Hasil untuk "Veterinary medicine"

P. Eckersall, R. Bell

Kiziltepe Şemistan, Karakurt Emin, Eser Gizem et al.

Ovine pulmonary adenocarcinoma (OPA) is an infectious and neoplastic disease etiologically linked to the Jaagsiekte sheep retrovirus (JSRV), characterised by tumour lesions of the lung. Because of the economic losses it induces, OPA is of great importance for flock health. In this study, oxidative stress markers and adenosine deaminase (ADA) activity were quantified in lung tissue from sheep, both healthy and those naturally afflicted with OPA. Compared to healthy sheep, malondialdehyde (MDA), nitric oxide (NO ), ceruloplasmin (CP) and ADA concentrations/activities were significantly increased (P<0.05 and P<0.001) in fresh lung tissues from JSRV–infected sheep, while reduced glutathione (GSH) levels were significantly decreased (P<0.05). In conclusion, pronounced oxidative stress and increased ADA enzyme activity were detected in the JSRV–infected sheep. These findings suggest that ADA activity could serve as a biomarker for disease diagnosis.

Enos Moyo, Hadrian Mangwana, Endalkachew Melese et al.

<b>Background:</b> Since the onset of the HIV epidemic, over 40 million individuals have died from AIDS-related illnesses, leading to nearly 14 million children aged 0–17 losing one or both parents to AIDS by 2022. In 2023, Namibia had 250,000 vulnerable children and 72,000 children aged 0–17 orphaned due to HIV and AIDS. Without parental support, orphaned and vulnerable children (OVC) face heightened risks, including neglect, distress, and compromised decision-making. These vulnerabilities can increase their susceptibility to risky behaviors, such as sexual experimentation. This study used data from the Project HOPE Namibia (PHN) OVC program to assess HIV testing rates and associated factors among OVC. <b>Methods:</b> This retrospective cross-sectional secondary analysis study used data from PHN’s OVC program implemented from 1 August 2023 to 30 November 2024. Data were analyzed using Chi-square tests and binomial and multinomial logistic regression. <b>Results:</b> Among the 16,995 participants included in this analysis, 15,014 (88.3%) participants had ever been tested for HIV (95% confidence interval (CI): 87.8–88.8%). Participants with an increased likelihood of having ever tested for HIV included those who had been in the program for 0–6 months (adjusted odds ratio (AOR) = 1.31, 95% CI (1.17–1.47)), and those from households experiencing little or moderate hunger (AOR = 1.29, 95% CI (1.12–1.50), AOR = 1.51, 95% CI (1.33–1.72), respectively. <b>Conclusions:</b> A multi-pronged approach involving all stakeholders is required to increase HIV testing among OVC. Such an approach should include community-based HIV testing, providing male-friendly healthcare services, and reducing household hunger through economically empowering vulnerable households.

Lincy Koodaly, Erika Friedmann, Nancy R. Gee et al.

<b>Background:</b> The growth of the older adult population calls for innovative and cost-effective ways of promoting their physical, psychological, and cognitive health. Human–animal interaction, including pet ownership, is related to positive and negative aspects of human health. Not all pet owners respond in the same way. The levels of pet attachment and pets’ influence on their owners’ lives could moderate the relationship between psychological status and health outcomes. <b>Purpose:</b> We examined the moderating role of pet attachment in the relationships of psychological status (mental wellbeing, happiness, anxiety, depression) to physical function (physical wellbeing, usual- and rapid-gait speeds, physical performance battery) in community-residing older adult pet owners. <b>Methods:</b> A cross-sectional, secondary analysis of pet-owning older adult participants in the Baltimore Longitudinal Study of Aging (n = 178). <b>Results:</b> In regression analyses, controlling for age, gender, and comorbidities, pet attachment and pet influence moderated the relationships of physical wellbeing to mental wellbeing and anxiety (<i>p</i> < 0.05). Pet influence also moderated the relationship between anxiety and usual gait speed (<i>p</i> < 0.05). <b>Conclusions:</b> Greater attachment and influence buffer the relationship of perceptions of poor mental function with perceptions of poor physical wellbeing suggesting one mechanism for health benefits of human-animal interaction.

Yin Dai, Xiaomiao Hu, Yueyi Zhong et al.

