冻干甲型肝炎减毒活疫苗的接种反应及免疫原性观察

目的观察冻干甲型肝炎减毒活疫苗的接种反应和免疫原性。方法采用随机、开放式试验设计,选择出生后未接种过甲肝疫苗的18月龄以上的儿童和18～35周岁的成人,接种冻干甲肝减毒活疫苗1针。接种后30min观察即时反应,24、48和72h进行局部和全身反应观察,免后30d采血,分离血清,进行免疫原性检测。结果562名观察对象接种疫苗后均未发生局部反应,全身反应仅表现为轻度发热,有35人次(6.23%)发生发热反应。471名完成免疫原性检测的观察对象免后抗-HAV阳性率为99.8%,保护性抗体阳性率为96.4%,抗-HAVGMC水平为527.4mIU/ml,较免前增长30.3倍。儿童组和成人组免后保护性抗体阳性率分别为97.0%和95.9%,抗-HAVGMC水平分别为84.1和2119.1mIU/ml。2组免前免后抗体阳性率和GMC水平差异均有统计学意义。277名易感者免后抗体阳性率为99.6%,保护性抗体阳性率为93.9%,GMC水平为92.6mIU/ml,儿童组免后抗体阳性率明显高于成人组,免后抗-HAVGMC水平则明显低于成人组,2组之间免后抗体阳性率和GMC水平差异均有统计学意义。结论国产冻干甲型肝炎减毒活疫苗接种后副反应轻微,免疫原性良好,适于推广应用。     

乙型脑炎减毒活疫苗诱导小鼠的细胞免疫应答

目的探讨乙型脑炎(简称乙脑)减毒活疫苗在小鼠中诱导细胞免疫应答的能力,评价疫苗的免疫原性。方法以乙脑减毒活疫苗免疫BALB/c小鼠,制备小鼠脾淋巴细胞,在体外以重组蛋白NS3-1和NS3-2进行刺激,采用酶联斑点法(ELISPOT)检测分泌细胞因子的效应T细胞的频数。结果乙脑减毒活疫苗免疫小鼠的脾淋巴细胞在体外经重组蛋白NS3-1和NS3-2刺激后,可诱导高频数的分泌IFNγ的CD4+和CD8+T淋巴细胞及低频数的分泌IL-4的淋巴细胞;而乙脑灭活疫苗仅诱导低频数的分泌IFNγ和IL-4的T淋巴细胞。结论乙脑减毒活疫苗可在小鼠中有效地诱导NS3-1和NS3-2抗原特异性T淋巴细胞应答,且应答水平显著高于乙脑灭活疫苗。     

一种新的求解配电网重构问题的免疫遗传算法

针对遗传算法求解配电网重构问题存在的不足,建立了以网损最小为目标函数的配电网重构数学模型,提出了一种新的免疫遗传算法。该算法的关键在于疫苗库的构建和免疫算子的设计。疫苗库可自动建立和更新,免疫算子由接种疫苗和免疫检测组成。另外,采用了基于基本环路的编码方法、高频变异和大选择压的锦标赛选择算子。IEEE 33和IEEE 69系统的仿真测试结果表明：该算法符合配电网重构问题的特点,能有效抑制进化中的退化和波动现象,在确保解的质量的同时,具有很快的收敛速度;与传统遗传算法和相关文献中的同类算法相比,该算法在效率和性能方面具有优越性。     

乳酸菌用作口服疫苗表达载体的应用研究进展

乳酸菌是人和大多数动物肠道内的常见细菌,被公认为绿色安全级微生物,可以在机体内粘附、定植和复制,益生作用和免疫原性使其成为最有前景的活菌载体。利用基因工程手段插入外源基因制备重组乳酸菌口服疫苗,能够持续刺激机体分泌特异性抗原蛋白,多个基因的插入甚至能达到一免治多病的目的。口服免疫效果优于注射免疫,治疗成本也大大降低。该文论述了乳酸菌活载体疫苗的优势,对乳酸菌表达载体在动物疫病防控及代谢产物调控上的应用等方面的研究进行综述,并对其应用前景进行分析和展望。     

The Versatile Manipulations of Self-Assembled Proteins in Vaccine Design

Protein assemblies provide unique structural features which make them useful as carrier molecules in biomedical and chemical science. Protein assemblies can accommodate a variety of organic, inorganic and biological molecules such as small proteins and peptides and have been used in development of subunit vaccines via display parts of viral pathogens or antigens. Such subunit vaccines are much safer than traditional vaccines based on inactivated pathogens which are more likely to produce side-effects. Therefore, to tackle a pandemic and rapidly produce safer and more effective subunit vaccines based on protein assemblies, it is necessary to understand the basic structural features which drive protein self-assembly and functionalization of portions of pathogens. This review highlights recent developments and future perspectives in production of non-viral protein assemblies with essential structural features of subunit vaccines.     

