Informatics in Medicine Unlocked最新文献

Background and objectives

With the proliferation of real-world or observational health data, there is increasing interest in studying treatment trajectories. The real-life treatment trajectories can be complex, and one has to simplify the patterns to draw any conclusions; however, oversimplification will cause the loss of essential details. Thus, the visualization challenge is to strike a balance between the two extremes.

Methods

We have implemented the observation of treatment trajectories starting from cohort definitions in cooperation with medical specialists, data processing, and then generating the interactive visualizations and detailed data tables derived from input data within an open-source R package as a Shiny dashboard. The created R package called TrajectoryViz (https://github.com/HealthInformaticsUT/TrajectoryViz) enables reproducible visual analysis and visual content generation for various data investigations and explanations.

Results

We illustrate the use of the tool by assessing the sequence of events present within the data of cervical cancer prevention pathways, as well as the proportions of timely follow-up procedure events.

Conclusion

Building a toolset to access, manage, and analyze observational health data enables more accessible visual analysis of complicated data, adding time dimension to otherwise simplified event sequences that make up trajectories.

Background and objective

Tricuspid regurgitation (TR) is one of the most common forms of valvular heart diseases. The morphological information of the tricuspid valve annulus (TVA) is critical in treatment planning for TR. It is necessary to extract the TVA from medical images to obtain that information, however this task is difficult and time-consuming to perform manually. In this paper, we propose a method to automatically extract and measure the TVA from computed tomography (CT) images.

Methods

Our proposed method coarsely crops CT images to the region surrounding the tricuspid valve based on the right atrium and the right ventricle regions. The cropped CT images are input to a stacked hourglass network with loss function integrating the mean squared error loss, the focal loss and the shape-aware weighted Hausdorff distance loss to extract 36 landmarks on the TVA. The extraction accuracy of TVA landmarks was evaluated by five-fold cross validation using 120 CT images with manually annotated TVA landmarks. In addition, measurements of TVA morphology based on automatically extracted TVA and those based on manually annotated TVA were calculated and compared using the same measurement algorithm which provides a means to automatically generate seven measurements based on TVA landmarks.

Results

Our proposed method extracted TVA inside the right heart in all CT images without any processing interruption. The mean processing time was 27.09 ± 8.65 s, and the Chamfer distance and Hausdorff distance were 2.07 ± 0.53 and 4.09 ± 1.29, respectively. The mean absolute error between the measurements based on automatically extracted TVA and those based on manually annotated TVA was less than 4 mm, which is less than the typical device size interval for surgical prosthetic valve rings in current use, for measurement items related to distance. For all seven measurement items, significant correlations (r = 0.51–0.99, p < 0.0071) were shown between the measurements based on automatically extracted TVA and those based on manually annotated TVA.

Conclusions

Our proposed method was able to automatically extract and measure the TVA. This method is expected to reduce the time and effort required by physicians in treatment planning for TR.

Acinetobacter baumannii is widely recognized as a human opportunistic pathogen in nosocomial infections. The proliferation of multidrug-resistant strains of A. baumannii has presented an array of difficulties for clinical anti-infective therapies and diagnostic procedures, owing to the existence of numerous variations. The development of therapy utilizing CRISPR/Cas9 for treatment and diagnosis necessitates an in-depth study of potential off-target consequences. The objective of this work is to assess potential off-target effects associated with a single guide RNA (sgRNA) designed to identify several variants present in A. baumannii. The current investigation involved the identification of Cas12 nuclease-specific protospacer adjacent motif (PAM) and downstream target sequences. This was achieved by utilizing computational tools and software to analyze conserved sections of the A. baumannii siderophore protein gene. Further, the in-silico expression vector was created with the SnapGene software. A total of 24 potential off-target sequences were identified in these sequences with 100% query identity with 96 different A. baumannii strains. In addition, a target-specific oligonucleotide single-guide RNA (sgRNA) template was synthesized by appending an additional nucleotide 'G' to the 5′ end. This research uses A. baumannii as an example of a problem that affects all treatments and diagnosis procedures to illustrate the significance of screening off-targets in different variants of a pathogen. Our findings may impact the safety and effectiveness of CRISPR/Cas9, which may have wider implications for additional targets that are currently being used therapeutically.