Duck viral enteritis (DVE) is an acute and highly contagious disease that affects waterfowl such as ducks, geese and swans. Duck enteritis virus (DEV) is the pathogen, causing huge economic losses to waterfowl farming in recent years. Establishing a rapid, simple, and visual detection should facilitate the early identification of DEV. After the amplification primers and reaction conditions were optimized, three multienzyme isothermal rapid amplification (MIRA) methods—basic MIRA, MIRA–quantitative PCR (MIRA–qPCR) and MIRA–lateral flow dipstick (MIRA–LFD)—were established to detect DEV. Specificity analyses showed that the three MIRA methods specifically detected DEV, with no cross-reaction with fowl adenovirus serotype 4, novel goose astrovirus, Muscovy duck reovirus, avian influenza virus subtype H9, or duck circovirus. The basic MIRA reaction was completed in 30 min at 35 °C, requiring only a pair of primers. Detection with MIRA–qPCR or MIRA–LFD was completed within 20 min, and the limits of detection were 1 × 10¹ copies/μL for both. MIRA–LFD required no specialized instruments, and the results could be viewed directly with the naked eye. Compared with the traditional PCR, MIRA assays are simple, rapid, and effective and therefore more suitable for the field detection of DEV.

Elisha D. O. Roberson

The medical ecosystem consists of the training of new clinicians and researchers, the practice of clinical medicine, and areas of adjacent research. There are many aspects of these domains that could benefit from the application of task automation and programmatic assistance. Machine learning and artificial intelligence techniques, including large language models (LLMs), have been promised to deliver on healthcare innovation, improving care speed and accuracy, and reducing the burden on staff for manual interventions. However, LLMs have no understanding of objective truth that is based in reality. They also represent real risks to the disclosure of protected information when used by clinicians and researchers. The use of AI in medicine in general, and the deployment of LLMs in particular, therefore requires careful consideration and thoughtful application to reap the benefits of these technologies while avoiding the dangers in each context.

YunHe Su, Zhengyang Lu, Junhui Liu et al.

This paper explores the advancements and applications of large-scale models in the medical field, with a particular focus on Medical Large Models (MedLMs). These models, encompassing Large Language Models (LLMs), Vision Models, 3D Large Models, and Multimodal Models, are revolutionizing healthcare by enhancing disease prediction, diagnostic assistance, personalized treatment planning, and drug discovery. The integration of graph neural networks in medical knowledge graphs and drug discovery highlights the potential of Large Graph Models (LGMs) in understanding complex biomedical relationships. The study also emphasizes the transformative role of Vision-Language Models (VLMs) and 3D Large Models in medical image analysis, anatomical modeling, and prosthetic design. Despite the challenges, these technologies are setting new benchmarks in medical innovation, improving diagnostic accuracy, and paving the way for personalized healthcare solutions. This paper aims to provide a comprehensive overview of the current state and future directions of large models in medicine, underscoring their significance in advancing global health.

Faraz Farhadi, Shadi A. Esfahani, Fereshteh Yousefirizi et al.

The field of Clinical-Computational Nuclear Medicine is rapidly advancing, fueled by AI, tracer kinetic modeling, radiomics, and integrated informatics. These technologies improve imaging quality, automate lesion detection, and enable personalized radiopharmaceutical therapy through physiologically based pharmacokinetic (PBPK) modeling and voxel-level dosimetry. Workflow automation and Natural Language Processing (NLP) further enhance operational efficiency. However, successful implementation and adoption of these tools require clinical oversight to ensure accuracy, interpretability, and patient safety. This paper highlights key computational innovations and emphasizes the critical role of clinician-guided evaluation in shaping the future of precision imaging and therapy.

Markus Bertl, Alan Mott, Salvatore Sinno et al.

