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1.
Chih-Yen Lin Aspiro Nayim Urbina Wen-Hung Wang Arunee Thitithanyanont Sheng-Fan Wang 《Viruses》2022,14(7)
Viral assembly and budding are the final steps and key determinants of the virus life cycle and are regulated by virus–host interaction. Several viruses are known to use their late assembly (L) domains to hijack host machinery and cellular adaptors to be used for the requirement of virus replication. The L domains are highly conserved short sequences whose mutation or deletion may lead to the accumulation of immature virions at the plasma membrane. The L domains were firstly identified within retroviral Gag polyprotein and later detected in structural proteins of many other enveloped RNA viruses. Here, we used HIV-1 as an example to describe how the HIV-1 virus hijacks ESCRT membrane fission machinery to facilitate virion assembly and release. We also introduce galectin-3, a chimera type of the galectin family that is up-regulated by HIV-1 during infection and further used to promote HIV-1 assembly and budding via the stabilization of Alix–Gag interaction. It is worth further dissecting the details and finetuning the regulatory mechanism, as well as identifying novel candidates involved in this final step of replication cycle. 相似文献
2.
OBJECTIVE
To estimate how many U.S. adults with diabetes would be eligible for individualized A1C targets based on 1) the 2012 American Diabetes Association (ADA) guideline and 2) a published approach for individualized target ranges.RESEARCH DESIGN AND METHODS
We studied adults with diabetes ≥20 years of age from the National Health and Nutrition Examination Survey 2007–2008 (n = 757). We assigned A1C targets based on duration, age, diabetes-related complications, and comorbid conditions according to 1) the ADA guideline and 2) a strategy by Ismail-Beigi focused on setting target ranges. We estimated the number and proportion of adults with each A1C target and compared individualized targets to measured levels.RESULTS
Using ADA guideline recommendations, 31% (95% CI 27–34%) of the U.S. adult diabetes population would have recommended A1C targets of <7.0%, and 69% (95% CI 66–73%) would have A1C targets less stringent than <7.0%. Using the Ismail-Beigi strategy, 56% (51–61%) would have an A1C target of ≤7.0%, and 44% (39–49%) would have A1C targets less stringent than <7.0%. If a universal A1C <7.0% target were applied, 47% (41–54%) of adults with diabetes would have inadequate glycemic control; this proportion declined to 30% (26–36%) with the ADA guideline and 31% (27–36%) with the Ismail-Beigi strategy.CONCLUSIONS
Using individualized glycemic targets, about half of U.S. adults with diabetes would have recommended A1C targets of ≥7.0% but one-third would still be considered inadequately controlled. Diabetes research and performance measurement goals will need to be revised in order to encourage the individualization of glycemic targets.For nearly a decade, diabetes care guidelines from the American Diabetes Association (ADA) have recommended that the goal of glycemic control should be to lower the A1C to <7.0% for adults living with diabetes (1). This recommendation currently motivates diabetes public health programs and diabetes care translational research. All of these efforts have the overall intention of shifting the national distribution of A1C levels downward in order to improve diabetes outcomes and may lead to overtreatment of A1C levels in certain diabetes populations.Although the standard A1C target of <7.0% is probably the best-known feature of the ADA guidelines, the ADA guidelines also recommend that A1C targets should be based on individual clinical circumstances. Similar recommendations for individualized targets have been supported by the Veterans Health Administration-Department of Defense (VA-DoD), American Geriatric Society, American College of Physicians (ACP), and American Association of Clinical Endocrinologists (AACE) (2–5). Recommendations to individualize targets are based on major type 2 diabetes trials that found different levels of benefit, and even harm, from lower A1C levels depending on diabetes population characteristics (e.g., duration of diabetes, age, and comorbidity) (6–10). According to the ADA, lower A1C targets are recommended for patients with a short duration of diabetes, long life expectancy, and no significant cardiovascular disease (1). Conversely, higher A1C targets are recommended for patients with longstanding diabetes, advanced age, limited life expectancy, a history of macrovascular or advanced microvascular complications, extensive comorbidities, or a high risk for severe hypoglycemia (1–5). Although guidelines have identified these