Electric fields control water-gated proton transfer in cytochrome c oxidase.

Aerobic life is powered by membrane-bound enzymes that catalyze the transfer of electrons to oxygen and protons across a biological membrane. Cytochrome c oxidase (CcO) functions as a terminal electron acceptor in mitochondrial and bacterial respiratory chains, driving cellular respiration and transducing the free energy from O2 reduction into proton pumping. Here we show that CcO creates orientated electric fields around a nonpolar cavity next to the active site, establishing a molecular switch that directs the protons along distinct pathways. By combining large-scale quantum chemical density functional theory (DFT) calculations with hybrid quantum mechanics/molecular mechanics (QM/MM) simulations and atomistic molecular dynamics (MD) explorations, we find that reduction of the electron donor, heme a, leads to dissociation of an arginine (Arg438)-heme a3 D-propionate ion-pair. This ion-pair dissociation creates a strong electric field of up to 1 V Å-1 along a water-mediated proton array leading to a transient proton loading site (PLS) near the active site. Protonation of the PLS triggers the reduction of the active site, which in turn aligns the electric field vectors along a second, "chemical," proton pathway. We find a linear energy relationship of the proton transfer barrier with the electric field strength that explains the effectivity of the gating process. Our mechanism shows distinct similarities to principles also found in other energy-converting enzymes, suggesting that orientated electric fields generally control enzyme catalysis.

Machine learning-based preoperative analytics for the prediction of anastomotic leakage in colorectal surgery: a swiss pilot study.

BACKGROUND: Anastomotic leakage (AL), a severe complication following colorectal surgery, arises from defects at the anastomosis site. This study evaluates the feasibility of predicting AL using machine learning (ML) algorithms based on preoperative data. METHODS: We retrospectively analyzed data including 21 predictors from patients undergoing colorectal surgery with bowel anastomosis at four Swiss hospitals. Several ML algorithms were applied for binary classification into AL or non-AL groups, utilizing a five-fold cross-validation strategy with a 90% training and 10% validation split. Additionally, a holdout test set from an external hospital was employed to assess the models' robustness in external validation. RESULTS: Among 1244 patients, 112 (9.0%) suffered from AL. The Random Forest model showed an AUC-ROC of 0.78 (SD: ± 0.01) on the internal test set, which significantly decreased to 0.60 (SD: ± 0.05) on the external holdout test set comprising 198 patients, including 7 (3.5%) with AL. Conversely, the Logistic Regression model demonstrated more consistent AUC-ROC values of 0.69 (SD: ± 0.01) on the internal set and 0.61 (SD: ± 0.05) on the external set. Accuracy measures for Random Forest were 0.82 (SD: ± 0.04) internally and 0.87 (SD: ± 0.08) externally, while Logistic Regression achieved accuracies of 0.81 (SD: ± 0.10) and 0.88 (SD: ± 0.15). F1 Scores for Random Forest moved from 0.58 (SD: ± 0.03) internally to 0.51 (SD: ± 0.03) externally, with Logistic Regression maintaining more stable scores of 0.53 (SD: ± 0.04) and 0.51 (SD: ± 0.02). CONCLUSION: In this pilot study, we evaluated ML-based prediction models for AL post-colorectal surgery and identified ten patient-related risk factors associated with AL. Highlighting the need for multicenter data, external validation, and larger sample sizes, our findings emphasize the potential of ML in enhancing surgical outcomes and inform future development of a web-based application for broader clinical use.

Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery.

Background and Objectives: The advancement of artificial intelligence (AI) based technologies in medicine is progressing rapidly, but the majority of its real-world applications has not been implemented. The establishment of an accurate diagnosis with treatment has now transitioned into an artificial intelligence era, which has continued to provide an amplified understanding of liver cancer as a disease and helped to proceed better with the method of procurement. This article focuses on reviewing the AI in liver-associated diseases and surgical procedures, highlighting its development, use, and related counterparts. Materials and Methods: We searched for articles regarding AI in liver-related ailments and surgery, using the keywords (mentioned below) on PubMed, Google Scholar, Scopus, MEDLINE, and Cochrane Library. Choosing only the common studies suggested by these libraries, we segregated the matter based on disease. Finally, we compiled the essence of these articles under the various sub-headings. Results: After thorough review of articles, it was observed that there was a surge in the occurrence of liver-related surgeries, diagnoses, and treatments. Parallelly, advanced computer technologies governed by AI continue to prove their efficacy in the accurate screening, analysis, prediction, treatment, and recuperation of liver-related cases. Conclusions: The continual developments and high-order precision of AI is expanding its roots in all directions of applications. Despite being novel and lacking research, AI has shown its intrinsic worth for procedures in liver surgery while providing enhanced healing opportunities and personalized treatment for liver surgery patients.