The digitization of healthcare presents numerous challenges, including the complexity of biological systems, vast data generation, and the need for personalized treatment plans. Traditional computational methods often fall short, leading to delayed and sometimes ineffective diagnoses and treatments. Quantum Computing (QC) and Quantum Machine Learning (QML) offer transformative advancements with the potential to revolutionize medicine. This paper summarizes areas where QC promises unprecedented computational power, enabling faster, more accurate diagnostics, personalized treatments, and enhanced drug discovery processes. However, integrating quantum technologies into precision medicine also presents challenges, including errors in algorithms and high costs. We show that mathematically-based techniques for specifying, developing, and verifying software (formal methods) can enhance the reliability and correctness of QC. By providing a rigorous mathematical framework, formal methods help to specify, develop, and verify systems with high precision. In genomic data analysis, formal specification languages can precisely (1) define the behavior and properties of quantum algorithms designed to identify genetic markers associated with diseases. Model checking tools can systematically explore all possible states of the algorithm to (2) ensure it behaves correctly under all conditions, while theorem proving techniques provide mathematical (3) proof that the algorithm meets its specified properties, ensuring accuracy and reliability. Additionally, formal optimization techniques can (4) enhance the efficiency and performance of quantum algorithms by reducing resource usage, such as the number of qubits and gate operations. Therefore, we posit that formal methods can significantly contribute to enabling QC to realize its full potential as a game changer in precision medicine.

Thi Thanh Thuy Dao, Ravi Fotedar

We investigated the effect of feed deprivation for 45 days on the growth, immunity, and health of 0⁺marron (<i>Cherax cainii</i>) initially fed for 110 days on various protein sources including fishmeal (FM), poultry by-product meal (PBM), black soldier fly meal (BSFM), soybean meal (SBM), lupin meal (LM), and tuna hydrolysate. The marron were weighed and sacrificed immediately after feeding stopped (day 0) and at days 15, 30, and 45 after the feed deprivation trial commenced. Total haemolymph count, differential haemocyte count, lysozyme activity, protease activity, total bacterial count in the digestive tract, and organosomatic indices were analysed. Initially feeding marron any protein sources did not influence the percentage of weight gain and specific growth rates of marron. All marron showed more than 83% survival; however, marron fed soybean meal showed significantly lower survival than others. Dietary sources of protein altered organosomatic indices of starved marron during various starvation periods and resulted in a significant decrease in total haemocyte counts, lysozyme activity, protease activity, and bacterial count in the digestive tract of marron. Starved marron initially fed PBM and BSFM showed higher tolerance to starvation, followed by marron initially fed FM and SBM, while marron initially fed TH and LM showed the highest susceptibility to starvation.

Qiao-Guang Chen, Qiao-Guang Chen, Ling-Ming Kong et al.

IntroductionHistomonosis, a protozoan disease caused by Histomonas meleagridis, poses a significant economic burden on domestic poultry in China. To reduce the losses caused by this disease in chickens, an attenuated vaccine was developed by exploiting the diminished virulence of H. meleagridis through successive in vitro passages.MethodsFour experiments were conducted to evaluate the viability of attenuated H. meleagridis as a potential vaccine candidate. Experiment 1 evaluated the route of infection (oral vs. intracloacal) and dose (5 × 104, 1 × 105, and 2 × 105H. meleagridis/chicken) using the virulent strain H. meleagridis JSYZ-D10. Experiment 2 evaluated the attenuated effect of the H. meleagridis JSYZ-D168 strain (infection dose: 2 × 105H. meleagridis/chicken). Experiment 3 evaluated the immunoprotective effect of different immunization doses (5 × 104, 1 × 105, and 2 × 105H. meleagridis/chicken). Experiment 4 evaluated the immunoprotective effect of different immunization schedules (immunization at 3 days of age; immunization at 14 days of age; two immunizations, one at 3 days of age and one at 14 days of age; immunization and infection dose: 2 × 105H. meleagridis/chicken).ResultsThe results showed that the intracloacal route of infection was more effective and stable compared to the oral route. The pathogenicity of the JSYZ-D168 H. meleagridis strain was significantly reduced compared to the original virulent strain. Chickens vaccinated by intracloacal immunization at a dose of 2 × 105H. meleagridis/chicken on day 14 provided effective protection against a virulent strain challenge, significantly resulting in increased body weight and reduced lesions in the cecum and liver within 28 days post-immunization (p &lt; 0.05). Poor immunoprotection was obtained either when the immunization dose was 1 × 105H. meleagridis/chicken or when the immunization program was a single immunization at 3 days of age only.DiscussionIn conclusion, the administration of a vaccine provides a measurable degree of protection against the detrimental effects induced by H. meleagridis, thus warranting its endorsement in clinical settings.

