The impact of diabetes mellitus on tuberculosis recurrence in Eastern China: a retrospective cohort study.

BACKGROUND: The comorbidity of tuberculosis (TB) and diabetes mellitus (DM) is a significant global public health issue. This study aims to explore the recurrence risk and related factors of active pulmonary TB, specifically focusing on the impact of DM. METHODS: A retrospective cohort study was conducted in Lianyungang City, Jiangsu Province, Eastern China by recruiting 12,509 individuals with newly diagnosed pulmonary TB between 2011 and 2019. The Cox proportional hazards models were performed to identify risk factors of recurrence and assess the association between DM and recurrence. The hazard ratio (HR) and 95% confidence interval (CI) were used to estimate the strength of the association. RESULTS: After a median follow-up period of 5.46 years, we observed 439 recurrent cases (incident recurrence rate: 6.62 per 1000 person-years). Males (HR: 1.30, 95% CI: 1.03-1.64), patients aged ≥ 60 years (HR: 1.39, 95% CI: 1.15-1.70), DM (HR: 2.40, 95% CI: 1.68-3.45), and etiologic positivity in the initial episode (HR: 2.42, 95% CI: 2.00-2.92) had a significantly increased risk of recurrence. CONCLUSIONS: Recurrence of pulmonary TB patients who have completed treatment, especially those who also suffer from DM, should be a concern. Enhanced follow-up and targeted surveillance of these high-risk groups are needed.

A clinical indicator-based prognostic model predicting treatment outcomes of pulmonary tuberculosis: a prospective cohort study.

OBJECTIVES: Identifying prognostic factors helps optimize the treatment regimen and promote favorable outcomes. We conducted a prospective cohort study on patients with pulmonary tuberculosis to construct a clinical indicator-based model and estimate its performance. METHODS: We performed a two-stage study by recruiting 346 pulmonary tuberculosis patients diagnosed between 2016 and 2018 in Dafeng city as the training cohort and 132 patients diagnosed between 2018 and 2019 in Nanjing city as the external validation population. We generated a risk score based on blood and biochemistry examination indicators by the least absolute shrinkage and selection operator (LASSO) Cox regression. Univariate and multivariate Cox regression models were used to assess the risk score, and the strength of association was expressed as the hazard ratio (HR) and 95% confidence interval (CI). We plotted the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC). Internal validation was conducted by 10-fold cross-validation. RESULTS: Ten significant indicators (PLT, PCV, LYMPH, MONO%, NEUT, NEUT%, TBTL, ALT, UA, and Cys-C) were selected to generate the risk score. Clinical indicator-based score (HR: 10.018, 95% CI: 4.904-20.468, P < 0.001), symptom-based score (HR: 1.356, 95% CI: 1.079-1.704, P = 0.009), pulmonary cavity (HR: 0.242, 95% CI: 0.087-0.674, P = 0.007), treatment history (HR: 2.810, 95% CI: 1.137-6.948, P = 0.025), and tobacco smoking (HR: 2.499, 95% CI: 1.097-5.691, P = 0.029) were significantly related to the treatment outcomes. The AUC was 0.766 (95% CI: 0.649-0.863) in the training cohort and 0.796 (95% CI: 0.630-0.928) in the validation dataset. CONCLUSION: In addition to the traditional predictive factors, the clinical indicator-based risk score determined in this study has a good prediction effect on the prognosis of tuberculosis.

Environment pollutants exposure affects the endogenous activation of within-host Mycobacterium tuberculosis.

