostic and therapeutic strategies.

Use of Individual-Based Mathematical Modelling to Understand More About Antibiotic Resistance Within-Host.

To model complex systems, individual-based models (IBMs), sometimes called "agent-based models" (ABMs), describe a simplification of the system through an adequate representation of the elements. IBMs simulate the actions and interaction of discrete individuals/agents within a system in order to discover the pattern of behavior that comes from these interactions. Examples of individuals/agents in biological systems are individual immune cells and bacteria that act independently with their own unique attributes defined by behavioral rules. In IBMs, each of these agents resides in a spatial environment and interactions are guided by predefined rules. These rules are often simple and can be easily implemented. It is expected that following the interaction guided by these rules we will have a better understanding of agent-agent interaction as well as agent-environment interaction. Stochasticity described by probability distributions must be accounted for. Events that seldom occur such as the accumulation of rare mutations can be easily modeled.Thus, IBMs are able to track the behavior of each individual/agent within the model while also obtaining information on the results of their collective behaviors. The influence of impact of one agent with another can be captured, thus allowing a full representation of both direct and indirect causation on the aggregate results. This means that important new insights can be gained and hypotheses tested.

Phylogenetic Survival Analysis.

Going back in time through a phylogenetic tree makes it possible to evaluate ancestral genomes and assess their potential to acquire key polymorphisms of interest over evolutionary time. Knowledge of this kind may allow for the emergence of key traits to be predicted and pre-empted from currently circulating strains in the future. Here, we present a novel genome-wide survival analysis and use the emergence of drug resistance in Mycobacterium tuberculosis as an example to demonstrate the potential and utility of the technique.

Within-Host Mathematical Models of Antibiotic Resistance.

Mathematical models have been used to study the spread of infectious diseases from person to person. More recently studies are developing within-host modeling which provides an understanding of how pathogens-bacteria, fungi, parasites, or viruses-develop, spread, and evolve inside a single individual and their interaction with the host's immune system.Such models have the potential to provide a more detailed and complete description of the pathogenesis of diseases within-host and identify other influencing factors that may not be detected otherwise. Mathematical models can be used to aid understanding of the global antibiotic resistance (ABR) crisis and identify new ways of combating this threat.ABR occurs when bacteria respond to random or selective pressures and adapt to new environments through the acquisition of new genetic traits. This is usually through the acquisition of a piece of DNA from other bacteria, a process called horizontal gene transfer (HGT), the modification of a piece of DNA within a bacterium, or through. Bacteria have evolved mechanisms that enable them to respond to environmental threats by mutation, and horizontal gene transfer (HGT): conjugation; transduction; and transformation. A frequent mechanism of HGT responsible for spreading antibiotic resistance on the global scale is conjugation, as it allows the direct transfer of mobile genetic elements (MGEs). Although there are several MGEs, the most important MGEs which promote the development and rapid spread of antimicrobial resistance genes in bacterial populations are plasmids and transposons. Each of the resistance-spread-mechanisms mentioned above can be modeled allowing us to understand the process better and to define strategies to reduce resistance.

Use of Whole Genome Sequencing for Mycobacterium tuberculosis Complex Antimicrobial Susceptibility Testing: From Sequence Data to Resistance Profiles.

Whole genome sequencing of Mycobacterium tuberculosis complex (MTBC) isolates has been shown to provide accurate predictions for resistance and susceptibility for many first- and second-line anti-tuberculosis drugs. However, bioinformatic pipelines and mutation catalogs to predict antimicrobial resistances in MTBC isolates are often customized and detailed protocols are difficult to access. Here, we provide a step-by-step workflow for the processing and interpretation of short-read sequencing data and give an overview of available analysis pipelines.

[Numerical study of the effect of geometrical parameters of straight impellers on the flow and hemolysis performance of centrifugal blood pumps].

