Modulatory immune responses in fungal infection associated with organ transplant - advancements, management, and challenges

Organ transplantation stands as a pivotal achievement in modern medicine, offering hope to individuals with end-stage organ diseases. Advancements in immunology led to improved organ transplant survival through the development of immunosuppressants, but this heightened susceptibility to fungal infections with nonspecific symptoms in recipients. This review aims to establish an intricate balance between immune responses and fungal infections in organ transplant recipients. It explores the fundamental immune mechanisms, recent advances in immune response dynamics, and strategies for immune modulation, encompassing responses to fungal infections, immunomodulatory approaches, diagnostics, treatment challenges, and management. Early diagnosis of fungal infections in transplant patients is emphasized with the understanding that innate immune responses could potentially reduce immunosuppression and promise efficient and safe immuno-modulating treatments. Advances in fungal research and genetic influences on immune-fungal interactions are underscored, as well as the potential of single-cell technologies integrated with machine learning for biomarker discovery. This review provides a snapshot of the complex interplay between immune responses and fungal infections in organ transplantation and underscores key research directions.

Computational design and evaluation of mRNA- and protein-based conjugate vaccines for influenza A and SARS-CoV-2 viruses

BACKGROUND

Israel confirmed the first case of "flurona"-a co-infection of seasonal flu (IAV) and SARS-CoV-2 in an unvaccinated pregnant woman. This twindemic has been confirmed in multiple countries and underscores the importance of managing respiratory viral illnesses.

RESULTS

The novel conjugate vaccine was designed by joining four hemagglutinin, three neuraminidase, and four S protein of B-cell epitopes, two hemagglutinin, three neuraminidase, and four S proteins of MHC-I epitopes, and three hemagglutinin, nine neuraminidase, and five S proteins of MHC-II epitopes with linkers and adjuvants. The constructed conjugate vaccine was found stable, non-toxic, non-allergic, and antigenic with 0.6466 scores. The vaccine contained 14.87% alpha helix, 29.85% extended strand, 9.64% beta-turn, and 45.64% random coil, which was modeled to a 3D structure with 94.7% residues in the most favored region of the Ramachandran plot and Z-score of -3.33. The molecular docking of the vaccine with TLR3 represented -1513.9 kcal/mol of binding energy with 39 hydrogen bonds and 514 non-bonded contacts, and 1.582925e-07 of eigenvalue complex. Immune stimulation prediction showed the conjugate vaccine could activate T and B lymphocytes to produce high levels of Th1 cytokines and antibodies.

CONCLUSION

The in silico-designed vaccine against IAV and SARS-CoV-2 showed good population coverage and immune response with predicted T- and B-cell epitopes, favorable molecular docking, Ramachandran plot results, and good protein expression. It fulfilled safety criteria, indicating potential for preclinical studies and experimental clinical trials.

Developing machine-learning-based models to diminish the severity of injuries sustained by pedestrians in road traffic incidents

An essential step in devising measures to improve road safety is road accident prediction. In particular, it is important to identify the risk factors that increase the likelihood of severe injuries in the event of an accident. There are two distinct ways of analyzing data in order to produce predictions: machine learning and statistical methods. This study explores the severity of road traffic injuries sustained by pedestrians through the use of machine-learning methodology. In general, the goal of the statistician is to model and understand the connections between variables, whereas machine learning focuses on more intricate and expansive datasets, with the aim of creating algorithms that can recognize patterns and make predictions without being explicitly programmed. The ability to handle very large datasets constitutes a distinct advantage of machine learning over statistical techniques. In addition, machine-learning models can be adapted to a wide range of data sources and problem domains, and can be utilized for numerous tasks, from image identification to natural language processing. Machine-learning models may be taught to recognize patterns and make predictions automatically, minimizing the need for manual involvement and enabling rapid data processing of enormous quantities of data. The use of new data to retrain or fine-tune a machine-learning model allows the model to adapt to changing conditions and enhances its accuracy over time. Finally, while non-linear interactions between variables can be difficult to predict using conventional statistical techniques, they can be recognized by machine-learning models. The study begins by compiling an inventory of features linked to both the accident and the environment, focusing on those that exert the greatest influence on the severity of pedestrian injuries. The "optimal" algorithm is then chosen based on its superior levels of accuracy, precision, recall, and F1 score. The developed model should not be regarded as fixed; it should be updated and retrained on a regular basis using new traffic accident data that mirror the evolving interplay between the road environment, driver characteristics, and pedestrian conduct. Having been constructed using Israeli data, the current model is predictive of injury outcomes within Israel. For broader applicability, the model should undergo retraining and reassessment using traffic accident data from the pertinent country or region.