Mahmoud Kassem, Ahmed Kamr, Creighton B. Wright et al.

Objective: Breast conserving surgery is an excellent option in the treatment of breast cancer. To achieve a good result with this modality, a surgeon needs to identify and excise the tumor with adequate margins. The radiofrequency identification (RFID) technique is a wireless localization technique used for intraoperative breast lesion identification. We assessed the efficacy and outcomes of the RFID technique in breast cancer patients at our institution. Materials and Methods: This is a single institution, retrospective study (BSMH 22-02X-MWH) of 73 patients. We analyzed the medical records of women with biopsy-proven breast cancer from June 2020 to August 2022; participants received surgical care at Mercy Health West Hospital. Data collected included demographics, clinicopathological characteristics, and surgical procedure. The primary objective was to determine the safety and efficacy of RFID. The secondary objective was to assess the impact of obesity and breast density on the RFID outcomes. Results: A total of 73 female patients met the eligibility criteria with stage I (59%) and grade 1 (51%) breast cancer with mean age of at diagnosis of 66.8 years and mean body mass index of 31.4 kg/m2. Patients had invasive ductal carcinoma (61%), hormonal positive (56%), and human epidermal growth factor receptor 2 negative (68%) disease. All RFID tags were placed under image guidance with 100% accuracy of placement with no evidence of migration or procedure revision. Ninety percent of patients had free surgical margins and only seven patients needed margin re-excision with successful removal of the lesion and the tag. Conclusion: RFID localization technique is a safe, effective and reliable procedure that results in favorable patient outcomes and quality of life.

Sunan He, Yuxiang Nie, Hongmei Wang et al.

Generalist foundation models (GFMs) are renowned for their exceptional capability and flexibility in effectively generalizing across diverse tasks and modalities. In the field of medicine, while GFMs exhibit superior generalizability based on their extensive intrinsic knowledge as well as proficiency in instruction following and in-context learning, specialist models excel in precision due to their domain knowledge. In this work, for the first time, we explore the synergy between the GFM and specialist models, to enable precise medical image analysis on a broader scope. Specifically, we propose a cooperative framework, Generalist-Specialist Collaboration (GSCo), which consists of two stages, namely the construction of GFM and specialists, and collaborative inference on downstream tasks. In the construction stage, we develop MedDr, the largest open-source GFM tailored for medicine, showcasing exceptional instruction-following and in-context learning capabilities. Meanwhile, a series of lightweight specialists are crafted for downstream tasks with low computational cost. In the collaborative inference stage, we introduce two cooperative mechanisms, Mixture-of-Expert Diagnosis and Retrieval-Augmented Diagnosis, to harvest the generalist's in-context learning abilities alongside the specialists' domain expertise. For a comprehensive evaluation, we curate a large-scale benchmark featuring 28 datasets and about 250,000 images. Extensive results demonstrate that MedDr consistently outperforms state-of-the-art GFMs on downstream datasets. Furthermore, GSCo exceeds both GFMs and specialists across all out-of-domain disease diagnosis datasets. These findings indicate a significant paradigm shift in the application of GFMs, transitioning from separate models for specific tasks to a collaborative approach between GFMs and specialists, thereby advancing the frontiers of generalizable AI in medicine.

Ehsan Abadi, Aldo Badano, Predrag Bakic et al.

This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare. The proceedings are categorized by the respective days of the conference: Monday presentations, Tuesday presentations, Wednesday presentations, followed by the abstracts for the posters presented on Monday and Tuesday.

Abbi Abdel-Rehim, Oghenejokpeme Orhobor, Gareth Griffiths et al.

The concept of personalised medicine in cancer therapy is becoming increasingly important. There already exist drugs administered specifically for patients with tumours presenting well-defined mutations. However, the field is still in its infancy, and personalised treatments are far from being standard of care. Personalised medicine is often associated with the utilisation of omics data. Yet, implementation of multi-omics data has proven difficult, due to the variety and scale of the information within the data, as well as the complexity behind the myriad of interactions taking place within the cell. An alternative approach to precision medicine is to employ a function-based profile of the cell. This involves screening a range of drugs against patient derived cells. Here we demonstrate a proof-of-concept, where a collection of drug screens against a highly diverse set of patient-derived cell lines, are leveraged to identify putative treatment options for a 'new patient'. We show that this methodology is highly efficient in ranking the drugs according to their activity towards the target cells. We argue that this approach offers great potential, as activities can be efficiently imputed from various subsets of the drug treated cell lines that do not necessarily originate from the same tissue type.

