Specific imaging of CD8 + T-Cell dynamics with a nanobody radiotracer against human CD8ß.

PURPOSE: While immunotherapy has revolutionized the oncology field, variations in therapy responsiveness limit the broad applicability of these therapies. Diagnostic imaging of immune cell, and specifically CD8+ T cell, dynamics could allow early patient stratification and result in improved therapy efficacy and safety. In this study, we report the development of a nanobody-based immunotracer for non-invasive SPECT and PET imaging of human CD8+ T-cell dynamics. METHODS: Nanobodies targeting human CD8ß were generated by llama immunizations and subsequent biopanning. The lead anti-human CD8ß nanobody was characterized on binding, specificity, stability and toxicity. The lead nanobody was labeled with technetium-99m, gallium-68 and copper-64 for non-invasive imaging of human T-cell lymphomas and CD8+ T cells in human CD8 transgenic mice and non-human primates by SPECT/CT or PET/CT. Repeated imaging of CD8+ T cells in MC38 tumor-bearing mice allowed visualization of CD8+ T-cell dynamics. RESULTS: The nanobody-based immunotracer showed high affinity and specific binding to human CD8 without unwanted immune activation. CD8+ T cells were non-invasively visualized by SPECT and PET imaging in naïve and tumor-bearing mice and in naïve non-human primates with high sensitivity. The nanobody-based immunotracer showed enhanced specificity for CD8+ T cells and/or faster in vivo pharmacokinetics compared to previous human CD8-targeting immunotracers, allowing us to follow human CD8+ T-cell dynamics already at early timepoints. CONCLUSION: This study describes the development of a more specific human CD8+ T-cell-targeting immunotracer, allowing follow-up of immunotherapy responses by non-invasive imaging of human CD8+ T-cell dynamics.

Tumor-associated alterations in white matter connectivity have prognostic significance in MGMT-unmethylated glioblastoma.

PURPOSE: We investigated the prognostic significance of tumor-associated white matter (TA-WM) tracts in glioblastoma (GBM) using magnetic resonance-diffusion tensor imaging (MR-DTI). We hypothesized that (1) TA-WM tracts harbor microscopic disease not targeted through surgery or radiotherapy (RT), and (2) the greater the extent of TA-WM involvement, the worse the survival outcomes. METHODS: We studied a retrospective cohort of 76 GBM patients. TA-WM tracts were identified by MR-DTI fractional anisotropy (FA) maps. For each patient, 22 TA-WM tracts were analyzed and each tract was graded 1-3 based on FA. A TA-WM score (TA-WMS) was computed based on number of involved tracts and corresponding FA grade of involvement. Kaplan-Meier statistics were utilized to determine survival outcomes, log-rank test was used to compare survival between groups, and Cox regression was utilized to determine prognostic variables. RESULTS: For the MGMT-unmethylated cohort, there was a decrease in OS for increasing TA-WMS (median OS 16.5 months for TA-WMS 0-4; 13.6 months for TA-WMS 5-8; 7.3 months for TA-WMS > 9; p = 0.0002). This trend was not observed in the MGMT-methylated cohort. For MGMT-unmethylated patients with TA-WMS > 6 and involvement of tracts passing through brainstem or contralateral hemisphere, median OS was 8.3 months versus median OS 14.1 months with TA-WMS > 6 but not involving aforementioned critical tracts (p = 0.003 log-rank test). For MGMT-unmethylated patients, TA-WMS was predictive of overall survival in multivariate analysis (HR = 1.14, 95% CI 1.03-1.27, p = 0.012) while age, gender, and largest tumor dimension were non-significant. CONCLUSION: Increased TA-WMS and involvement of critical tracts are associated with decreased overall survival in MGMT-unmethylated GBM.

Source Apportionment of PM10 Over Three Tropical Urban Atmospheres at Indo-Gangetic Plain of India: An Approach Using Different Receptor Models.

The present work is the ensuing part of the study on spatial and temporal variations in chemical characteristics of PM10 (particulate matter with aerodynamic diameter ≤ 10 µm) over Indo Gangetic Plain (IGP) of India. It focuses on the apportionment of PM10 sources with the application of different receptor models, i.e., principal component analysis with absolute principal component scores (PCA-APCS), UNMIX, and positive matrix factorization (PMF) on the same chemical species of PM10. The main objective of this study is to perform the comparative analysis of the models, obtained mutually validated outputs and more robust results. The average PM10 concentration during January 2011 to December 2011 at Delhi, Varanasi, and Kolkata were 202.3 ± 74.3, 206.2 ± 77.4, and 171.5 ± 38.5 µg m-3, respectively. The results provided by the three models revealed quite similar source profile for all the sampling regions, with some disaccords in number of sources as well as their percent contributions. The PMF analysis resolved seven individual sources in Delhi [soil dust (SD), vehicular emissions (VE), secondary aerosols (SA), biomass burning (BB), sodium and magnesium salt (SMS), fossil fuel combustion, and industrial emissions (IE)], Varanasi [SD, VE, SA, BB, SMS, coal combustion, and IE], and Kolkata [secondary sulfate (Ssulf), secondary nitrate, SD, VE, BB, SMS, IE]. However, PCA-APCS and UNMIX models identified less number of sources (besides mixed type sources) than PMF for all the sampling sites. All models identified that VE, SA, BB, and SD were the dominant contributors of PM10 mass concentration over the IGP region of India.