Immunoinformatic-guided designing and evaluating protein and mRNA-based vaccines against Cryptococcus neoformans for immunocompromised patients

BACKGROUND

Cryptococcus neoformans is a fungal pathogen that can cause serious meningoencephalitis in individuals with compromised immune systems due to HIV/AIDS (human immunodeficiency virus/acquired immunodeficiency syndrome), liver cirrhosis, and transplantation. Mannoproteins (MPs), glycoproteins in the C. neoformans capsule, crucially impact virulence by mediating adhesion to lung cells and modulating immune response via cytokine induction and phagocytosis influence. Therefore, creating a vaccine that can generate targeted antibodies to fight infection and prevent fungal illnesses is essential.

RESULTS

This research aims to create a unique, stable, and safe vaccine through bioinformatics methodologies, aiming at epitopes of T and B cells found in the MP of C. neoformans. Based on toxicity, immunogenicity, and antigenicity, this research predicted novel T cells (GNPVGGNVT, NPVGGNVTT, QTSYARLLS, TSVGNGIAS, WVMPGDYTN, AAATGSSSSGSTGSG, GSTGSGSGSAAAGST, SGSTGSGSGSAAAGS, SSGSTGSGSGSAAAG, and SSSGSTGSGSGSAAA) and B cell (ANGSTSTFQQRYTGTYTNGDGSLGTWTQGETVTPQTAYSTPATSNCKTYTSVGNGIASLALSNAGSNSTAAATNSSSGGASAAATGSSSSGSTGSGSGSAAAGSTAAASSSGDSSSSTSAAMSNGI, HGATGLGNPVGGNVTT, TMGPTNPSEPTLGTAI, GNPVGGNVTTNATGSD, and NSTAAATNSSSGGASA) epitopes for a multiple-epitope vaccine and constructed a vaccine subunit with potential immunogenic properties. The present study used four linkers (AAY, GPGPG, KK, and EAAAK linkers) to connect the epitopes and adjuvant. After constructing the vaccine, it was confronted with receptor docking and simulation analysis. Subsequently, the vaccine was cloned into the vector of Escherichia coli pET-28a ( +) by ligation process for the expression using the SnapGene tool, which confirmed a significant immune response. To assess the constructed vaccine's properties, multiple computational tools were employed. Based on the MP sequence, the tools evaluated the antigenicity, immunogenicity, cytokine-inducing capacity, allergenicity, toxicity, population coverage, and solubility.

CONCLUSION

Eventually, the results revealed a promising multi-epitope vaccine as a potential candidate for addressing global C. neoformans infection, particularly in immunocompromised patients. Yet, additional in vitro and in vivo investigations are necessary to validate its safety and effectiveness.

[SEASONALITY, HOLIDAYS AND THE DEMAND FOR URGENT PSYCHIATRIC CARE IN ISRAEL]

INTRODUCTION

The demand for urgent psychiatric services provided in emergency departments demonstrates high variability throughout the year. Seasonality and holidays may influence this demand. While these two phenomena are widely documented for mental healthcare, the unique climate and Israeli population, which is mostly Jewish, raises doubts regarding the relevance of prior results.

Calculating Strategic Risk in Financial Institutions

Banks face many intangible hazards that are difficult to calculate. Strategic risk is one of the most critical factors affecting a bank's profitability, financial strength, and commercial success. The impact of risk on profit may be insignificant in the short term. Still, it may become highly significant in the medium and long term, with the potential to cause substantial financial losses and impair bank stability. Hence, strategic risk management is an important endeavor that must be carried out according to the rules set out under the Basel II framework. Analysis of strategic risk is a relatively new research enterprise. The current literature addresses the need to manage this risk and links it to the concept of economic capital, the amount of capital that a company should hold to survive such a risk. However, an action plan has yet to be produced. This paper attempts to address this gap by providing a mathematical analysis of the probability and effect of different strategic risk factors. Specifically, we develop a methodology for calculating a metric of strategic risk in terms of a bank's risk assets. Furthermore, we suggest a way of integrating this metric into the calculation of the capital adequacy ratio.