Background

There is limited information on burden of hepatitis B infection in the Western zone of Tanzania. In this study, we analyzed a dataset from blood donors to determine Hepatitis B virus (HBV) seroprevalence and related socio-demographic factors among blood donors in the Western regions of Tanzania.

Material and methods

This was a cross-sectional retrospective hospital-based study, in which data were retrieved from the blood donor dataset at the Zonal Blood Transfusion Center. The analyzed information from the dataset included reported Transfusion Transmissible Infections (TTIs), which included Hepatitis B, donor demographics, donor status, donor type, donation place, and the year of donation. The analyzed data was retrieved within five years from January 2018 to December 2022. Rates of hepatitis B surface antigen (HBsAg) were determined and univariate and multivariate analyses were conducted to determine the association between infection and demographic risk factors.

Results

A total of 9604 retrospective blood donors were screened. Majority 8963 (93.3 %) were men, and most of them were under 45 years (89.6 %). Overall, HBsAg seroprevalence was 6.9 % (661), with Katavi (7.8 %) being relatively higher in the studied three regions. The highest HBsAg seroprevalence of 8.2 % (199) was found in the age group ranging from 35 to 44 years. Moreover, 2 (9.5 %) polygamists and 15 (17.1 %) car drivers had relatively high seroprevalence. Results from the multivariate analysis indicated that, car drivers (OR 5.44, 95 % CI; 2.43–12.20, p < 0.001), and first-time donors (OR 5.19, 95 % CI 2.56 = 10.52, P < 0.001), were highly associated with increased chance of getting hepatitis B infection.

Conclusion

The findings from this study indicated that, there was high seroprevalence of HBV infection in the Western regions of Tanzania during the studied time period. These findings call for more advocacy on HBV immunization for all groups of persons found at high risk for HBV infection.

Liver hepatocellular carcinoma (LIHC) is considered one of the primary contributors to cancer-related mortality on a global scale. The identification of new biomarkers is of utmost importance due to the fact that patients with LIHC are frequently detected at advanced stages, leading to an increased mortality rate. The study utilized TCGA-LIHC gene expression datasets to identify biomarkers and to address the complexity of datasets. A combination of feature selection (FS) techniques was used, and the performance of this strategy was assessed using ten machine learning classifiers. The findings were integrated, revealing biomarkers identified through at least five FS techniques. Through our proposed approach, we identified 55 potential biomarkers for LIHC. The Gaussian Naive Bayes Classifier (AUC = 0.99) was found to be the most effective classifier, achieving 98.67% accuracy when utilizing the 55 identified biomarkers in the test dataset. Additionally, we conducted differential gene expression, survival analysis, and enrichment analysis for all the identified biomarkers. Subsequently, Lasso-penalized Cox regression further refined the identified biomarkers to thirteen. Out of thirteen genes, we singled out B4GALNT1 because of its statistical significance in differential expression analysis and increasing importance across various cancer types, including LIHC. We carried out comprehensive bioinformatics and molecular dynamics simulation studies along with other structural analysis of B4GALNT1 in LIHC. In LIHC, six mutations (P64Q, S131F, A311S, R340Q, D478H, and P507Q) have been predicted to be probably damaging by evaluating in-silico prediction algorithms. In comparison to the wild type, the B4GALNT1 variations, specifically P64Q and S131F, demonstrate increased stability. However, these mutations lead to decreased atomic fluctuations, indicating a rigid protein structure. Again, mutations like A311S and P507Q induce increased flexibility, highlighting their structural impact on B4GALNT1. The study demonstrated the combination of various feature selection methods effectively reveals new biomarkers, thereby directly impacting their biological significance. Furthermore, our findings indicate a link between increased B4GALNT1 expression in individuals with liver cancer and a poorer prognosis, highlighting its potential as a promising therapeutic target.