Kyung Ho Park, Tae‐Un Kim, Hyun‐Woo Park et al.

Abstract Background Most extramedullary plasmacytomas (EMPs) aresolitary and located in the head and neck region. They may also occur in the visceral parts of the body. Objectives Here, we report a case of oral EMP followed by neoplastic plasma cell metastasis to both kidneys in a neutered male Pomeranian. Methods Oral plasmacytoma recurred 11 months aftersurgical removal of an oral mass and partial maxillectomy was performed. Eighteen months after partial maxillectomy, neoplastic masses were detected in both kidneys on computed tomography. The dog died 12 months after detection of bilateral kidney neoplasms. The resected neoplastic masses were routinely processed for histopathological observation and immunohistochemistry against pan‐cytokeratin, desmin, CD3, and MUM‐1. Results The recurred mass mainly consisted of well‐differentiated plasma cells and contained a small portion of aggressive cells with malignant features. Monoclonal gammopathy was not observed on serumelectrophoresis performed to exclude multiple myeloma. The mass was composed of plasma cells with high nuclear pleomorphism and abundant mitotic figures. The neoplasm stained positive for MUM‐1 with a more aggressive morphology than in oral EMP. Conclusion Based on serum biomarker and pathological observations, a diagnosis of recurrence and metastasis of oral‐to‐renal EMP was established. To the best of our knowledge, metastasis of oral EMP into the bilateral kidneys, as described in the current case, has not been previously reported in dogs.

Joshua Durso-Finley, Jean-Pierre Falet, Raghav Mehta et al.

Image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve their clinical outcome. Machine learning frameworks that integrate uncertainty estimation as part of their treatment recommendations would be safer and more reliable. However, little work has been done in adapting uncertainty estimation techniques and validation metrics for precision medicine. In this paper, we use Bayesian deep learning for estimating the posterior distribution over factual and counterfactual outcomes on several treatments. This allows for estimating the uncertainty for each treatment option and for the individual treatment effects (ITE) between any two treatments. We train and evaluate this model to predict future new and enlarging T2 lesion counts on a large, multi-center dataset of MR brain images of patients with multiple sclerosis, exposed to several treatments during randomized controlled trials. We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate how knowledge of uncertainty could modify clinical decision-making to improve individual patient and clinical trial outcomes.

Dimitris Bertsimas, Angelos G. Koulouras, Georgios Antonios Margonis

We propose a prognostic stratum matching framework that addresses the deficiencies of Randomized trial data subgroup analysis and transforms ObservAtional Data to be used as if they were randomized, thus paving the road for precision medicine. Our approach counters the effects of unobserved confounding in observational data by correcting the estimated probabilities of the outcome under a treatment through a novel two-step process. These probabilities are then used to train Optimal Policy Trees (OPTs), which are decision trees that optimally assign treatments to subgroups of patients based on their characteristics. This facilitates the creation of clinically intuitive treatment recommendations. We applied our framework to observational data of patients with gastrointestinal stromal tumors (GIST) and validated the OPTs in an external cohort using the sensitivity and specificity metrics. We show that these recommendations outperformed those of experts in GIST. We further applied the same framework to randomized clinical trial (RCT) data of patients with extremity sarcomas. Remarkably, despite the initial trial results suggesting that all patients should receive treatment, our framework, after addressing imbalances in patient distribution due to the trial's small sample size, identified through the OPTs a subset of patients with unique characteristics who may not require treatment. Again, we successfully validated our recommendations in an external cohort.

Seiichi Okuno, Seiichi Okuno, Hirotaka Katahira et al.