special populations, recommendations on how to set individualized A1C targets have been open to interpretation.Recently, a formal strategy for individualizing targets was published by Ismail-Beigi et al. (11). Similar to diabetes care guidelines, this strategy was based on expert interpretation of outcomes from prominent diabetes trials, including the U.K. Prospective Diabetes Study (UKPDS), Action to Control Cardiovascular Risk in Diabetes (ACCORD), Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified-Release Control Evaluation (ADVANCE), and Veterans Affairs Diabetes Trial (VADT) (6–10). The Ismail-Beigi strategy used the same clinical characteristics proposed in previous guidelines from the VA-DoD, American Geriatric Society, and ACP (e.g., age, duration of diabetes, history of macrovascular and microvascular complications, comorbidity, and psychosocioeconomic context). Based on their strategy, only adults 20–44 years of age with no history of diabetes-related complications would be recommended an A1C target of ≤6.5%, and several populations are recommended individualized A1C targets above the conventional ADA threshold of <7.0%, including adults 45–65 years of age with established macrovascular or advanced microvascular complications, adults >65 years of age with longstanding diabetes or established macrovascular or advanced microvascular complications, and all adults with advanced age. Additionally, because the Ismail-Beigi strategy suggested ranges of glycemic targets (i.e., ∼7, 7.0–8.0, or ∼8.0%), there exists the potential that some patients who could safely tolerate lower glycemic targets may be undertreated in order to stay within range.These recent calls for greater individualization of A1C targets raise fundamental public health questions. The degree to which the individualization of diabetes care is regarded as important depends on how many U.S. adults with diabetes may be candidates for A1C targets more or less stringent than the conventional target of <7.0%. Previous assessments of diabetes care quality have used population-level A1C thresholds to judge the quality of care (12–14); however, the diabetes care quality may differ from previous reports using these newer standards of individualization (15). In order to understand the potential impact of the individualization of glycemic targets on diabetes care quality, we characterized the U.S. adult diabetes population by clinical variables that have been proposed as reasons to individualize A1C targets. We then operationalized the ADA and Ismail-Beigi strategies for individualization to estimate 1) the distribution of the U.S. adult diabetes population across each individualized A1C target and 2) the size of the population who have measured A1C levels that are at or below their recommended individualized A1C target. 相似文献3.
Typing (A/B) and subtyping (H1/H3/H5) of influenza A viruses by multiplex real-time RT-PCR assays 总被引:1,自引:0,他引:1
Suwannakarn K Payungporn S Chieochansin T Samransamruajkit R Amonsin A Songserm T Chaisingh A Chamnanpood P Chutinimitkul S Theamboonlers A Poovorawan Y 《Journal of virological methods》2008,152(1-2):25-31
In this study, a specific and sensitive one-step multiplex real-time RT-PCR was developed in two assays by using primers and a number of specific locked nucleic acid (LNA)-mediated TaqMan probes which increase the thermal stability of oligonucleotides. The first assay consisted of primers and probes specific to the matrix (M1) gene of influenza A virus, matrix (M1) gene of influenza B virus and GAPDH gene of host cells for typing of influenza virus and verification by an internal control, respectively. The other assay employed primers and probes specific to the hemagglutinin gene of H1, H3 and H5 subtypes in order to identify the three most prominent subtypes of influenza A capable of infecting humans. The specificity results did not produce any cross reactivity with other respiratory viruses or other subtypes of influenza A viruses (H2, H4 and H6-H15), indicating the high specificity of the primers and probes used. The sensitivity of the assays which depend on the type or subtype being detected was approximately 10 to 10(3)copies/microl that depended on the types or subtypes being detected. Furthermore, the assays demonstrated 100% concordance with 35 specimens infected with influenza A viruses and 34 specimens infected with other respiratory viruses, which were identified by direct nucleotide sequencing. In conclusion, the multiplex real-time RT-PCR assays have proven advantageous in terms of rapidity, specificity and sensitivity for human specimens and thus present a feasible and attractive method for large-scale detection aimed at controlling influenza outbreaks. 相似文献
4.