Mesenchymal precursor cells maintain the differentiation and proliferation potentials of breast epithelial cells.

INTRODUCTION: Stromal-epithelial interactions play a fundamental role in tissue homeostasis, controlling cell proliferation and differentiation. Not surprisingly, aberrant stromal-epithelial interactions contribute to malignancies. Studies of the cellular and molecular mechanisms underlying these interactions require ex vivo experimental model systems that recapitulate the complexity of human tissue without compromising the differentiation and proliferation potentials of human primary cells. METHODS: We isolated and characterized human breast epithelial and mesenchymal precursors from reduction mammoplasty tissue and tagged them with lentiviral vectors. We assembled heterotypic co-cultures and compared mesenchymal and epithelial cells to cells in corresponding monocultures by analyzing growth, differentiation potentials, and gene expression profiles. RESULTS: We show that heterotypic culture of non-immortalized human primary breast epithelial and mesenchymal precursors maintains their proliferation and differentiation potentials and constrains their growth. We further describe the gene expression profiles of stromal and epithelial cells in co-cultures and monocultures and show increased expression of the tumor growth factor beta (TGFß) family member inhibin beta A (INHBA) in mesenchymal cells grown as co-cultures compared with monocultures. Notably, overexpression of INHBA in mesenchymal cells increases colony formation potential of epithelial cells, suggesting that it contributes to the dynamic reciprocity between breast mesenchymal and epithelial cells. CONCLUSIONS: The described heterotypic co-culture system will prove useful for further characterization of the molecular mechanisms mediating interactions between human normal or neoplastic breast epithelial cells and the stroma, and will provide a framework to test the relevance of the ever-increasing number of oncogenomic alterations identified in human breast cancer.

Long-term follow-up of a randomized controlled trial of Lichtenstein's operation versus mesh plug repair for inguinal hernia.

OBJECTIVE: To compare long-term results of Lichtenstein's operation versus mesh plug repair for open inguinal hernia repair. BACKGROUND: The technique of best choice in open prosthetic inguinal hernia repair remains a subject of ongoing debate. METHODS: In this prospective, randomized controlled multicenter trial, patients with primary or recurrent inguinal hernias were randomized to undergo either Lichtenstein's operation or mesh plug repair. The primary endpoint was the long-term recurrence rate. Secondary endpoints included chronic pain, sensibility disorders, and reoperation rate. RESULTS: In total, 697 hernias in 594 patients were randomized (297 patients per group). At a median follow-up of 6.5 years, 528 (76%) operated hernias in 444 (75%) patients were clinically evaluated. The recurrence rate was similar in both groups [mesh plug: 21/268 hernias = 7.8%; Lichtenstein: 21/260 hernias = 8.1%; adjusted odds ratio (OR): 0.92; 95% confidence interval (CI): 0.51, 1.68; P = 0.795]. We did not find a significant difference for chronic pain (Visual Analog Scale score >3) (OR: 0.58; 95% CI: 0.31, 1.09; P = 0.088) and sensory testing (17% vs 20% of patients; OR: 0.53; 95% CI: 0.21, 1.37; P = 0.190) between the 2 groups. There were less reoperations in the mesh plug than in the Lichtenstein's operation group (OR: 0.43; 95% CI: 0.22, 0.85; P = 0.016). CONCLUSIONS: The long-term results of this trial indicate not enough evidence for differences in recurrence, chronic pain, and sensibility disorders between mesh plug repair and Lichtenstein's operation but a lower likelihood for reoperation for mesh plug repair. Estimates for all endpoints were statistically not significant or based on large CIs. CLINICAL TRIALS REGISTRATION: ClinicalTrials.gov Identifier: NCT01637818.

Development of predictive model for predicting postoperative BMI and optimize bariatric surgery: a single center pilot study.

