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Reliable data on air pollution are fundamental to understanding and taking corrective action to improve air quality. In addition, where data are shared with the public, everyone across private, public and civil society can innovate, collaborate, and apply effective solutions towards clean air. However, air quality (AQ) monitoring, as well as AQ data sharing, are both limited in many low- and middle-income countries. While a few reviews of air quality in Africa and South/Southeast Asia exist, a review focused on monitoring capabilities and data sharing is necessary, especially with growth in non-traditional, non-reference monitoring technologies. We conducted a scoping review based on the Arksey and O’Malley methodological framework along with updates proposed in literature consistent with the PRISMA-ScR (Scoping Review Extension). We implemented a search strategy that was iteratively refined to review diverse sources including scientific and gray literature, air quality data aggregators, and publicly internet-available documents of national governments. We complemented our findings by consulting with experts. We found that AQ monitoring in Africa and South/Southeast Asia is lacking, with very few countries having reference-grade monitoring programs and few sharing the data they collect publicly. We recommend leveraging emerging lower-cost alternatives along with traditional (“reference”) technologies. This is supported by our review of emerging approaches and highlighted case studies of countries like Uganda and Cambodia that have adopted such approaches.

The study aimed to evaluate the beta penalization factor of the BSREM reconstruction algorithm on a five-ring BGO-based PET CT system and compared it with conventional reconstructions.Retrospective study involves 30 breast cancer patient data of 18F-fluorodeoxyglucose ( 18 F-FDG) PET CT for reconstruction with OSEM, OSEM + PSF, and BSREM under variable β factors ranging from 200 to 600 in the steps of 50. Liver noise, lesion SUVmax, SBR, and SNR for each reconstruction were calculated. Quantitative parameters of each beta factor of BSREM were compared with OSEM and OSEM + PSF, using the Wilcoxon sign rank test with Bonferroni correction, a value of P0.002 was considered statistically significant. Visual scoring by two readers was also evaluated.Thirty lesions of mean size 1.91 ± 0.58 cm range (0.7-3.6 cm) were identified. Liver noise and SBR were reduced, whereas SNR was increased with an increasing β value of BSREM. In comparison with OSEM, liver noise was not significantly different from β200 and β250. SNR of OSEM was significantly lower than any other β factors and SBR of β factor less than 500 was significantly higher than OSEM. In comparison with OSEM + PSF, liver noise was not significantly different from β400 and β350-500 do not show a significant difference in SNR and SBR compared with OSEM + PSF. β350 scored highest under visual scoring with a moderate agreement.The study quantitatively indicates the optimum beta range of β250-450 and the qualitative evaluation indicates that β350 is an optimum beta factor of BSREM in breast cancer cases for 18 F-FDG WB-PET CT.

Aim: To estimate the standard uptake values (SUVs) of Tc-99m methylene-diphosphonate (Tc-99m MDP) from normal skeletal sites in breast cancer patients using quantitative single-photon emission computed tomography (SPECT).Materials and Methods: A total of 60 breast cancer patients who underwent Tc-99m MDP SPECT/CT study at different postinjection acquisition times were included in this study. Based on postinjection acquisition time, patients were divided into four study groups (n_15 each), i.e. I st (2 h), II nd (3 h), III rd (4 h), and IV th (5 h). Image quantification (SUVmax and SUVmean) was performed using Q.Metrix software. Delineation of volume of interest was shaped around different bones of the skeletal system. Results: The highest normal SUVmax and SUVmean values were observed in lumber and thoracic vertebra (8.89 ± 2.26 and 2.89 ± 0.58) for Group I and in pelvis and thoracic (9.6 ± 1.32 and 3.04 ± 0.64), (10.93 ± 3.91 and 3.65 ± 0.97), (11.33 ± 2.67 and 3.65 ± 0.22) for Group II, III and IV, respectively. Lowest normal SUVmax and SUVmean values were observed in humerus and ribs (3.22 ± 0.67 and 0.97 ± 0.18), (5.16 ± 1.82 and 1.18 ± 0.16) for Group I, IV, and in humerus (3.17 ± 0.58 and 0.85 ± 0.26), (3.98 ± 1.12 and 1.04 ± 0.28) for Group II and III, respectively. Significant difference ( P < 0.05) noted in SUVmax for sternum, cervical, humerus, ribs, and pelvis with respect to time. However, significant difference ( P < 0.05) noted in SUVmean for all skeletal sites with respect to time. Conclusions: Our study shows variability in normal SUV values for different skeletal sites in breast cancer patients. Vertebral bodies and pelvis contribute highest SUV values. Time dependency of SUVs emphasizes the usefulness of routinely acquired images at the same time after Tc-99m MDP injection, especially in follow-up studies.