Inter-annual Variation of Ambient Ammonia and Related Trace Gases in Delhi, India.

In this study, ambient NH3, NO, NO2, CO and SO2 were measured continuously from February, 2008, to December, 2016. The annual average mixing ratios (mole/mole) of NH3, NO, NO2, CO and SO2 were 17.8 ± 3.4 ppb; 21.2 ± 2.3 ppb, 18.1 ± 3.2 ppb; 1.7 ± 0.3 ppm and 2.0 ± 0.3 ppb, respectively. All the trace gases (NH3, NO, NO2, CO and SO2) showed significant annual variation during the study. A significant increasing trend in mixing ratios of ambient NH3 and NO2 were observed at the observational site of Delhi, whereas, increasing trend were recorded in case of NO, CO and NO2 mixing ratios. The results emphasized that traffic could be one of the significant sources of ambient NH3 at the urban site of Delhi, as illustrated by positive correlations of NH3 with traffic related co-pollutants (NO and CO).

Source Apportionment of PM2.5 in Delhi, India Using PMF Model.

Chemical characterization of PM2.5 [organic carbon, elemental carbon, water soluble inorganic ionic components, and major and trace elements] was carried out for a source apportionment study of PM2.5 at an urban site of Delhi, India from January, 2013, to December, 2014. The annual average mass concentration of PM2.5 was 122 ± 94.1 µg m(-3). Strong seasonal variation was observed in PM2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon. A receptor model, positive matrix factorization (PMF) was applied for source apportionment of PM2.5 mass concentration. The PMF model resolved the major sources of PM2.5 as secondary aerosols (21.3 %), followed by soil dust (20.5 %), vehicle emissions (19.7 %), biomass burning (14.3 %), fossil fuel combustion (13.7 %), industrial emissions (6.2 %) and sea salt (4.3 %).

Variation of Stable Carbon and Nitrogen Isotopic Composition of PM10 at Urban Sites of Indo Gangetic Plain (IGP) of India.

This paper presents the variation of elemental concentrations of total carbon (TC), total nitrogen (TN) and isotopic ratios of δ13C and δ15N along with δ13OC and OC of PM10 mass over Delhi, Varanasi and Kolkata of the Indo Gangetic Plain (IGP), India. For Delhi, the average concentrations of TC and TN of PM10 were 53.0±33.6 and 14.9±10.8 µg m(-3), whereas δ13C and δ15N of PM10 were -25.5±0.5 and 9.6±2.8‰, respectively. For Varanasi, the average values of δ13C and δ15N of PM10 were -25.4±0.8 and 6.8±2.4‰, respectively. For Kolkata, TC and TN values for PM10 ranged from 9.1-98.2 to 1.4-25.9 µg m(-3), respectively with average values of 32.6±24.9 and 9.3±8.2 µg m(-3), respectively. The average concentrations of δ13C and δ15N were -26.0±0.4 and 7.4±2.7‰, respectively over Kolkata with ranges of -26.6 to -24.9‰ and 2.8±11.5‰, respectively. The isotopic analysis revealed that biomass burning, vehicular emission and secondary inorganic aerosols were likely sources of PM10 mass over IGP, India.

Treating low- and intermediate-risk acute promyelocytic leukemia with and without chemotherapy: A comparison in a tertiary care center.

Background: Acute promyelocytic leukemia (APL) comprises approximately 10% of acute myeloid leukemia (AML) cases. Material and Methods: Both options of treatment (ATRA-ATO and ATRA-chemotherapy) were discussed with patients with low- and intermediate-risk APL, pros and cons explained in details, and treatment regimen selected after getting informed written consent. Results: Total 71 patients were included in the study; among these patients, 3 were negative for both FISH for t (15,17) and RT-PCR for promyelocytic leukemia retinoic acid receptor alpha, and 36 patients with APL had white blood cell count at diagnosis >10 × 109/l. Total 30 patients with newly diagnosed as low- and intermediate-risk-APL fulfilled all inclusion criteria, treated and followed for a minimum period of 2 years up to June, 2016. Fifteen patients liked to be treated with all-trans retinoic acid (ATRA) and arsenic trioxide (ATO), while rest of the 15 patients preferred treatment with ATRA and chemotherapy. Conclusion: Combination of ATRA and ATO is equally effective, less toxic, and more feasible in comparison to ATRA and chemotherapy for patients with low- and intermediate-risk APL and is a viable option for this subset of patients, especially in countries with limited resources.

A review on morphology, nanostructure, chemical composition, and number concentration of diesel particulate emissions.