OBJECTIVE: Epidemiological studies have linked ambient pollutants with tuberculosis (TB) risk, but the association has not been fully understood. Here, for the first time, we applied whole-genome sequencing (WGS) to assess the reproductive state of Mycobacterium tuberculosis (MTB) by profiling the mutation rate of MTB (MTBMR) during within-host endogenous reactivated progression, intending to dissect the actual effects of ambient pollutants on the endogenous reactivation. METHODS: We conducted a retrospective cohort study on bacteriologically confirmed TB patients and followed them for relapse in Jiangsu and Sichuan Province, China. Endogenous and exogenous activation were distinguished by WGS of the pathogen. The average concentration of air pollution was estimated by considering a lag of 0-1 to 0-12 months. We applied a generalized additive model with a Poisson function to evaluate the relationships between ambient pollutants exposure and MTBMR. RESULTS: In the single-pollutant adjusted models, the maximum effect for PM10 (MTBMR increase: 81.87%, 95% CI: 38.38, 139.03) and PM2.5 (MTBMR increase: 73.91%, 95% CI: 22.17, 147.55) was observed at a lag of 0-12 months for every 10 µg/m³ increase. For SO2, the maximum effect was observed at lag 0-8 months, with MTBMR increasing by 128.06% (95% CI: 45.92, 256.44); and for NO2, the maximum effect was observed at lag 0-9 months, with MTBMR increasing by 124.02% (95% CI: 34.5, 273.14). In contrast, the O3 concentration was inversely associated with MTBMR, and the maximum reduction of MTBMR was 6.18% (95% CI: -9.24, -3.02) at a lag of 0-9 months. Similar results were observed for multi-pollutant models. CONCLUSIONS: Increased exposure to ambient pollutants (PM10, PM2.5, SO2, and NO2) contributed to a faster MTBMR, indicating that MTB exhibits increased reproductive activity, thus accelerating within-host endogenous reactivation. O3 exposure could decrease the MTBMR, suggesting that MTB exerts low reproductive activity by inhibiting within-host endogenous activation.

Application of Computer-Aided Diagnosis to the Sonographic Evaluation of Cervical Lymph Nodes.

We initiated an observer study to evaluate a computerized system developed in our previous study for automatic extraction of 10 features and estimation of the malignancy probability of cervical nodes in sonograms. In the present study, five expert radiologists and five resident radiologists interpreted the sonograms of 178 nodes. The malignancy rating and patient management recommendation (biopsy or follow-up) were made without and then with the computer aid. Under these two reading conditions, the performances of radiologists and agreement among a group of radiologists were evaluated by using the receiver operating characteristic (ROC) analysis and the κ statistic, respectively. With the computer aid, the performances of radiologists improved significantly, as indicated by the increase in the area under the ROC curve (Az) from 0.843 to 0.896 (p = 0.031) and from 0.705 to 0.822 (p < 0.001), for the expert and resident groups, respectively. Agreement among all 10 radiologists improved from slight to moderate as indicated by an increase in the κ value from 0.195 to 0.421 (p < 0.001). The average performance of residents with aid (Az = 0.822) was close to that of experts without aid (Az = 0.843). Results indicate that computer-aided diagnosis is useful to improve radiologist performance (especially that of inexperienced radiologists) in the ultrasonographic evaluation of cervical nodes and to reduce variability among radiologists.

Intelligent speckle reducing anisotropic diffusion algorithm for automated 3-D ultrasound images.

A novel 3-D filtering method is presented for speckle reduction and detail preservation in automated 3-D ultrasound images. First, texture features of an image are analyzed by using the improved quadtree (QT) decomposition. Then, the optimal homogeneous and the obvious heterogeneous regions are selected from QT decomposition results. Finally, diffusion parameters and diffusion process are automatically decided based on the properties of these two selected regions. The computing time needed for 2-D speckle reduction is very short. However, the computing time required for 3-D speckle reduction is often hundreds of times longer than 2-D speckle reduction. This may limit its potential application in practice. Because this new filter can adaptively adjust the time step of iteration, the computation time is reduced effectively. Both synthetic and real 3-D ultrasound images are used to evaluate the proposed filter. It is shown that this filter is superior to other methods in both practicality and efficiency.

Multivariable time series prediction for the icing process on overhead power transmission line.

The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters.

[An approach for the measurement of the surface area of scalp flap over the cranial defect after decompressive craniectomy].