BACKGROUND: The pilot study addresses the challenge of predicting postoperative outcomes, particularly body mass index (BMI) trajectories, following bariatric surgery. The complexity of this task makes preoperative personalized obesity treatment challenging. OBJECTIVES: To develop and validate sophisticated machine learning (ML) algorithms capable of accurately forecasting BMI reductions up to 5 years following bariatric surgery aiming to enhance planning and postoperative care. The secondary goal involves the creation of an accessible web-based calculator for healthcare professionals. This is the first article that compares these methods in BMI prediction. SETTING: The study was carried out from January 2012 to December 2021 at GZOAdipositas Surgery Center, Switzerland. Preoperatively, data for 1004 patients were available. Six months postoperatively, data for 1098 patients were available. For the time points 12 months, 18 months, 2 years, 3 years, 4 years, and 5 years the following number of follow-ups were available: 971, 898, 829, 693, 589, and 453. METHODS: We conducted a comprehensive retrospective review of adult patients who underwent bariatric surgery (Roux-en-Y gastric bypass or sleeve gastrectomy), focusing on individuals with preoperative and postoperative data. Patients with certain preoperative conditions and those lacking complete data sets were excluded. Additional exclusion criteria were patients with incomplete data or follow-up, pregnancy during the follow-up period, or preoperative BMI ≤30 kg/m2. RESULTS: This study analyzed 1104 patients, with 883 used for model training and 221 for final evaluation, the study achieved reliable predictive capabilities, as measured by root mean square error (RMSE). The RMSE values for three tasks were 2.17 (predicting next BMI value), 1.71 (predicting BMI at any future time point), and 3.49 (predicting the 5-year postoperative BMI curve). These results were showcased through a web application, enhancing clinical accessibility and decision-making. CONCLUSION: This study highlights the potential of ML to significantly improve bariatric surgical outcomes and overall healthcare efficiency through precise BMI predictions and personalized intervention strategies.

Glucose-dependent insulinotropic polypeptide induces cytokine expression, lipolysis, and insulin resistance in human adipocytes.

Obesity-related insulin resistance is linked to a chronic state of systemic and adipose tissue-derived inflammation. Glucose-dependent insulinotropic polypeptide (GIP) is an incretin hormone also acting on adipocytes. We investigated whether GIP affects inflammation, lipolysis, and insulin resistance in human adipocytes. Human subcutaneous preadipocyte-derived adipocytes, differentiated in vitro, were treated with human GIP to analyze mRNA expression and protein secretion of cytokines, glycerol, and free fatty acid release and insulin-induced glucose uptake. GIP induced mRNA expression of IL-6, IL-1ß, and the IL-1 receptor antagonist IL-1Ra, whereas TNFα, IL-8, and monocyte chemotactic protein (MCP)-1 remained unchanged. Cytokine induction involved PKA and the NF-κB pathway as well as an autocrine IL-1 effect. Furthermore, GIP potentiated IL-6 and IL-1Ra secretion in the presence of LPS, IL-1ß, and TNFα. GIP induced lipolysis via activation of hormone-sensitive lipase and was linked to NF-κB activation. Finally, chronic GIP treatment impaired insulin-induced glucose uptake possibly due to the observed impaired translocation of glucose transporter GLUT4. In conclusion, GIP induces an inflammatory and prolipolytic response via the PKA -NF-κB-IL-1 pathway and impairs insulin sensitivity of glucose uptake in human adipocytes.

The development of machine learning in bariatric surgery.

Background: Machine learning (ML), is an approach to data analysis that makes the process of analytical model building automatic. The significance of ML stems from its potential to evaluate big data and achieve quicker and more accurate outcomes. ML has recently witnessed increased adoption in the medical domain. Bariatric surgery, otherwise referred to as weight loss surgery, reflects the series of procedures performed on people demonstrating obesity. This systematic scoping review aims to explore the development of ML in bariatric surgery. Methods: The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR). A comprehensive literature search was performed of several databases including PubMed, Cochrane, and IEEE, and search engines namely Google Scholar. Eligible studies included journals published from 2016 to the current date. The PRESS checklist was used to evaluate the consistency demonstrated during the process. Results: A total of seventeen articles qualified for inclusion in the study. Out of the included studies, sixteen concentrated on the role of ML algorithms in prediction, while one addressed ML's diagnostic capacity. Most articles (n = 15) were journal publications, whereas the rest (n = 2) were papers from conference proceedings. Most included reports were from the United States (n = 6). Most studies addressed neural networks, with convolutional neural networks as the most prevalent. Also, the data type used in most articles (n = 13) was derived from hospital databases, with very few articles (n = 4) collecting original data via observation. Conclusions: This study indicates that ML has numerous benefits in bariatric surgery, however its current application is limited. The evidence suggests that bariatric surgeons can benefit from ML algorithms since they will facilitate the prediction and evaluation of patient outcomes. Also, ML approaches to enhance work processes by making data categorization and analysis easier. However, further large multicenter studies are required to validate results internally and externally as well as explore and address limitations of ML application in bariatric surgery.

