There has been an important change in the clinical characteristics and immune profile of Coronavirus disease 2019 (COVID‐19) patients during the pandemic thanks to the extensive vaccination programs. Here, we highlight recent studies on COVID‐19, from the clinical and immunological characteristics to the protective and risk factors for severity and mortality of COVID‐19. The efficacy of the COVID‐19 vaccines and potential allergic reactions after administration are also discussed. The occurrence of new variants of concerns such as Omicron BA.2, BA.4, and BA.5 and the global administration of COVID‐19 vaccines have changed the clinical scenario of COVID‐19. Multisystem inflammatory syndrome in children (MIS‐C) may cause severe and heterogeneous disease but with a lower mortality rate. Perturbations in immunity of T cells, B cells, and mast cells, as well as autoantibodies and metabolic reprogramming may contribute to the long‐term symptoms of COVID‐19. There is conflicting evidence about whether atopic diseases, such as allergic asthma and rhinitis, are associated with a lower susceptibility and better outcomes of COVID‐19. At the beginning of pandemic, the European Academy of Allergy and Clinical Immunology (EAACI) developed guidelines that provided timely information for the management of allergic diseases and preventive measures to reduce transmission in the allergic clinics. The global distribution of COVID‐19 vaccines and emerging severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) variants with reduced pathogenic potential dramatically decreased the morbidity, severity, and mortality of COVID‐19. Nevertheless, breakthrough infection remains a challenge for disease control. Hypersensitivity reactions (HSR) to COVID‐19 vaccines are low compared to other vaccines, and these were addressed in EAACI statements that provided indications for the management of allergic reactions, including anaphylaxis to COVID‐19 vaccines. We have gained a depth knowledge and experience in the over 2 years since the start of the pandemic, and yet a full eradication of SARS‐CoV‐2 is not on the horizon. Novel strategies are warranted to prevent severe disease in high‐risk groups, the development of MIS‐C and long COVID‐19.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/all.15593

Recent developments in the immunopathology of COVID‐19

Allergic diseases are a major public health problem with increasing prevalence. These immune-mediated diseases are characterized by defective epithelial barriers, which are explained by the epithelial barrier theory and continuously emerging evidence. Environmental exposures (exposome) including global warming, changes and loss of biodiversity, pollution, pathogens, allergens and mites, laundry and dishwasher detergents, surfactants, shampoos, body cleaners and household cleaners, microplastics, nanoparticles, toothpaste, enzymes and emulsifiers in processed foods, and dietary habits are responsible for the mucosal and skin barrier disruption. Exposure to barrier-damaging agents causes epithelial cell injury and barrier damage, colonization of opportunistic pathogens, loss of commensal bacteria, decreased microbiota diversity, bacterial translocation, allergic sensitization, and inflammation in the periepithelial area. Here, we review scientific evidence on the environmental components that impact epithelial barriers and microbiome composition and their influence on asthma and allergic diseases. We also discuss the historical overview of allergic diseases and the evolution of the hygiene hypothesis with theoretical evidence.

Epithelial Barrier Theory: The Role of Exposome, Microbiome, and Barrier Function in Allergic Diseases

BACKGROUND
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can result in a prolonged multisystem disorder termed long COVID, which may affect up to 10% of people following coronavirus disease 2019 (COVID-19). It is currently unclear why certain individuals do not fully recover following SARS-CoV-2 infection.


SUMMARY
In this review, we examine immunological mechanisms that may underpin the pathophysiology of long COVID. These mechanisms include an inappropriate immune response to acute SARS-CoV-2 infection, immune cell exhaustion, immune cell metabolic reprogramming, a persistent SARS-CoV-2 reservoir, reactivation of other viruses, inflammatory responses impacting the central nervous system, autoimmunity, microbiome dysbiosis, and dietary factors.


KEY MESSAGES
Unfortunately, the currently available diagnostic and treatment options for long COVID are inadequate, and more clinical trials are needed that match experimental interventions to underlying immunological mechanisms.

Immune Mechanisms Underpinning Long COVID: Collegium Internationale Allergologicum Update 2024.

The COVID-19 pandemic demonstrated the need to develop strategies to control a new viral infection. However, the different characteristics of the health system and population of each country and hospital would require the implementation of self-systems adapted to their characteristics. The objective of this work was to determine predictors that should identify the most severe patients with COVID-19 infection. Given the poor situation of the hospitals in the first wave, the analysis of the data from that period with an accurate and fast technique can be an important contribution. In this regard, machine learning is able to objectively analyze data in hourly sets and is used in many fields. This study included 291 patients admitted to a hospital in Spain during the first three months of the pandemic. After screening seventy-one features with machine learning methods, the variables with the greatest influence on predicting mortality in this population were lymphocyte count, urea, FiO2, potassium, and serum pH. The XGB method achieved the highest accuracy, with a precision of >95%. Our study shows that the machine learning-based system can identify patterns and, thus, create a tool to help hospitals classify patients according to their severity of illness in order to optimize admission.