OBJECTIVE: To introduce a simple, fast and universal measuring method used in measurement of the surface area of scalp flap over the cranial defect after decompressive craniectomy. METHODS: The first step: CT images of the patient with craniocerebral trauma after decompressive craniectomy were obtained and imported into Mimics. The second step: based on the defined threshold, the 3D geometric models of brain and skull were reconstructed after the original Dicom format pictures three-dimensional processed by Mimics. The third step: based on the two builded 3D models, utilizing the segmentation and measurement tools of Mimics to conduct cutting, splitting and measuring operations for the 3D model of brain. The forth step: estimating the surface area of scalp flap over the removed bone flap by using mathematical computation methods. RESULTS: The application of the introduced method estimated the surface area of scalp flap over the cranial defect of different people with different position of craniocerebral trauma. CONCLUSIONS: This paper introduces a simple, fast and universal new measuring method. We can conveniently estimate the surface area of scalp flap by using the introduced method.

Impacts of heavy smoking on non-coding RNA expression for patients with esophageal carcinoma.

BACKGROUND: Smoking is a well-recognized risk factor for esophageal carcinoma, but the underlying molecular mechanism remains unclear. Previous studies have demonstrated the predictive value of non-coding RNA (ncRNA) for the prognosis of esophageal carcinoma; however, the expression of smoking-related ncRNAs has not been systematically characterized. Herein, we comprehensively assessed the hazard of heavy smoking and its impact on ncRNA expression patterns in patients with esophageal carcinoma. METHODS: Transcriptome and clinical features of patients with esophageal carcinoma were acquired from The Cancer Genome Atlas (TCGA) database. Cox regression analysis was employed to calculate the hazard ratio (HR) of smoking behavior. Differential expression analysis was conducted with the "edgeR" package. The smoking-related RNA regulatory network was based on lncRNA‒miRNA and miRNA‒mRNA pairs and visualized by Cytoscape 3.7.1. We applied Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for functional annotation. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used for model construction. We applied Kaplan‒Meier analysis with a log-rank test for survival analysis, with group comparison by the Wilcoxon signed ranked test. RESULTS: Heavy smoking contributed to the poor overall survival of esophageal carcinoma, with an HR of 3.167 (95% CI: 1.077-9.312). A total of 195 lncRNAs and 73 miRNAs were differentially expressed between patients with or without smoking behavior. We constructed smoking-related RNA regulatory networks, and functional annotation enriched a series of cancer-related pathways. We generated a smoking-related prognostic risk score and found that patients with a high score had a poor prognosis. Fourteen out of 23 immune cell types differentially infiltrated into a distinct risk group, while no correlation was observed between the risk score and immune cells. CONCLUSION: Altogether, we profiled smoking-related ncRNA expression patterns and constructed an RNA regulatory network, providing a landscape of smoking-related molecular mechanisms of esophageal carcinoma. The smoking-related risk score, which was related to prognosis, revealed that tobacco smoking could suppress tumor immunity via the ncRNA mechanism.

Inactivated COVID-19 vaccine booster dose shortened the viral shedding time of patients infected with the Omicron variant BA.2.

OBJECTIVES: We assessed the effect of inactivated COVID-19 vaccine boosting immunization on the viral shedding time for patients infected with the Omicron variant BA.2. METHODS: We performed a real-world study by analyzing the outbreak data of patients infected with the COVID-19 Omicron variant BA.2 from March to May 2022 in Shanghai, China. Patients were categorized into three groups, including not fully vaccinated (zero and one dose), fully vaccinated (two doses), and booster-vaccinated (three doses). RESULTS: A total of 4443 patients infected with COVID-19 were included in the analysis. The proportion of viral shedding within 14 days in the three groups was 94.7%, 95.5%, and 96.7%, respectively (P <0.001). After adjusting for sex, age, underlying conditions, and clinical symptoms, the booster vaccination had a 29% increased possibility (hazard ratio: 1.29, 95% confidence interval: 1.18-1.41) of no detectable viral shedding within 14 days, whereas the fully vaccinated group had an 11% increased possibility of no detectable viral shedding (hazard ratio: 1.11, 95% confidence interval: 1.01-1.23). The effect of booster vaccination was more significant in males, the elderly, and people with underlying conditions or symptomatic infections. CONCLUSION: Our study confirmed that the booster vaccination could significantly shorten the viral shedding time of patients infected with the Omicron variant BA.2.