Corrigendum: The development of machine learning in bariatric surgery.

[This corrects the article DOI: 10.3389/fsurg.2023.1102711.].

Gastrointestinal Stromal Tumor (GIST): A Remarkable Case Report and Literature Review.

Gastrointestinal stromal tumor (GIST) makes up less than 1% of all gastrointestinal tumors, but it is the most common mesenchymal tumor of the digestive system. It is commonly found in the stomach and the small intestine and rarely seen in the colon and the esophagus. Additionally, sigmoid GIST is quite rare since colorectal GIST often occurs in the rectum. A total of 21 patients (including the study case) were looked at for this study, of which 14 (66.6%) were males and seven (33.3%) were females. We focused on GIST and conducted an online search and systematic analysis of all case presentations.

Development and validation of a predictive model of the hospital cost associated with bariatric surgery.

Hospitals are facing difficulties in predicting, evaluating, and managing cost-affecting parameters in patient treatments. Inaccurate cost prediction leads to a deficit in operational revenue. This study aims to determine the ability of Machine Learning (ML) algorithms to predict the cost of care in bariatric and metabolic surgery and develop a predictive tool for improved cost analysis. 602 patients who underwent bariatric and metabolic surgery at Wetzikon hospital from 2013 to 2019 were included in the study. Multiple variables including patient factors, surgical factors, and post-operative complications were tested using a number of predictive modeling strategies. The study was registered under Req 2022-00659 and approved by an institutional review board. The cost was defined as the sum of all costs incurred during the hospital stay, expressed in CHF (Swiss Francs). The data was preprocessed and split into a training set (80%) and a test set (20%) to build and validate models. The final model was selected based on the mean absolute percentage error (MAPE). The Random Forest model was found to be the most accurate in predicting the overall cost of bariatric surgery with a mean absolute percentage error of 12.7. The study provides evidence that the Random Forest model could be used by hospitals to help with financial calculations and cost-efficient operation. However, further research is needed to improve its accuracy. This study serves as a proof of principle for an efficient ML-based prediction tool to be tested on multi-center data in future phases of the study.

[Management of the post-bariatric surgery patient]. / Der Patient nach bariatrischem Eingriff.

Worldwide the obesity epidemic is becoming one of the leading causes of mortality and morbidity. The rates of bariatric surgery procedures are sharply increasing. Today it is the only treatment option for a substantial and durable long-term weight loss. However, bariatric surgery is not a guarantee of successful weight loss and maintenance. For all patients undergoing bariatric surgery, education and clinical management to prevent and detect nutritional deficiencies are recommended. Particularly for patients undergoing malabsorptive procedures, the management of potential nutritional deficiencies is important. Strategies should be employed to compensate for food intolerance aiming at risk reduction for nutritional deficiencies. All patients should receive care from a multidisciplinary team and be considered for comprehensive perioperative program for nutrition and lifestyle management.

Thyroglossal Duct Cyst, a Case Report and Literature Review.

A thyroglossal duct cyst (TGDC) is one of the most commonly encountered congenital anomalies of the neck. However, it is difficult to diagnose as differentiating it from other cysts like brachial cysts, lymphangiomas, epidermoid cysts, dermoid cysts, and hydatid cysts, is challenging. In this paper, we systematically reviewed the literature of 47 patients-25 males (53.1%) and 21 females (44.7%)-about their TGDC to assess the clinical picture, therapy, and prognosis of the disease. Most of the patients were children under the age of ten (63.8%). All patients had a history of a painless swelling in the anterior midline of the neck that moved in response to deglutition and tongue protrusion, thus interfering with their daily activity. Post-resection recurrence was unusual, with only 3 of 47 patients (6.4%) experiencing recurrence.