Red blood cells are destroyed when the shear stress in the blood pump exceeds a threshold, which in turn triggers hemolysis in the patient. The impeller design of centrifugal blood pumps significantly influences the hydraulic characteristics and hemolytic properties of these devices. Based on this premise, the present study employs a multiphase flow approach to numerically simulate centrifugal blood pumps, investigating the performance of pumps with varying numbers of blades and blade deflection angles. This analysis encompassed the examination of flow field characteristics, hydraulic performance, and hemolytic potential. Numerical results indicated that the concentration of red blood cells and elevated shear stresses primarily occurred at the impeller and volute tongue, which drastically increased the risk of hemolysis in these areas. It was found that increasing the number of blades within a certain range enhanced the hydraulic performance of the pump but also raised the potential for hemolysis. Moreover, augmenting the blade deflection angle could improve the hemolytic performance, particularly in pumps with a higher number of blades. The findings from this study can provide valuable insights for the structural improvement and performance enhancement of centrifugal blood pumps.

AI-Enabled Portable E-Nose Regression Predicting Harmful Molecules in a Gas Mixture.

The biomimetic electronic nose (e-nose) technology is a novel technology used for the identification and monitoring of complex gas molecules, and it is gaining significance in this field. However, due to the complexity and multiplicity of gas mixtures, the accuracy of electronic noses in predicting gas concentrations using traditional regression algorithms is not ideal. This paper presents a solution to the difficulty by introducing a fusion network model that utilizes a transformer-based multikernel feature fusion (TMKFF) module combined with a 1DCNN_LSTM network to enhance the accuracy of regression prediction for gas mixture concentrations using a portable electronic nose. The experimental findings demonstrate that the regression prediction performance of the fusion network is significantly superior to that of single models such as convolutional neural network (CNN) and long short-term memory (LSTM). The present study demonstrates the efficacy of our fusion network model in accurately predicting the concentrations of multiple target gases, such as SO2, NO2, and CO, in a gas mixture. Specifically, our algorithm exhibits substantial benefits in enhancing the prediction performance of low-concentration SO2 gas, which is a noteworthy achievement. The determination coefficient (R2) values of 93, 98, and 99% correspondingly demonstrate that the model is very capable of explaining the variation in the concentration of the target gases. The root-mean-square errors (RMSE) are 0.0760, 0.0711, and 3.3825, respectively, while the mean absolute errors (MAE) are 0.0507, 0.0549, and 2.5874, respectively. These results indicate that the model has relatively small prediction errors. The method we have developed holds significant potential for practical applications in detecting atmospheric pollution detection and other molecular detection areas in complex environments.

Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework.

BACKGROUND: Biologically annotated neural networks (BANNs) are feedforward Bayesian neural network models that utilize partially connected architectures based on SNP-set annotations. As an interpretable neural network, BANNs model SNP and SNP-set effects in their input and hidden layers, respectively. Furthermore, the weights and connections of the network are regarded as random variables with prior distributions reflecting the manifestation of genetic effects at various genomic scales. However, its application in genomic prediction has yet to be explored. RESULTS: This study extended the BANNs framework to the area of genomic selection and explored the optimal SNP-set partitioning strategies by using dairy cattle datasets. The SNP-sets were partitioned based on two strategies-gene annotations and 100 kb windows, denoted as BANN_gene and BANN_100kb, respectively. The BANNs model was compared with GBLUP, random forest (RF), BayesB and BayesCπ through five replicates of five-fold cross-validation using genotypic and phenotypic data on milk production traits, type traits, and one health trait of 6,558, 6,210 and 5,962 Chinese Holsteins, respectively. Results showed that the BANNs framework achieves higher genomic prediction accuracy compared to GBLUP, RF and Bayesian methods. Specifically, the BANN_100kb demonstrated superior accuracy and the BANN_gene exhibited generally suboptimal accuracy compared to GBLUP, RF, BayesB and BayesCπ across all traits. The average accuracy improvements of BANN_100kb over GBLUP, RF, BayesB and BayesCπ were 4.86%, 3.95%, 3.84% and 1.92%, and the accuracy of BANN_gene was improved by 3.75%, 2.86%, 2.73% and 0.85% compared to GBLUP, RF, BayesB and BayesCπ, respectively across all seven traits. Meanwhile, both BANN_100kb and BANN_gene yielded lower overall mean square error values than GBLUP, RF and Bayesian methods. CONCLUSION: Our findings demonstrated that the BANNs framework performed better than traditional genomic prediction methods in our tested scenarios, and might serve as a promising alternative approach for genomic prediction in dairy cattle.