The current study applies a new artificial intelligence (AI) method, ALiX, which is based on interval arithmetic, to analyze and interpret biological data for a clinical problem: identification of biomarkers for cancer diagnosis. The key unique and important feature of this study is that ALiX provides an explanation to our clinical hypothesis in the form of a list of ranked protein biomarkers that identifies which biomarkers are the most significant drivers of the predicted outcome, a capability that is not currently available in other AI methods. Based on the significant drivers, this study identifies a machine learning model and solution for stratifying cancer patients into subtypes that will predict response to treatment.

Background

The International Classification of Diseases 11th revision (ICD-11) serves a wide extent of uses and provides detailed information on the range, causes, and effects of human disease and death through the reported and coded data.

Objective

Concerning the ICD-11 classification system, the present study was conducted to implement the ICD-11 and evaluate coding productivity in medical coders following a 1-month training program.

Methods

An observational study was conducted in two general hospitals. During the four months from August to November, twelve trained coders coded 1,909 inpatient records. The timing of medical record reading and diagnostic coding with ICD-10 and ICD-11 was documented separately in minutes as a self-report. The trend of coding productivity changes was analyzed to evaluate productivity in the first months of ICD-11 implementation.

Results

For record this research, 1475 medical records were included. The overall productivity loss was 42.24 % in the first three months after ICD-11 use. Productivity at the end of the fourth month was slightly better than baseline ICD-10 coding. Trauma cases required more coding time as more details should be coded for post-coordination.

Conclusion

Regarding the comprehensive documentation of medical records and the completeness of the details needed for coding with ICD-11 along with the instruction of the principles of ICD-11 coding rules and convention, the time required for coding can be significantly reduced when transitioning to the ICD-11 coding system. It can be hoped that after the four months of training and mentoring the coders, the coding speed will return to the baseline.

Numerous prevention intervention strategies have been developed to curtail the spread of pulmonary tuberculosis to susceptible populations. However, pulmonary tuberculosis continues to claim many lives worldwide. In this paper, a deterministic mathematical model incorporating an asymptomatic infectious population, considering vaccine efficacy, and vaccination rate, has been formulated. The model includes asymptomatic infectious individuals since they spread infections incessantly to susceptible populations without being noticed, thus contributing to the high transmission rate. Sensitivity and numerical analysis have been conducted to investigate the impact of varying vaccine efficacy and vaccination rates on the transmission of pulmonary tuberculosis infections from the asymptomatic infectious population. The sensitivity and numerical results show that an increase in vaccine efficacy reduces the asymptomatic infectious population and subsequently lowers the transmission rate of infections. Moreover, an increase in vaccine efficacy was shown to reduce the control reproduction number due to asymptomatic infectious individuals, thereby decreasing the transmission of pulmonary tuberculosis to susceptible populations. Further results indicate that an increase in vaccination rate reduces the control reproduction number due to asymptomatic infectious individuals, consequently lowering the rate of infection transmission. These findings emphasize the need to develop a vaccine of higher efficacy to reduce infection transmission to susceptible populations by the asymptomatic infectious individuals. Additionally, the results underscore the importance of increasing vaccination rates to eradicate pulmonary tuberculosis from the population.

Introduction

In intensity-modulated radiation therapy (IMRT) techniques, although the dose conformity increases, the out-of-field doses would not decrease. This study aimed to assess the dose error calculated by the treatment planning system (TPS) in the out-of-field regions using the dynamic IMRT (D-IMRT) method in nasopharyngeal cancer (NPC) patients.

Methods

The out-of-field doses were measured for the chiasm and parotid organs using the D-IMRT technique (6 MV energy) with Monaco TPS. Computed tomography (CT) images of 10 NPC patients (54–77 years, mean: 61.6 ± 12.2 years) were considered and countered using 7-field and 11-field methods. The OCTAVIUS 4D phantom was utilized for dose assessment.

Results

According to the OCTAVIUS measurements, the Monaco TPS dose errors ranged from −58.8 to 105.5%. The average dose error for optic chiasm and parotid organs was −25% and 8.5%, respectively, with several cases falling within tolerance (±5%).

Conclusion

There were considerable dose calculation errors by Monaco TPS for organs located in out-of-field regions (optic chiasm and parotid) during IMRT for NPC patients. Therefore, accurate dose estimation in the out-of-field regions should be considered in clinical practices.