The Development of Artificial Intelligence in Hernia Surgery: A Scoping Review.

Background: Artificial intelligence simulates human intelligence in machines that have undergone programming to make them think like human beings and imitate their activities. Artificial intelligence has dominated the medical sector to perform various patient diagnosis activities and improve communication between professionals and patients. The main goal of this study is to perform a scoping review to evaluate the development of artificial intelligence in all forms of hernia surgery except the diaphragm and upside-down hernia. Methods: The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR) to guide the structuring of the manuscript and fulfill all the requirements of every subheading. The sources used to gather data are the PubMed, Cochrane, and EMBASE databases, IEEE and Google and Google Scholar search engines. AMSTAR tool is the most appropriate for assessing the methodological quality of the included studies. Results: The study exclusively included twenty articles, whereby seven focused on artificial intelligence in inguinal hernia surgery, six focused on abdominal hernia surgery, five on incisional hernia surgery, and two on AI in medical imaging and robotics in hernia surgery. Conclusion: The outcomes of this study reveal a significant literature gap on artificial intelligence in hernia surgery. The results also indicate that studies focus on inguinal hernia surgery more than any other types of hernia surgery since the articles addressing the topic are more. The study implies that more research is necessary for the field to develop and enjoy the benefits associated with AI. Thus, this situation will allow the integration of AI in activities like medical imaging and surgeon training.

The Effects of Anastomotic Leaks on the Net Revenue from Colon Surgery.

Background: Complications in colon surgery can have severe health consequences, while at the same time, they are associated with increased costs. An anastomotic leak (AL) is associated with significantly increased costs compared to cases without. The aim of our analysis was to evaluate, which individual processes and patient-unrelated factors influencing the treatment process of colon surgery are responsible for the financial burden in patients with AL. Methods: Data from 263 patients who underwent colon surgery in Wetzikon hospital between January 2018 and December 2020 and was analyzed. In these 263 cases, 12 anastomotic leaks occurred and were compared with 36 cases without AL using a Propensity Score Matching (PSM). The covariates for the PSM have been Age, Sex, and Type of Surgery (t value: −3.26, p-value: 0.001). Results: A total of 48 surgeries were broken down in terms of costs and profitability. This reflected a mean deficit of −37,527 CHF per case (range from −130.05 to +755 CHF) for patients with AL, whereas a mean profit of 1590 CHF per case (range from −24.37 to +12.65 CHF) for those without AL (p < 0.001). Thus, the difference in profit showed a factor of 24.6 with an overall significant negative outcome for the occurrence of AL. The main cost contributing factors were the length of hospital stay (~p < 0.05) and length of intensive care (p < 0.05), whereas neither surgical operation time and anesthesia time nor surgical access, insurance status, indication or type of operation had a significant influence on the net revenue. Conclusion: AL after colon surgery leads to a significant deficit regarding the net revenue. Regarding process optimization, our analysis identified several sectors of non-patient-related, yet cost-influencing variables that should be addressed in future evaluations and optimization of the colon surgery treatment processes.

Cost and Quality Comparison of Hernia Surgery in Stationary, Day-Patient and Outpatient Care.

BACKGROUND: Medical progress is increasingly enabling more and more stationary treatment to be provided in the outpatient sector. This development should be welcomed, as healthcare costs have been rising for years. The design of efficient processes and a needs-based infrastructure enable further savings. According to international recommendations (EHS/IEHS), outpatient treatment of unilateral inguinal hernias is recommended. METHOD: Data from patients in GZO Hospital Wetzikon/Zurich between 2019 and 2021 for unilateral inguinal hernia repair was included in this study (n = 234). Any over- or under-coverage correlated with one of the three treatment groups: stationary, partially stationary and patients treated in outpatients clinic. Complications and 30-day readmissions were also monitored. RESULTS: Final revenue for all patients is -95.36 CHF. For stationary treatments, the mean shifts down to -575.01 CHF, for partially stationary treatments the mean shifts up to -24.73 CHF, and for patients in outpatient clinic final revenue is 793.12 CHF. This result is also consistent with the operation times, which are lowest in the outpatient clinic with a mean of 36 min, significantly longer in the partially stationary setting with 58 min, and longest in the stationary setting with 76 min. The same applies to the anesthesia times and the relevant care times by the nurses as the most important cost factors in addition to the supply and allocation costs. CONCLUSIONS: We show that cost-effective elective unilateral inguinal hernia care in the outpatient clinic with profit (mean 793.12 CHF) is possible. Stationary unilateral hernia care (mean -575.01 CHF) is loss-making. Crucial factors for cost efficiency are optimized processes in the operating room (anesthesia, surgical technique and quality, operating time), as well as optimized care processes with minimal preoperative services and care times for the patient. However, at the same time, these optimizations pose a challenge to surgical and anesthesiology training and structures with high levels of preoperative and Postoperative services and pay-as-you-go costs. The complication rate is 0.91% lower than in a comparable study. The readmission within 30 days post-operation results with a positive deviation of -3.53% (stationary) and with a negative deviation of +2.29% (outpatient clinic) compared to a comparative study.