Infections after organ transplantation and immune response

Organ transplantation has provided another chance of survival for end-stage organ failure patients. Yet, transplant rejection is still a main challenging factor. Immunosuppressive drugs have been used to avoid rejection and suppress the immune response against allografts. Thus, immunosuppressants increase the risk of infection in immunocompromised organ transplant recipients. The infection risk reflects the relationship between the nature and severity of immunosuppression and infectious diseases. Furthermore, immunosuppressants show an immunological impact on the genetics of innate and adaptive immune responses. This effect usually reactivates the post-transplant infection in the donor and recipient tissues since T-cell activation has a substantial role in allograft rejection. Meanwhile, different infections have been found to activate the T-cells into CD₄⁺ helper T-cell subset and CD₈⁺ cytotoxic T-lymphocyte that affect the infection and the allograft. Therefore, the best management and preventive strategies of immunosuppression, antimicrobial prophylaxis, and intensive medical care are required for successful organ transplantation. This review addresses the activation of immune responses against different infections in immunocompromised individuals after organ transplantation.

In-silico Structural Modeling of Human Immunodeficiency Virus Proteins

Human immunodeficiency virus (HIV) is an infectious virus that depletes the CD4⁺ T lymphocytes of the immune system and causes a chronic life-treating disease-acquired immunodeficiency syndrome (AIDS). The HIV genome encodes different structural and accessory proteins involved in viral entry and life cycle. Determining the 3D structure of HIV proteins is essential for new target position finding, structure-based drug designing, and future planning for computational and laboratory experimentations. Hence, the study aims to predict the 3D structures of all the HIV structural and accessory proteins using computational homology modeling to understand better the structural basis of HIV proteins interacting with host cells and viral replication. The sequences of HIV capsid, matrix, nucleocapsid, p6, reverse transcriptase, invertase, protease, gp120, gp41, virus protein r, viral infectivity factor, virus protein unique, RNA splicing regulator, transactivator protein, negative regulating factor, and virus protein x proteins were retrieved from UniProt. The primary and secondary structures of HIV proteins were predicted by Expasy ProtParam and SOPMA web servers. For the homology modeling, the MODELLER predicted the 3D structures of HIV proteins using templates. Then, the modeled structures were validated by the Ramachandran plot, local and global quality estimation scores, QMEAN scores, and Z-scores. Most of the amino acid residues of HIV proteins were present in the most favored and generously allowed regions in the Ramachandran plots. The local and global quality scores and Z-scores of the HIV proteins confirmed the good quality of modeled structures. The 3D modeled structures of HIV proteins might help further investigate the possible treatment.

Queueing Problems in Emergency Departments: A Review of Practical Approaches and Research Methodologies

Problems related to patient scheduling and queueing in emergency departments are gaining increasing attention in theory, in the fields of operations research and emergency and healthcare services, and in practice. This paper aims to provide an extensive review of studies addressing queueing-related problems explicitly related to emergency departments. We have reviewed 229 articles and books spanning seven decades and have sought to organize the information they contain in a manner that is accessible and useful to researchers seeking to gain knowledge on specific aspects of such problems. We begin by presenting a historical overview of applications of queueing theory to healthcare-related problems. We subsequently elaborate on managerial approaches used to enhance efficiency in emergency departments. These approaches include bed management, fast-track, dynamic resource allocation, grouping/prioritization of patients, and triage approaches. Finally, we discuss scientific methodologies used to analyze and optimize these approaches: algorithms, priority models, queueing models, simulation, and statistical approaches.

Immune response against the biomaterials used in 3D bioprinting of organs

Regenerative medicine has developed promising approaches for healing and replacing defective and damaged organs or tissues with functional ones. Three-dimensional (3D) bioprinting innovation has integrated a potential to design organs or tissues specific to the patient with the capability of rapid construction to fulfill the storage of organs and the need for transplantation. 3D bioprinting of organs has the main goal to develop a structural and functional organ or tissue mimic to the original one. The highly complex fabrication of tissue engineering scaffolds containing biomaterials, tissue models, and biomedical devices has made it possible to print small blood vessels to mimic organs to reduce organ or tissue rejection. 3D bioprinting has the concept of bioinks containing biomaterials that may trigger the immune responses in the body. Nevertheless, foreign body response (FBR) is mediated by various cell types such as B-cells, dendritic cells, macrophages, natural killer cells, neutrophils, and T-cells, and molecular signals such as antibodies (Abs), cytokines, and reactive radical species. Typically, the biomaterial is shielded by the fibrous encapsulation that is regulated by molecular signals. This review explored the progress in 3D bioprinting of vital organs and basic immune response against the biomaterials used in this approach. Thus, evaluating immune response against biomaterials used in 3D printed organs is necessary to mitigate tissue rejection after the transplantation.