Particulate matter (PM) emitted from compression ignition (CI) engines (diesel engines) has a detrimental effect on human health and the environment. The physical and chemical characteristics of PM emitted from CI-engines are influenced by engine operating conditions and fuel properties. The morphology, nanostructure, and chemical composition of PM affect its toxicity and interaction with the environment. From automotive industry perspective, these parameters influence the design of diesel particulate filters. This study presents a review of the physical and chemical characteristics of particulate emissions from the CI-engine. The present study commences with a brief description about the composition of PM emitted from CI-engine and the PM formation mechanism in CI-engine. Later on, the detailed review of PM's physical and chemical characteristics and the effect of engine operating parameters and alternative fuels on the particle number concentration, morphology, nano-structure, and oxidative reactivity of PM is presented. Online and offline methods of diesel particulate characterization and emerging chemical characterization techniques such as X-ray photoelectron spectroscopy and X-ray absorption fine structure (EXAFS) are also discussed briefly. Correlation between physical and chemical properties, and oxidative reactivity of PM is also discussed. It was found that engine operating parameters affect the physical and chemical properties of PM. Use of alternative fuels changes the diesel particulate morphology, nanostructure, and chemical composition which enhances the oxidative reactivity of PM.

Chemical properties of emissions from solid residential fuels used for energy in the rural sector of the southern region of India.

In the present study, we have estimated the emission factors (EFs) of particulate matter (PM), organic and elemental carbon (OC and EC), oxide of sulfur and nitrogen, and water-soluble ionic species emitted from residential fuels (fuelwood, crop residue, dung cake) used in the rural sector of five states (Kerala, Karnataka, Andhra Pradesh, Telangana, Tamil Nadu) of the southern region of India. Average EFs of PM, OC, and EC from fuelwood (FW), crop residues (CR), and dung cakes (DC) from southern region of India are estimated as follows: PM: 6.35 ± 5.64 g/kg (FW), 6.99 ± 5.46 g/kg (CR), 9.69 ± 3.73 g/kg (DC); OC: 1.60 ± 1.72 g/kg (FW), 1.50 ± 1.52 g/kg (CR), 3.54 ± 0.75 g/kg (DC); and EC: 0.46 ± 0.53 g/kg (FW), 0.29 ± 0.17 g/kg (CR), 0.21 ± 0.11 g/kg (DC), respectively. Similarly, the average EFs of SO2, NOx from FW, CR, and DC are determined to be as follows: SO2: 0.40 ± 0.37 g/kg (FW), 1.17 ± 0.25 g/kg (CR), and 0.18 ± 0.10 g/kg (DC); NOx: 1.11 ± 1.22 g/kg (FW), 0.69 ± 0.37 g/kg (CR), and 0.91 ± 0.54 g/kg (DC), respectively. PO43- shows the highest EF from FW (646.02 ± 576.35 mg/kg), CR (531.06 ± 678.29 mg/kg) among all anions followed by Cl- (FW: 512.91 ± 700.35 mg/kg, CR: 661.61 ± 865.46 mg/kg and DC: 104.16 ± 54.01 mg/kg); whereas, Na+ shows highest EF from FW (254.05 ± 298.50 mg/kg) and CR (249.36 ± 294.85 mg/kg) among all cations. The total emissions of trace gases, PM, and their chemical composition from FW, CR, and DC have been calculated using laboratory-generated EFs over the southern region of India. CR (1595.58 ± 14.24 Gg) contributes to higher emission of PM as compared to FW (218.78 ± 53.93 Gg), whereas the contribution from DC is negligible.

For a critical appraisal of artificial intelligence in healthcare: The problem of bias in mHealth.

RATIONALE, AIMS AND OBJECTIVES: Artificial intelligence and big data are more and more used in medicine, either in prevention, diagnosis or treatment, and are clearly modifying the way medicine is thought and practiced. Some authors argue that the use of artificial intelligence techniques to analyze big data would even constitute a scientific revolution, in medicine as much as in other scientific disciplines. Moreover, artificial intelligence techniques, coupled with mobile health technologies, could furnish a personalized medicine, adapted to the individuality of each patient. In this paper we argue that this conception is largely a myth: what health professionals and patients need is not more data, but data that are critically appraised, especially to avoid bias. METHODS: In this historical and conceptual article, we focus on two main problems: first, the data and the problem of its validity; second, the inference drawn from the data by AI, and the establishment of correlations through the use of algorithms. We use examples from the contemporary use of mobile health (mHealth), i.e. the practice of medicine and public health supported by mobile or wearable devices such as mobile phones or smart watches. RESULTS: We show that the validity of the data and of the inferences drawn from these mHealth data are likely to be biased. As biases are insensitive to the size of the sample, even if the sample is the whole population, artificial intelligence and big data cannot avoid biases and even tend to increase them. CONCLUSIONS: The large amount of data thus appears rather as a problem than a solution. What contemporary medicine needs is not more data or more algorithms, but a critical appraisal of the data and of the analysis of the data. Considering the history of epidemiology, we propose three research priorities concerning the use of artificial intelligence and big data in medicine.