Can Sb(III)-oxidizing prokaryote also oxidize As(III) under aerobic and anaerobic conditions, and vice versa?

Sb(III) and As(III) share similar chemical features and coexist in the environment. However, their oxidase enzymes have completely different sequences and structures. This raises an intriguing question: Could Sb(III)-oxidizing prokaryotes (SOPs) also oxidize As(III), and vice versa? Regarding this issue, previous investigations have yielded unclear, incorrect and even conflicting data. This work aims to address this matter. First, we prepared an enriched population of SOPs that comprises 55 different AnoA genes, lacking AioAB and ArxAB genes. We found that these SOPs can oxidize both Sb(III) and As(III) with comparable capabilities. To further confirm this finding, we isolated three cultivable SOP strains that have AnoA gene, but lack AioAB and ArxAB genes. We observed that they also oxidize both Sb(III) and As(III) under both anaerobic and aerobic conditions. Secondly, we obtained an enriched population of As(III)-oxidizing prokaryotes (AOPs) from As-contaminated soils, which comprises 69 different AioA genes, lacking AnoA gene. We observed that the AOP population has significant As(III)-oxidizing activities, but lack detectable Sb(III)-oxidizing activities under both aerobic and anaerobic conditions. Therefore, we convincingly show that SOPs can oxidize As(III), but AOPs cannot oxidize Sb(III). These findings clarify the previous ambiguities, confusion, errors or contradictions regarding how SOPs and AOPs oxidize each other's substrate.

Fine particulate pollution driven by nitrate in the moisture urban atmospheric environment in the Pearl River Delta region of south China.

To identify potential sources of fine particles (PM2.5, with aerodynamic diameter (Da) ≤ 2.5 µm) in urban Dongguan of south China, a comprehensive campaign was carried out in the whole 2019. Hourly PM2.5 and its dominant chemical components including organic carbon (OC), elemental carbon (EC), water-soluble inorganic ions (WSIIs) and thirteen elements were measured using online instruments. Gaseous pollutants including NH3, HNO3, NO2, NO and O3 and meteorological parameters were also synchronously measured. PM2.5 was dominated by carbonaceous aerosols in summer and by WSIIs in the other seasons. PM2.5 and its dominant chemical components mostly peaked around noon (10:00-14:00 LST). Furthermore, high PM2.5 levels during the daytime were closely related with the increased NO3- levels. The high mass concentrations of NO3- in urban Dongguan during the daytime were likely related with regional transport of NO3- from suburban Dongguan, which was originated from the reaction between NO2 and O3 under the moisture condition during the nighttime. Seven major source factors for PM2.5 including secondary sulfate, ship emission, traffic emission, secondary nitrate, industrial processes, soil dust and coal combustion were identified by positive matrix factorization (PMF) analysis, which contributed 26 ± 14%, 16 ± 16%, 16 ± 10%, 14 ± 11%, 12 ± 11%, 8 ± 6% and 8 ± 6%, respectively, to annual PM2.5 mass concentration. Although secondary sulfate contributed much more than secondary nitrate to PM2.5 on annual basis, the latter exceeded the former source factor when daily PM2.5 mass concentration was higher than 60 µg m-3, indicating the critical role nitrate played in PM2.5 episode events.

[Characteristics and Source Apportionment of Volatile Organic Compounds (VOCs) in a Typical Industrial Area in Dongguan During Periods of Ozone and Non-ozone Pollution in Summer].

To investigate the characteristics and sources of atmospheric volatile organic compounds (VOCs) in a typical industrial zone in Dongguan, 56 VOCs species were continuously measured in Houjie Town of Dongguan in summer of 2020. In addition, mass concentrations of O3, NOx, and CO and meteorological data were synchronously collected. Then, characteristics of total VOCs and major species, the contributions of major VOCs species to ozone formation potential (OFP), and source apportionment of VOCs under the different ozone concentrations were discussed. The mean mixing ratio of VOCs was 53.1×10-9 including aromatics (24.7×10-9), alkanes (23.7×10-9), alkenes (3.9×10-9), and alkynes (0.7×10-9). The mean mixing ratios of aromatics, alkanes, alkenes, and alkynes increased approximately 10%, 43%, 38%, and 98% during the period of ozone pollution, respectively, compared with those during the period of non-ozone pollution. Aromatics contributed the most to OFP during the periods of both ozone pollution and non-ozone pollution, followed by alkanes, alkenes, and alkynes. Solvent sources, liquefied petroleum gas (LPG) leakage, fossil fuel combustion, and hydrocarbon volatilization were resolved using the PMF model, which accounted for 60%±20%, 16%±11%, 15%±11%, and 9%±6% of total VOCs, respectively. During the period of ozone pollution, the contribution of solvent sources to the total VOCs decreased to 44%, whereas that of LPG leakage and hydrocarbon volatilization increased to 21% and 16%, respectively.