This study aimed to identify the impact on spinal cord integrity and determine the electrophysiological safety level during surgery for thoracolumbar intervertebral disk herniation in dogs. A total of 52 dogs diagnosed with thoracolumbar intervertebral disk herniation were enrolled. The tibial nerve somatosensory evoked potential elicited on the scalp by stimulation of the tibial nerve was recorded before and during hemilaminectomy. Both the amplitude and latency of the somatosensory evoked potential were periodically registered, and the percentage changes from the pre-operative control values (amplitude rate and latency rate) were calculated. When the multifidus muscles were retracted after removal from the spinous processes and vertebrae, the somatosensory evoked potential amplitude rate decreased in all dogs, while the latency rate increased in 33 dogs examined. The amplitude rate remained unchanged during the halting procedure, loosening retraction, and hemilaminectomy. After removing the disc material from the spinal canal, the amplitude rate was increased. The somatosensory evoked potential latency increased when the multifidus muscles were retracted and shortened after multifidus muscles closure in four cases. The outcome of all cases showed improvement in clinical signs 7 days after operation. Spinal cord conduction is impaired by retraction of the multifidus muscles and improved by removal of disk materials. Maintaining intraoperative SEP amplitudes above 50% of control may help avoid additional spinal cord injury during surgery. Since we have no case that worsened after the surgery, however, further studies are necessary to confirm this proposal.

Yu-Ke Liu, Guo-Hua Liu, Lei Liu et al.

Abstract Background Haemaphysalis flava is a hematophagous ectoparasite that acquires the nutrition needed for development and reproduction by sucking blood and digesting the blood meal. During blood-sucking and blood-meal digestion, the prevention of blood coagulation is important for this tick. Previous studies have shown that heat shock cognate 70 (HSC70) protein has certain anticoagulant activities, but its immunogenicity remains unclear. Also, whether the mutation of individual bases of the TKD-like peptide of HSC70 through the overlap extension method can change its anticoagulant activities and immunogenicity remains to be investigated. Methods The gene encoding the HSC70 protein was cloned from a complementary DNA library synthesized from H. flava. The coding gene of the TKD-like peptide of HSC70 was mutated into a TKD peptide coding gene (HSC70TKD) using the overlap extension method. Escherichia coli prokaryotic expression plasmids were constructed to obtain the recombinant proteins of HSC70 (rHSC70) and HSC70TKD (rHSC70TKD). The purified rHSC70 and rHSC70TKD were evaluated at different concentrations for anticoagulant activities using four in vitro clotting assays. Emulsifying recombinant proteins with complete and incomplete Freund’s adjuvants were subcutaneously immunized in Sprague Dawley rats. The serum antibody titers and serum concentrations of interferon-gamma (IFN-γ) and interleukin-4 (IL-4) were detected using an indirect enzyme-linked immunosorbent assay to assess the immunogenicity of rHSC70 and rHSC70TKD. Results The open reading frame of HSC70 was successfully amplified and found to have a length of 1958 bp. The gene encoding the TKD-like peptide of HSC70 was artificially mutated, with the 1373-position adenine (A) of the original sequence mutated into guanine (G), the 1385-position cytosine (C) mutated into G and the 1386-position G mutated into C. rHSC70 and rHSC70TKD that fused with His-tag were obtained using the expression plasmids pET-28a-HSC70 and pET-28a-HSC70TKD, respectively. rHSC70 and rHSC70TKD prolonged the thrombin time (TT) and reduced the fibrinogen (FIB) content in the plasma, but did not affect the prothrombin time (PT) or activated partial thromboplastin time (APTT) when compared to the negative control. Interestingly, the ability of rHSC70TKD to prolong the TT and reduce the FIB content in the plasma was better than that of rHSC70. The specific antibody titers of both rHSC70 and rHSC70TKD in rat serum reached 1:124,000 14 days after the third immunization. The serum concentration of IFN-γ in the rHSC70TKD group was higher than that in the rHSC70 group. The rHSC70 group has the highest serum concentration of IL-4, and the serum concentration of IL-4 in the rHSC70TKD group was higher than that in the negative group. Conclusions rHSC70 and rHSC70TKD exhibited anticoagulant activities by prolonging the TT and reducing the FIB content in vitro. rHSC70TKD had better anticoagulant activities than rHSC70. Both rHSC70 and rHSC70TKD had good immunogenicity and induced humoral and cellular immunity.