Expression, Purification, and Characterization of Ras Protein (BmRas1) from Bombyx mori

The Ras subfamily is the member of small G proteins superfamily involved in cellular signal transduction. Activation of Ras signaling causes cell growth, differentiation, and survival. Bombyx mori Ras-like protein (BmRas1) may belong to the Ras subfamily. It contained an H-N-K-Ras-like domain. The BmRas1 mRNA consisted of 1459 bp. The open reading frame contained 579 bp, encoding 192 amino acids. The protein had such secondary structures as α-helices, extended strand, and random coil. BmRas1 was expressed successfully in E. coli BL21. The recombinant protein was purified with metal-chelating affinity chromatography. The GTPase activity of purified protein was determined by FeSO4-(NH4)2MoO4 assay. The results showed that purified recombinant protein had intrinsic activity of GTPase. High titer polyclonal antibodies were generated by New Zealand rabbit immunized with purified protein. The gene expression features of BmRas1 at different stages and in different organs of the fifth instar larvae were analyzed by Western blot. The results showed that BmRas1 was expressed highly in three development stages including egg, pupae, and adult, but low expression in larva. BmRas1 was expressed in these tissues including head, malpighian tubule, genital gland, and silk gland. The purified recombinant protein would be utilized to further function studies of BmRas1

Hyperprogressive disease in non-small cell lung cancer after PD-1/PD-L1 inhibitors immunotherapy: underlying killer

Immune checkpoint inhibitors (ICIs) target the negative regulatory pathway of T cells and effectively reactive the anti-tumor immune function of T cells by blocking the key pathway of the immune escape mechanism of the tumor—PD-1/PD-L1, and fundamentally changing the prospect of immunotherapy for non-small cell lung cancer patients. However, such promising immunotherapy is overshadowed by Hyperprogressive Disease, a response pattern associated with unwanted accelerated tumor growth and characterized by poor prognosis in a fraction of treated patients. This review comprehensively provides an overview of Hyperprogressive Disease in immune checkpoint inhibitor-based immunotherapy for non-small cell lung cancer including its definition, biomarkers, mechanisms, and treatment. A better understanding of the black side of immune checkpoint inhibitors therapy will provide a more profound insight into the pros and cons of immunotherapy

In Vivo Bioassay of Recombinant Human Growth Hormone Synthesized in B. mori Pupae

The human growth hormone (hGH) has been expressed in prokaryotic expression system with low bioactivity previously. Then the effective B. mori baculovirus system was employed to express hGH identical to mature hGH successfully in larvae, but the expression level was still limited. In this work, the hGH was expressed in B. mori pupae by baculovirus system. Quantification of recombinant hGH protein (BmrhGH) showed that the expression of BmrhGH reached the level of approximately 890 μg/mL pupae supernatant solution, which was five times more than the level using larvae. Furthermore, Animals were gavaged with BmrhGH at the dose of 4.5 mg/rat.day, and the body weight gain (BWG) of treated group had a significant difference (P < .01) compared with the control group. The other two parameters of liver weight and epiphyseal width were also found to be different between the two groups (P < .05). The results suggested that BmrhGH might be used as a protein drug by oral administration

Serum anti-Mullerian hormone predicts ovarian response in (Macaca fascicularis) monkeys

AMH as a promising predictor of ovarian response has been studied extensively in women undergoing assisted reproductive technology treatment, but little is known about its prediction value in monkeys undergoing ovarian stimulation. In the current study, a total of 380 cynomolgus monkeys ranging from 5 to 12 years received 699 ovarian stimulation cycles. Serum samples were collected for AMH measure with enzyme-linked immunosorbent assay. It was found that serum AMH levels were positive correlated with the number of retrieved oocytes (P < 0.01) in the first, second and third stimulation cycles. In the first cycles, area under the curve (ROCAUC) of AMH is 0.688 for low response and 0.612 for high response respectively, indicating the significant prediction values (P = 0.000 and P = 0.005). The optimal AMH cutoff value was 9.68 ng/mL for low ovarian response and 15.88 ng/mL for high ovarian response prediction. In the second stimulation cycles, the significance of ROCAUC of AMH for high response rather than the low response was observed (P = 0.001 and P = 0.468). The optimal AMH cutoff value for high ovarian response was 15.61 ng/mL. In the third stimulation cycles, AMH lost the prediction value with no significant ROCAUC. Our data demonstrated that AMH, not age, is a cycle-dependent predictor for ovarian response in form of oocyte yields, which would promote the application of AMH in assisted reproductive treatment (ART) of female cynomolgus monkeys. AMH evaluation would optimize candidate selection for ART and individualize the ovarian stimulation strategies, and consequentially improve the efficiency in monkeys

Erratum: Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

Interpretation: By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning

Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017

Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk–outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings In 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3–6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning

Global variations in diabetes mellitus based on fasting glucose and haemogloblin A1c

Fasting plasma glucose (FPG) and haemoglobin A1c (HbA1c) are both used to diagnose diabetes, but may identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening had elevated FPG, HbA1c, or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardised proportion of diabetes that was previously undiagnosed, and detected in survey screening, ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the agestandardised proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global gap in diabetes diagnosis and surveillance.peer-reviewe

Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017.

BACKGROUND: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk-outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk-outcome pairs, and new data on risk exposure levels and risk-outcome associations. METHODS: We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017