A simulation-based approach for improving the clinical blood sample supply chain

We consider a three-echelon blood sample supply chain comprising the following elements: (i) clinics, where blood samples are taken from patients, (ii) centrifugation centers, where collected blood samples are separated into their different components, and (iii) a centralized testing laboratory, where the samples are analyzed. We focus on the scheduling of vehicles that transport blood samples from clinics to centrifugation centers-a special case of the vehicle routing problem (VRP). Our study presents a novel simulation-based approach to the VRP, designed and implemented in MATLAB, and tailored to the unique constraints of the three-echelon blood sample collection chain. We apply this approach to data from a large Health Maintenance Organization to determine the optimal vehicle fleet size for blood sample transport, while ensuring that the quality of the healthcare service is not compromised. Results suggest that our simulation model can be generalized to serve as a useful and straightforward decision support tool for optimizing resource utilization and service quality in healthcare systems.

Addressing overcrowding in an emergency department: an approach for identifying and treating influential factors and a real-life application

Background

Overcrowding in hospital emergency departments that arises from long length-of-stay is an unfortunate common occurrence. While some factors affecting length-of-stay are well known, there may be additional factors that have not yet been properly addressed. This research offers a method for emergency department managers to use available data from their departments to identify new factors that significantly influence emergency departments crowding and patient length-of-stay.

Methods

We propose an algorithm that can assist emergency department managers in determining which of these factors to address, given budgetary constraints. We implemented it in a case study which takes into account factors that are known to be influential, e.g., reason for arrival, occupancy in the emergency department, and arrival time, as well as factors that are explored for the first time in this paper, such as patient heart rate, the number of accompanying escorts, and the number of tests assigned to patients (e.g., blood tests and urinalysis).

Results

All the implemented and new factors are shown to have a significant influence on the length-of-stay and crowding. We also obtained additional support for our results by interviewing emergency departments physicians and nurses from various hospitals.

Conclusions

It is expected that, by taking all the above factors into consideration, emergency departments efficiency can be improved. The algorithm constructed here allows the choice of the most cost-effective factors to be improved, subject to a given budget. We have been able to derive practical recommendations that emergency departments managers might use to limit crowding and patient length-of-stay.

Attitudes, knowledge, and preferences of the Israeli public regarding the allocation of donor organs for transplantation

Background

There is a stark disparity between the number of patients awaiting deceased-donor organ transplants and the rate at which organs become available. Though organs for transplantation are assumed to be a community resource, and the organ supply depends on public willingness to donate, current allocation schemes do not explicitly incorporate public priorities and preferences. This paper seeks to provide insights regarding the Israeli public’s preferences regarding criteria for organ (specifically, kidney) allocation, and to determine whether these preferences are in line with current allocation policies.

Methods

A market research company administered a telephone survey to 604 adult participants representing the Jewish-Israeli public (age range: 18–95; 50% male). The questionnaire comprised 39 questions addressing participants’ knowledge, attitudes, and preferences regarding organ donation and criteria for organ allocation, including willingness to donate.

Results

The criteria that respondents marked as most important in prioritizing waitlist candidates were maximum medical benefit (51.3% of respondents) and waiting time (21%). Donor status (i.e., whether the candidate is registered as an organ donor) was ranked by 43% as the least significant criterion. Most participants expressed willingness to donate the organs of a deceased relative; notably, they indicated that they would be significantly more willing to donate if organ allocation policies took their preferences regarding allocation criteria into account. Unlike individuals in other countries (e.g., the UK, the US, and Australia) who responded to similar surveys, Israeli survey respondents did not assign high importance to the candidate’s age (24% ranked it as the least important factor). Interestingly, in some cases, participants’ declared preferences regarding the importance of various allocation criteria diverged from their actual choices in hypothetical organ allocation scenarios.

Conclusions

The findings of this survey indicate that Israel’s citizens are willing to take part in decisions about organ allocation. Respondents did not seem to have a strict definition or concept of what they deem to be just; yet, in general, their preferences are compatible with current policy. Importantly, participants noted that they would be more willing to donate organs if their preferences were integrated into the allocation policy. Accordingly, we propose that allocation systems must strive to respect community values and perceptions while maintaining continued clinical effectiveness.