The Development of Healthcare Jobs in the COVID-Pandemic-A New Economic Market.

Background: The COVID-19 pandemic commenced in China and has caused the death of numerous people globally. Despite the adverse effects, the outbreak has created room for job opportunities in healthcare, particularly the pharmaceutical domain. The main goal of this study is to examine how the current pandemic has triggered job creation in the healthcare domain and created a new economic market. Methods: The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR) to structure the manuscript and the subheadings to use. The source used to gather data is the PubMed database. Results: The study exclusively included fourteen articles, five of which focused on the pharmaceutical sector, three focused on vaccine sales, three on vaccination centers, and three on testing centers. Conclusion: The COVID-19 pandemic has created job opportunities in the healthcare sector. Most jobs are in the pharmaceutical sector, vaccination, and testing centers. However, more comprehensive research on the topic is necessary to gather conclusive outcomes on whether these jobs will be relevant after the pandemic.

Developing and validating a multivariable prediction model for predicting the cost of colon surgery.

Hospitals are burdened with predicting, calculating, and managing various cost-affecting parameters regarding patients and their treatments. Accuracy in cost prediction is further affected when a patient suffers from other health issues that hinder the traditional prognosis. This can lead to an unavoidable deficit in the final revenue of medical centers. This study aims to determine whether machine learning (ML) algorithms can predict cost factors based on patients undergoing colon surgery. For the forecasting, multiple predictors will be taken into the model to provide a tool that can be helpful for hospitals to manage their costs, ultimately leading to operating more cost-efficiently. This proof of principle will lay the groundwork for an efficient ML-based prediction tool based on multicenter data from a range of international centers in the subsequent phases of the study. With a mean absolute percentage error result of 18%-25.6%, our model's prediction showed decent results in forecasting the costs regarding various diagnosed factors and surgical approaches. There is an urgent need for further studies on predicting cost factors, especially for cases with anastomotic leakage, to minimize unnecessary hospital costs.

Intranodal immunization with a vaccinia virus encoding multiple antigenic epitopes and costimulatory molecules in metastatic melanoma.

Recombinant vaccinia virus (rVV) encoding tumor-associated antigens (TAAs) and adhesion or costimulatory molecules may represent important immunogenic reagents for cancer immunotherapy. Recently, intranodal (IN) antigen administration was suggested to be more immunogenic than intradermal (ID) vaccination. However, IN rVV administration has not been attempted so far. We used a rVV encoding gp100(280-288), Melan-A/MART-1(27-35) and tyrosinase(1-9) HLA-A0201 restricted epitopes and CD80 and CD86 costimulatory molecules in stage III and IV melanoma patients in a phase 1/2 trial. Of 15 patients initiating treatment, including two cycles of IN immunization, each comprising one rVV administration and three recall injections of the corresponding peptides, accompanied by subcutaneous granulocyte macrophage-colony stimulating factor supplementation, five withdrew due to progressing disease. Of 10 remaining patients seven showed evidence of induction of cytotoxic T lymphocytes (CTLs) directed against at least one epitope under investigation, as detectable by limiting dilution analysis (LDA) of specific precursors and multimer staining. Adverse reactions were mild (National Cancer Institute (NCI) grade 1-2) and mainly represented by fever, skin rashes, and pruritus. These data indicate that IN administration of rVV encoding melanoma-associated epitopes and costimulatory molecules is safe and immunogenic.