Sathit Pichyangkul Somporn Krasaesub Anan Jongkaewwattana Arunee Thitithanyanont Suwimon Wiboon-ut Kosol Yongvanitchit Amporn Limsalakpetch Utaiwan Kum-Arb Duangrat Mongkolsirichaikul Nuanpan Khemnu Rangsini Mahanonda Jean-Michel Garcia Carl J. Mason Douglas S. Walsh David L. Saunders 《The American journal of tropical medicine and hygiene》2014,90(1):149-152
We studied cross-reactive antibodies against avian influenza H5N1 and 2009 pandemic (p) H1N1 in 200 serum samples from US military personnel collected before the H1N1 pandemic. Assays used to measure antibodies against viral proteins involved in protection included a hemagglutination inhibition (HI) assay and a neuraminidase inhibition (NI) assay. Viral neutralization by antibodies against avian influenza H5N1 and 2009 pH1N1 was assessed by influenza (H5) pseudotyped lentiviral particle-based and H1N1 microneutralization assays. Some US military personnel had cross-neutralizing antibodies against H5N1 (14%) and 2009 pH1N1 (16.5%). The odds of having cross-neutralizing antibodies against 2009 pH1N1 were 4.4 times higher in subjects receiving more than five inactivated whole influenza virus vaccinations than those subjects with no record of vaccination. Although unclear if the result of prior vaccination or disease exposure, these pre-existing antibodies may prevent or reduce disease severity.Outbreaks of 1997 avian influenza H5N1 and 2009 pandemic (p) H1N1 in humans have provided an opportunity to gain insight into cross-reactive immunity. The US military periodically collects and stores serum samples from service members linked to medical records.1 We measured cross-reactive antibodies in stored serum to avian influenza H5N1 and 2009 pH1N1 from US military personnel and identified factors associated with presence of neutralizing antibodies.Two hundred archived serum samples were obtained from the US Department of Defense Serum Repository. They were representative of a wide cross-section of active military personnel at the times of collection, whereas specific geographic information was not available on the individual selected; the cohort represents the general US military population, which is deployed throughout the United States and globally. Fifty samples each were selected from four birth cohorts: (1) < 1949, (2) 1960–1965, (3) 1966–1971, and (4) 1972–1977. Within each cohort, 25 samples were collected in the year 2000 (before the introduction of intranasal live attenuated influenza vaccine [LAIV]), and 25 samples were collected in 2008 (where 51% of donors had received LAIV). It has been suggested that LAIV elicits cross-reactive immunity.2,3 The samples were all collected before the outbreak of 2009 pH1N1, and there have not been any reported outbreaks of H5N1 in US military personnel.Assays used to measure antibodies included a hemagglutination inhibition (HI) assay and a neuraminidase inhibition (NI) assay.4 Viral neutralization by antibodies against H5N1 and 2009 pH1N1 was assessed by influenza (H5) pseudotyped lentiviral particle-based (H5pp)5 and microneutralization assays, respectively. Electronic medical and vaccination records from the Defense Medical Surveillance System (DMSS), which captured records before the serum sample date, were linked to samples and compared with the in vitro results.1The odds ratios (ORs) and 95% confidence intervals (95% CIs) of univariate and multivariate binary logistic regression analyses were used to determine the association between donor characteristics and positive antibody responses. A multiple logistic regression model was constructed, and it included