Babesia Bovis Ligand-Receptor Interaction: AMA-1 Contains Small Regions Governing Bovine Erythrocyte Binding

Apical membrane antigen 1 is a microneme protein which plays an indispensable role during Apicomplexa parasite invasion. The detailed mechanism of AMA-1 molecular interaction with its receptor on bovine erythrocytes has not been completely defined in Babesia bovis. This study was focused on identifying the minimum B. bovis AMA-1-derived regions governing specific and high-affinity binding to its target cells. Different approaches were used for detecting ama-1 locus genetic variability and natural selection signatures. The binding properties of twelve highly conserved 20-residue-long peptides were evaluated using a sensitive and specific binding assay based on radio-iodination. B. bovis AMA-1 ectodomain structure was modelled and refined using molecular modelling software. NetMHCIIpan software was used for calculating B- and T-cell epitopes. The B. bovis ama-1 gene had regions under functional constraint, having the highest negative selective pressure intensity in the Domain I encoding region. Interestingly, B. bovis AMA-1-DI (¹⁰⁰YMQKFDIPRNHGSGIYVDLG¹¹⁹ and ¹²⁰GYESVGSKSYRMPVGKCPVV¹³⁹) and DII (³⁰²CPMHPVRDAIFGKWSGGSCV³²¹)-derived peptides had high specificity interaction with erythrocytes and bound to a chymotrypsin and neuraminidase-treatment sensitive receptor. DI-derived peptides appear to be exposed on the protein’s surface and contain predicted B- and T-cell epitopes. These findings provide data (for the first-time) concerning B. bovis AMA-1 functional subunits which are important for establishing receptor-ligand interactions which could be used in synthetic vaccine development.     

Molecular Mechanisms to Control Post-Transplantation Hepatitis B Recurrence

Hepatitis B often progresses to decompensated liver cirrhosis requiring orthotopic liver transplantation (OLT). Although newer nucleos(t)ide analogues result in >90% viral and hepatitis activity control, severely decompensated patients still need OLT because of drug-resistant virus, acute exacerbation, or hepatocellular carcinoma. Acute hepatitis B is also an indication for OLT, because it can progress to fatal acute liver failure. After OLT, the hepatitis B recurrence rate is >80% without prevention, while >90% of transplant recipients are clinically controlled with combined hepatitis B immunoglobulin (HBIG) and nucleos(t)ide analogue treatment. However, long-term HBIG administration is associated with several unresolved issues, including limited availability and extremely high cost; therefore, several treatment protocols with low-dose HBIG, combined with nucleos(t)ide analogues, have been investigated. Another approach is to induce self-producing anti-hepatitis B virus (HBV) antibodies using an HBV envelope (HBs) antigen vaccine. Patients who are not HBV carriers, such as those with acutely infected liver failure, are good candidates for vaccination. For chronic HBV carrier liver cirrhosis patients, a successful vaccine response can only be achieved in selected patients, such as those treated with experimentally reduced immunosuppression protocols. The present protocol for post-OLT HBV control and the future prospects of newer treatment strategies are reviewed.     

One-year persistence of neutralizing anti-SARS-CoV-2 antibodies in dialysis patients recovered from COVID-19

The immunological mechanisms that modulate immune response to SARS-CoV-2 infection remain elusive. Little is known on the magnitude and the durability of antibody response against COVID-19. There is consensus that patients with immune dysfunction, such as dialysis patients, may be unable to mount a robust and durable humoral immunity after infections. Recent studies showed that dialysis patients seroconverted after COVID-19, but data on the durability of the immune response are missing. We reported the data of a durable anti-spike protein seroconversion after natural SARS-CoV-2 infection in three patients on hemodialysis with a mean age of 67.2 ± 13.8 years. A mean antibody titer of 212.6 ± 174.9 UA/ml (Liaison®, DiaSorin) was found after one year (range, 366–374 days) from the diagnosis of COVID-19. In conclusion, this case series provided evidence that patients receiving hemodialysis who recovered from severe COVID-19 were able to mount a long-lasting immune response against SARS-CoV-2. Although the protective capacity of this long-term immunity remains to be determined, these patients did not report signs of reinfection after recovery from COVID-19.     

Improving Routine Immunization Coverage Through Optimally Designed Predictive Models

Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.