A retrospective review of tuberculosis exposure among health care workers in a tertiary hospital

Functionalizing Dendrimers for Targeted Delivery of Bioactive Molecules to Macrophages: A Potential Treatment for Mycobacterium tuberculosis Infection-A Review

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that replicates inside human alveolar macrophages. This disease causes significant morbidity and mortality throughout the world. According to the World Health Organization 1.4 million people died of this disease in 2021. This indicates that despite the progress of modern medicine, improvements in diagnostics, and the development of drug susceptibility tests, TB remains a global threat to public health. In this sense, host-directed therapy may provide a new approach to the cure of TB, and the expression of miRNAs has been correlated with a change in the concentration of various inflammatory mediators whose concentrations are responsible for the pathophysiology of M. tuberculosis infection. Thus, the administration of miRNAs may help to modulate the immune response of organisms. However, direct administration of miRNAs, without adequate encapsulation, exposes nucleic acids to the activity of cytosolic nucleases, limiting their application. Dendrimers are a family of highly branched molecules with a well-defined architecture and a branched conformation which gives rise to cavities that facilitate physical immobilization, and functional groups that allow chemical interaction with molecules of interest. Additionally, dendrimers can be easily functionalized to target different cells, macrophages among them. In this sense, various studies have proposed the use of different cell receptors as target molecules to aim dendrimers at macrophages and thus release drugs or nucleic acids in the cell of interest. Based on the considerations, the primary objective of this review is to comprehensively explore the potential of functionalized dendrimers as delivery vectors for miRNAs and other therapeutic agents into macrophages. This work aims to provide insights into the use of functionalized dendrimers as an innovative approach for TB treatment, focusing on their ability to target and deliver therapeutic cargo to macrophages.

Prediction of tuberculosis cases based on sociodemographic and environmental factors in gombak, Selangor, Malaysia: A comparative assessment of multiple linear regression and artificial neural network models

Background

Early prediction of tuberculosis (TB) cases is very crucial for its prevention and control. This study aims to predict the number of TB cases in Gombak based on sociodemographic and environmental factors.

Methods

The sociodemographic data of 3325 TB cases from January 2013 to December 2017 in Gombak district were collected from the MyTB web and TB Information System database. Environmental data were obtained from the Department of Environment, Malaysia; Department of Irrigation and Drainage, Malaysia; and Malaysian Metrological Department from July 2012 to December 2017. Multiple linear regression (MLR) and artificial neural network (ANN) were used to develop the prediction model of TB cases. The models that used sociodemographic variables as the input datasets were referred as MLR1 and ANN1, whereas environmental variables were represented as MLR2 and ANN2 and both sociodemographic and environmental variables together were indicated as MLR3 and ANN3.

Results

The ANN was found to be superior to MLR with higher adjusted coefficient of determination (R²) values in predicting TB cases; the ranges were from 0.35 to 0.47 compared to 0.07 to 0.14, respectively. The best TB prediction model, that is, ANN3 was derived from nationality, residency, income status, CO, NO₂, SO₂, PM₁₀, rainfall, temperature, and atmospheric pressure, with the highest adjusted R² value of 0.47, errors below 6, and accuracies above 96%.

Conclusions

It is envisaged that the application of the ANN algorithm based on both sociodemographic and environmental factors may enable a more accurate modeling for predicting TB cases.

Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia

Tuberculosis (TB) cases have increased drastically over the last two decades and it remains as one of the deadliest infectious diseases in Malaysia. This cross-sectional study aimed to establish the spatial distribution of TB cases and its association with the sociodemographic and environmental factors in the Gombak district. The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency, and smoking status from 1st January 2013 to 31st December 2017 in Gombak district were collected from the MyTB web and Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic. Environmental data consisting of air pollution such as air quality index (AQI), carbon monoxide (CO), nitrogen dioxide (NO₂), sulphur dioxide (SO₂), and particulate matter 10 (PM₁₀,) were obtained from the Department of Environment Malaysia from 1st July 2012 to 31st December 2017; whereas weather data such as rainfall were obtained from the Department of Irrigation and Drainage Malaysia and relative humidity, temperature, wind speed, and atmospheric pressure were obtained from the Malaysia Meteorological Department in the same period. Global Moran’s I, kernel density estimation, Getis-Ord Gi* statistics, and heat maps were applied to identify the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to determine the spatial association of sociodemographic and environmental factors with the TB cases. Spatial autocorrelation analysis indicated that the cases was clustered (p<0.05) over the five-year period and year 2016 and 2017 while random pattern (p>0.05) was observed from year 2013 to 2015. Kernel density estimation identified the high-density regions while Getis-Ord Gi* statistics observed hotspot locations, whereby consistently located in the southwestern part of the study area. This could be attributed to the overcrowding of inmates in the Sungai Buloh prison located there. Sociodemographic factors such as gender, nationality, employment status, health care worker status, income status, residency, and smoking status as well as; environmental factors such as AQI (lag 1), CO (lag 2), NO₂ (lag 2), SO₂ (lag 1), PM₁₀ (lag 5), rainfall (lag 2), relative humidity (lag 4), temperature (lag 2), wind speed (lag 4), and atmospheric pressure (lag 6) were associated with TB cases (p<0.05). The GWR model based on the environmental factors i.e. GWR2 was the best model to determine the spatial distribution of TB cases based on the highest R² value i.e. 0.98. The maps of estimated local coefficients in GWR models confirmed that the effects of sociodemographic and environmental factors on TB cases spatially varied. This study highlighted the importance of spatial analysis to identify areas with a high TB burden based on its associated factors, which further helps in improving targeted surveillance.

Latent Tuberculosis Infection among Healthcare Students and Postgraduates in a Mediterranean Italian Area: What Correlation with Work Exposure?

Background: Tuberculosis screening is part of the standard protocol for evaluating the risk of infection in healthcare workers. The aim of this study was to evaluate the prevalence of latent tuberculosis infection (LTBI) among students attending various healthcare profession degree courses and postgraduate medical courses at the School of Medicine of the University of Palermo, Italy, and assess the possible professional origin of infection. Methods: In total, 2946 students (2082 undergraduates and 864 postgraduates) took part in a screening program for LTBI between January 2014 to April 2019 using the tuberculin skin test (TST). Students with a positive TST result underwent a Quantiferon-TB test (QFT). Results: Among the 2082 undergraduates, 23 (1.1%) had a positive TST; the result was confirmed with QFT for 13 (0.62%) of them. Among the 864 postgraduate students, 24 (2.78%) had a positive TST and only 18 (2.08%) showed a positive QTF. Latent tuberculosis infections were significantly more frequent among postgraduates than undergraduates (2.08% > 0.62%, p < 0.0001). There was a higher number of subjects previously vaccinated for TB (18.87% > 0.24%, p < 0.0001), and of vaccinated subjects found positive for TST and QTF (66.67% > 7.69%, p = 0.001) in the postgraduate group. Conclusion: Latent TB is relatively low among medical school students in our geographic area. Nevertheless, this infectious disease must be regarded as a re-emerging biohazard for which preventive strategies are required to limit the risk of infection, especially among exposed workers.