independent variables with a P value of < 0.05 in univariate logistic regression. A P value of < 0.05 was considered to indicate statistical significance. SPSS 12.0 for Windows (SPSS Inc., Chicago, IL) was used to perform all statistical analysis.Cross-reactivity is summarized in 5 and 22.5% for the NI assay. H5pp and NI antibody titers to H5N1 were evenly distributed among birth cohorts and did not differ substantially based on history of vaccination or prior respiratory infections. Of those individuals with neutralizing antibodies to H5N1 (N = 28), 32.1% also had neutralizing antibodies to pH1N1, whereas 19.3% of those individuals with any H5N1-specific antibody response also had neutralizing antibodies to pH1N1 (Characteristics (n) H5N1 2009 pH1N1§ HI assay* % positive (GM titer) H5pp† % positive (GM titer) NI assay‡ % positive (GM titer) HI assay % positive (GM titer) Neutralization % positive (GM titer) NI assay % positive (GM titer) Total 200 0.5 (5.1) 14.0 (21.4) 22.5 (121.6) 5.5 (7.1) 16.5 (20.4) 9.0 (92.8) Birth cohort 1936–1949 (50) 2.0 (5.3) 18.0 (22.0) 24.0 (126.0) 6.0 (7.3) 16.0 (19.5) 12.0 (97.6) 1960–1965 (50) 0.0 (5.0) 16.0 (20.3) 26.0 (129.6) 6.0 (7.7) 30.0 (27.5) 6.0 (90.3) 1966–1971 (50) 0.0 (5.0) 12.0 (23.3) 20.0 (117.9) 10.0 (8.0) 16.0 (23.6) 10.0 (92.2) 1972–1977 (50) 0.0 (5.3) 10.0 (20.0) 20.0 (113.7) 0.0 (5.7) 4.0 (13.6) 8.0 (91.5) Serum collection year Y2000 (100) 0.0 (5.1) 15.0 (21.7) 21.0 (120.3) 7.0 (7.3) 16.0 (20.6) 11.0 (94.5) Y2008 (100) 1.0 (5.2) 13.0 (21.1) 24.0 (123.0) 4.0 (7.0) 17.0 (20.1) 7.0 (91.2) Sex Female (32) 3.1 (5.7) 21.9 (26.3) 12.5 (102.4) 3.1 (6.9) 12.5 (19.2) 6.3 (96.7) Male (168) 0.0 (5.0) 12.5 (20.5) 24.4 (125.7) 6.0 (7.2) 17.3 (20.6) 9.5 (92.1) Any cross-reactive antibody to H5N1 (57) 8.8 (8.9) 19.3 (25.2) 22.8 (119.9) pH1N1 (45) 2.2 (5.3) 28.9 (31.2) 37.8 (165.2) Neutralizing antibodies to H5N1 H5pp (28) 10.7 (9.5) 32.1 (33.6) 25.0 (116.9) 2009 pH1N1 neutralization (33) 3.0 (5.4) 27.3 (28.9) 30.3 (140.3) Lifetime seasonal vaccinations No record (66) 0.0 (5.1) 10.6 (20.2) 27.7 (128.1) 7.6 (7.4) 15.2 (20.6) 12.1 (96.5) 1–5 vaccinations (88) 1.1 (5.2) 15.9 (21.5) 17.0 (109.2) 5.7 (7.1) 17.0 (20.5) 6.8 (89.1) > 5 vaccinations (46) 0.0 (5.1) 15.2 (22.2) 32.6 (138.8) 2.2 (6.8) 17.4 (19.7) 8.7 (95.0) Time since last vaccine No record (66) 0.0 (5.1) 10.6 (20.2) 22.7 (128.1) 7.6 (7.4) 15.2 (20.6) 12.1 (96.5) ≤ 1 year (96) 0.0 (5.1) 15.6 (21.5) 24.0 (120.7) 4.2 (7.1) 19.8 (21.0) 8.3 (91.2) > 1 year (38) 2.6 (5.3) 15.8 (22.4) 18.4 (113.4) 5.2 (6.8) 10.5 (18.3) 5.3 (90.6) Vaccination history lifetime (at least one dose) No record of vaccination (66) 0.0 (5.1) 10.6 (20.2) 22.7 (128.1) 7.6 (7.4) 15.2 (20.6) 12.1 (96.5) Inactivated whole virus (71) 0.0 (5.0) 14.1 (20.4) 22.5 (115.7) 2.8 (6.4) 15.5 (19.6) 5.6 (87.1) Split type (102) 1.0 (5.0) 15.7 (20.4) 21.6 (115.7) 4.9 (6.4) 19.6 (19.6) 6.9 (87.1) Influenza vaccine not otherwise specified (16) 0.0 (5.2) 12.5 (27.9) 37.5 (166.4) 0.0 (6.2) 6.3 (16.1) 12.5 (102.3) Live attenuated intranasal (50) 0.0 (5.1) 10.0 (18.8) 20.0 (112.2) 4.0 (7.0) 18.0 (20.3) 4.0 (85.2) History of respiratory illness No record of illness (119) 0.0 (5.0) 10.1 (18.5) 18.5 (112.6) 4.2 (7.0) 15.1 (20.5) 8.4 (90.7) Influenza-like illness (4) 0.0 (5.0) 25.0 (20.7) 0.0 (80.0) 0.0 (8.4) 25.0 (28.3) 25.0 (100.2) Upper respiratory infection (65) 1.5 (5.4) 23.1 (29.3) 27.7 (135.0) 7.7 (7.3) 18.5 (20.7) 9.2 (93.1) Lower respiratory infection (37) 2.7 (5.6) 18.9 (30.2) 35.1 (157.6) 8.1 (8.1) 21.6 (22.4) 13.5 (108.4) Respiratory illness past year (28) 0 (5.1) 25.0 (25.1) 32.1 (154.9) 7.1 (8.0) 28.6 (24.4) 3.6 (86.3)