Molecular features, biological behaviors and clinical implications of m5C RNA methylation modification regulators in gastrointestinal cancers.

Epitranscriptome studies have shown that critical RNA modifications drive tumorigenicity; however, the role of 5-methylcytosine (m5C) RNA methylation remains poorly understood. We extracted 17 m5C regulators and clustered distinct m5C modification patterns by consensus clustering analysis. Gene set variation and single-sample gene set enrichment analysis were applied to quantify functional analysis and immune infiltration. The least absolute shrinkage and selection operator was employed to develop a prognostic risk score. Kaplan-Meier with log-rank test was used for survival analysis. Differential expression analysis was performed with the "limma" R package. Wilcoxon signed ranked test or Kruskal-Wallis test was used to compare groups. We observed that m5C RNA methylation was commonly upregulated in gastrointestinal cancer and related to prognosis. Clusters were identified for m5C patterns, with distinct immune infiltrations and functional pathways. The risk scores of m5C regulators were independent risk factors. Differentially expressed mRNAs (DEmRNAs) in m5C clusters were involved in cancer-related pathways. The methylation-based m5Cscore showed a significant effect on the prognosis. Patients with a lower m5Cscore exhibited more therapeutic efficiency on anti-CTLA4 therapy in liver cancer, while the combination of anti-CTLA4 therapy and pd1 was more efficient for patients with a lower m5Cscore in pancreatic cancer. We uncovered dysregulations of m5C-related regulators in gastrointestinal cancer and their associations with overall survival. Some immune cells were differently infiltrated in distinct m5C modification patterns, indicating their potential impacts on gastrointestinal cancer cell-immune. Moreover, an m5Cscore, derived from DEmRNAs in specific clusters, can serve as a classifier for immunotherapy.

A computer-aided Cobb angle measurement method and its reliability.

STUDY DESIGN: Development of a computer-aided Cobb measurement method and evaluation of its reliability. OBJECTIVES: To reduce the variability of Cobb angle measurement by developing the computer-aided method and to investigate if the developed method is sensitive to observer skill levels or experiences. SUMMARY OF BACKGROUND DATA: Therapeutic decisions for scoliosis heavily rely on the Cobb angle measured from consecutive radiographs. The manual Cobb measurement is subject to human errors. The observer error is 3 to 10 degrees resulted from different end-vertebrae selection and/or manually drawing variable best-fit lines to the endplates of the end-vertebrae. METHODS: A fussy Hough transform technique was used to develop a computer-aided method to detect the vertebral endplates. The Cobb angle, upper end-vertebra, and lower end-vertebra were then measured automatically. The computer-aided method was tested twice by each of 3 observers in 84 posteroanterior radiographs from patients with adolescent idiopathic scoliosis. The intraobserver and interobserver errors were analyzed. RESULTS: Both the intraobserver and interobserver reliability analyses resulted in the intraclass correlation coefficients higher than 0.9 for the Cobb angle. The average intraobserver and interobserver errors were less than 3 degree for the Cobb angle, and less than 0.3 levels for both the upper and lower end-vertebral identification. There were no significant differences in the measurement variability between groups of curve location (thoracic, thoracolumbar, and lumbar), curve direction (right and left), curve magnitude (curves less than 25 degree, between 25 and 45 degrees, and more than 45 degree), and observer experience (experienced observer and inexperienced observers). CONCLUSIONS: Compared with the documented results, variability of the Cobb measurement is reduced by using the developed computer-aided method. This method can help orthopedic surgeons measure the Cobb angle more reliably during scoliosis clinics.