Can We Reliably Identify the Pathological Outcomes of Neoadjuvant Chemotherapy in Patients with Breast Cancer? Development and Validation of a Logistic Regression Nomogram Based on Preoperative Factors.

BACKGROUND: Pathological responses of neoadjuvant chemotherapy (NCT) are associated with survival outcomes in patients with breast cancer. Previous studies constructed models using out-of-date variables to predict pathological outcomes, and lacked external validation, making them unsuitable to guide current clinical practice. OBJECTIVE: The aim of this study was to develop and validate a nomogram to predict the objective remission rate (ORR) of NCT based on pretreatment clinicopathological variables. METHODS: Data from 110 patients with breast cancer who received NCT were used to establish and calibrate a nomogram for pathological outcomes based on multivariate logistic regression. The predictive performance of this model was further validated using a second cohort of 55 patients with breast cancer. Discrimination of the prediction model was assessed using an area under the receiver operating characteristic curve (AUC), and calibration was assessed using calibration plots. The diagnostic odds ratio (DOR) was calculated to further evaluate the performance of the nomogram and determine the optimal cut-off value. RESULTS: The final multivariate regression model included age, NCT cycles, estrogen receptor, human epidermal growth factor receptor 2 (HER2), and lymphovascular invasion. A nomogram was developed as a graphical representation of the model and showed good calibration and discrimination in both sets (an AUC of 0.864 and 0.750 for the training and validation cohorts, respectively). Finally, according to the Youden index and DORs, we assigned an optimal ORR cut-off value of 0.646. CONCLUSION: We developed a nomogram to predict the ORR of NCT in patients with breast cancer. Using the nomogram, for patients who are operable and whose ORR is < 0.646, we believe that the benefits of NCT are limited and these patients can be treated directly using surgery.

The incidence and survival rate of population-based pancreatic cancer patients: Shanghai Cancer Registry 2004-2009.

BACKGROUND: Pancreatic cancer is a devastating disease with dismal prognosis. Large population-based evidence on its survival rate and influence factors is lacking in China. OBJECTIVE: This study aimed to depict the demographic factors, tumor characteristics, incidence rate and survival rate of pancreatic cancer cases in urban China. METHODS: The demographic factors, tumor characteristics were collected for all the pancreatic cancer cases identified during 2004 to 2009 from the Shanghai Cancer Registry. The survival time was ascertained through linkage of the Shanghai Cancer Registry and the Shanghai Vital Statistics Registry. The deadline of death certificates was the end of December 2012. Kaplan-Meier method and Cox proportional-hazards regression model were used to explore the survival rate and influence factors. RESULTS: 11,672 new pancreatic cancer cases were identified among Shanghai residency during 2004 to 2009. The crude incidence rate of pancreatic cancer was increasing from 12.80/100,000 in 2004 to 15.66/100,000 in 2009, while the standardized incidence rate was about 6.70/100,000 and didn't change a lot. The overall 5-year survival rate was 4.1% and the median survival time was 3.9 (95% Confidence Interval (CI) 3.8-4.0) months. Subjects had received surgical resection had improved survival (HR  = 0.742, 95% CI: 0.634-0.868) than its counterparts. In adjusted multivariable Cox proportional-hazard models, factors associated with poor survival included older age at diagnosis (age > = 70 years: hazard ratio (HR)  = 1.827, 95% CI: 1.614-2.067), male sex (HR  = 1.155, 95% CI: 1.041-1.281), distant disease at diagnosis (HR =  1.257, 95% CI: 1.061-1.488), positive lymph node (HR  = 1.236, 95% CI: 1.085-1.408), tumor stage (Stage IV HR  = 2.817, 95% CI: 2.029-3.909). CONCLUSION: The age-adjusted incidence rate was stable and overall survival rate was low among pancreatic cancer patients of Shanghai residency. Early detection and improved treatment strategies are needed to improve prognosis for this deadly disease.