Preservation of quadrature Doppler signals from bidirectional slow blood flow close to the vessel wall using an adaptive decomposition algorithm.

A novel approach based on the phasing-filter (PF) technique and the empirical mode decomposition (EMD) algorithm is proposed to preserve quadrature Doppler signal components from bidirectional slow blood flow close to the vessel wall. Bidirectional mixed Doppler ultrasound signals, which were echoed from the forward and reverse moving blood and vessel wall, were initially separated to avoid the phase distortion of quadrature Doppler signals (which is induced from direct decomposition by the nonlinear EMD processing). Separated unidirectional mixed Doppler signals were decomposed into intrinsic mode functions (IMFs) using the EMD algorithm and the relevant IMFs that contribute to blood flow components were identified and summed to give the blood flow signals, whereby only the components from the bidirectional slow blood flow close to the vessel wall were retained independently. The complex quadrature Doppler blood flow signal was reconstructed from a combination of the extracted unidirectional Doppler blood flow signals. The proposed approach was applied to simulated and clinical Doppler signals. It is concluded from the experimental results that this approach is practical for the preservation of quadrature Doppler signal components from the bidirectional slow blood flow close to the vessel wall, and may provide more diagnostic information for the diagnosis and treatment of vascular diseases.

[Using CTS and PK-PD models to predict the effect of uncertainty about population parameters on clinical trial power].

The traditional clinical trail designs always depend on expert opinions and lack statistical evaluations. In this article, we present a method and illustrate how population parameter uncertainty may be incorporated in the overall simulation model. Using the techniques of clinical trail simulation (CTS) and setting up predictions on the basis of pharmacokinetics-pharmacodynamics (PK-PD) models, we advance the modeling methods for simulation, for treatment effects, and for the clinical trail power under the given PK-PD conditions. Then we discuss the model of uncertainty, suggest an ANOVA-based method, add eta2 statistics for sensitivity analysis, and canvass the effect of uncertainty about population parameters on clinical trail power. The results from simulations and the indices derived from this type of sensitivity analysis may be used for grading the influence on the prediction quality of uncertainty about different population parameters. The experiment results are satisfactory and the approach presented has practical value in clinical trails.

ECG compression using uniform scalar dead-zone quantization and conditional entropy coding.

A new wavelet-based method for the compression of electrocardiogram (ECG) data is presented. A discrete wavelet transform (DWT) is applied to the digitized ECG signal. The DWT coefficients are first quantized with a uniform scalar dead-zone quantizer, and then the quantized coefficients are decomposed into four symbol streams, representing a binary significance stream, the signs, the positions of the most significant bits, and the residual bits. An adaptive arithmetic coder with several different context models is employed for the entropy coding of these symbol streams. Simulation results on several records from the MIT-BIH arrhythmia database show that the proposed coding algorithm outperforms some recently developed ECG compression algorithms.

[Evaluation studies of ultrasonic image features extraction for cervical lymph nodes].

Abstract Ultrasonic image features of cervical lymph nodes were extracted by a computerized scheme. The scheme quantified the parameters of shape, border, cortex to medulla ratio, distributing of medulla, long axis to short axis ratio, vascular density and vascular pattern features. Then correlations between the experts andthe computerized scheme for assessing parameters in corresponding categories were computed to evaluate the validity of the parameters extracted by the computer. 106 cervical lymph nodes were studied, and the results showed that good agreement was seen in NRL (normalized radial length) zero crossing count, convex hull depth, long axis to short axis ratio, the vascular density and the entropy of the blood mapping boundary points' distribution.

The removal of wall components in Doppler ultrasound signals by using the empirical mode decomposition algorithm.

Doppler ultrasound systems, used for the noninvasive detection of the vascular diseases, normally employ a high-pass filter (HPF) to remove the large, low-frequency components from the vessel wall from the blood flow signal. Unfortunately, the filter also removes the low-frequency Doppler signals arising from slow-moving blood. In this paper, we propose to use a novel technique, called the empirical mode decomposition (EMD), to remove the wall components from the mixed signals. The EMD is firstly to decompose a signal into a finite and usually small number of individual components named intrinsic mode functions (IMFs). Then a strategy based on the ratios between two adjacent values of the wall-to-blood signal ratio (WBSR) has been developed to automatically identify and remove the relevant IMFs that contribute to the wall components. This method is applied to process the simulated and clinical Doppler ultrasound signals. Compared with the results based on the traditional high-pass filter, the new approach obtains improved performance for wall components removal from the mixed signals effectively and objectively, and provides us with more accurate low blood flow.

Noise reduction in Doppler ultrasound signals using an adaptive decomposition algorithm.

A novel de-noising method for improving the signal-to-noise ratio (SNR) of Doppler ultrasound blood flow signals, called the matching pursuit method, has been proposed. Using this method, the Doppler ultrasound signal was first decomposed into a linear expansion of waveforms, called time-frequency atoms, which were selected from a redundant dictionary named Gabor functions. Subsequently, a decay parameter-based algorithm was employed to determine the decomposition times. Finally, the de-noised Doppler signal was reconstructed using the selected components. The SNR improvements, the amount of the lost component in the original signal and the maximum frequency estimation precision with simulated Doppler blood flow signals, have been used to evaluate a performance comparison, based on the wavelet, the wavelet packets and the matching pursuit de-noising algorithms. From the simulation and clinical experiment results, it was concluded that the performance of the matching pursuit approach was better than those of the DWT and the WPs methods for the Doppler ultrasound signal de-noising.

Correction for broadening in Doppler blood flow spectrum estimated using wavelet transform.

The conventionally used spectral estimation technique for Doppler blood flow signal analysis is short-time Fourier transform (STFT). But this method requires stationarity of the signal during the window interval. Wavelet transform (WT), which has a flexible time-frequency window, is particularly suitable for nonstationary signals. In recently years, the WT has been used to investigate its advantages and limitations for the analysis of Doppler blood flow signals. In these studies, the estimated spectral width of Doppler blood flow signals using the WT might include significant window and nonstationarity broadening errors. These broadening errors of the time-varying spectrum were clearly undesirable since it would tend to mask the effect of flow disturbance on the spectra width. In this paper, a closed form expression for window and nonstationary root-mean-squared (rms) spectral width is given when using the WT to estimate the Doppler blood flow spectrum. The increases in the rms spectral width can be calculated and then the spectral width estimation based on the WT can be corrected.

[Application of Burg algorithm in time-frequency analysis of Doppler blood flow signal based on AR modeling].

The Doppler blood flow signal is inherently a nonstationary Gaussian random process whose time-frequency representation associates with the time-varying velocity of blood flow and its variations. With the assumption that the signal being analyzed is stationary during a short time interval, we can not get Doppler time-frequency representations with satisfactory time and frequency resolution. AR modeling based on Levinson-Durbin algorithm has been used to generate time-frequency representations of Doppler blood flow signals. But the errors of the parameters computed by the algorithm will be aggrandized by the shortening of the time interval. Burg has advanced an algorithm, which computes the parameters by making the sum of forward and backward forecasting errors minimum. In the paper, time-frequency representations computed by Burg and Levinson-Durbin algorithm were compared with the theoretical representation. We found that the time-frequency representations computed by Burg algorithm are more similar to the theoretical representation, especially in frequency band.

[The Doppler fetal heart rate detection with wavelet transform].

Various interference and noises should be eliminated while picking up the signal of fetal heart rate from the Doppler fetal signal in order to obtain smooth curve of the fetal heart Doppler signal and so to figure out the fetal heart rate. In this study, the mean frequency shift curve estimated from the Doppler fetal signals was smoothed using wavelet transform. The results showed that the fetal-heart rate obtained by computing from the autocorrelation waveform based on the smoothed mean frequency shift curve was more accurate than that based on unsmoothed curve.