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

BACKGROUND
An impaired epithelial barrier integrity in the gastrointestinal tract is important to the pathogenesis of many inflammatory diseases. Accordingly, we assessed the potential of biomarkers of epithelial barrier dysfunction as predictive of severe COVID-19.


METHODS
Levels of bacterial DNA and zonulin family peptides (ZFP) as markers of bacterial translocation and intestinal permeability and a total of 180 immune and inflammatory proteins were analyzed from the sera of 328 COVID-19 patients and 49 healthy controls.


RESULTS
Significantly high levels of circulating bacterial DNA were detected in severe COVID-19 cases. In mild COVID-19 cases, serum bacterial DNA levels were significantly lower than in healthy controls suggesting epithelial barrier tightness as a predictor of a mild disease course. COVID-19 patients were characterized by significantly elevated levels of circulating ZFP. We identified 36 proteins as potential early biomarkers of COVID-19, and six of them (AREG, AXIN1, CLEC4C, CXCL10, CXCL11, and TRANCE) correlated strongly with bacterial translocation and can be used to predict and discriminate severe cases from healthy controls and mild cases (area under the curve (AUC): 1 and 0.88, respectively). Proteomic analysis of the serum of 21 patients with moderate disease at admission which progressed to severe disease revealed 10 proteins associated with disease progression and mortality (AUC: 0.88), including CLEC7A, EIF4EBP1, TRANCE, CXCL10, HGF, KRT19, LAMP3, CKAP4, CXADR, and ITGB6.


CONCLUSION
Our results demonstrate that biomarkers of intact or defective epithelial barriers are associated with disease severity and can provide early information on the prediction at the time of hospital admission.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/all.15800

Disrupted epithelial permeability as a predictor of severe COVID-19 development.

In the current COVID-19 pandemic, clinicians require a manageable set of decisive parameters that can be used to (i) rapidly identify SARS-CoV-2 positive patients, (ii) identify patients with a high risk of a fatal outcome on hospital admission, and (iii) recognize longitudinal warning signs of a possible fatal outcome.This comparative study was performed in 515 patients in the Maria Skłodowska-Curie Specialty Voivodeship Hospital in Zgierz, Poland. The study groups comprised 314 patients with COVID-like symptoms who tested negative and 201 patients who tested positive for SARS-CoV-2 infection; of the latter, 72 patients with COVID-19 died and 129 were released from hospital. Data on which we trained several machine learning (ML) models included clinical findings on admission and during hospitalization, symptoms, epidemiological risk, and reported comorbidities and medications.We identified a set of eight on-admission parameters: white blood cells, antibody-synthesizing lymphocytes, ratios of basophils/lymphocytes, platelets/neutrophils, and monocytes/lymphocytes, procalcitonin, creatinine, and C-reactive protein. The medical decision tree built using these parameters differentiated between SARS-CoV-2 positive and negative patients with up to 90-100% accuracy. Patients with COVID-19 who on hospital admission were older, had higher procalcitonin, C-reactive protein, and troponin I levels together with lower hemoglobin and platelets/neutrophils ratio were found to be at highest risk of death from COVID-19. Furthermore, we identified longitudinal patterns in C-reactive protein, white blood cells, and D dimer that predicted the disease outcome.Our study provides sets of easily obtainable parameters that allow one to assess the status of a patient with SARS-CoV-2 infection, and the risk of a fatal disease outcome on hospital admission and during the course of the disease. 

/pdf/machine-learning-successfully-detects-patients-with-covid-19-35dsw0uo.pdf

Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study

/pdf/sirtuin-1-and-sirtuin-2-plasma-concentrations-in-patients-154boyxj.pdf

Sirtuin 1 and Sirtuin 2 Plasma Concentrations in Patients with Ascending Aortic Dissection and Ascending Aortic Aneurysm

Background: In the current pandemic, oversaturation of hospitals with patients with SARS-CoV-2 infection-like symptoms and an excess of hospitalized COVID-19 patients led to a global healthcare crisis. The aim of our study was to find a manageable set of decisive parameters that can be used to i) rapidly identify SARS-CoV-2 positive patients, ii) identify high-risk patients, and iii) recognize longitudinal warning signs of a possible fatal outcome. Methods: We trained several machine learning (ML) models using data on reported comorbidities, medications, symptoms, and laboratory parameters on hospital admission, and over the disease course in 201 SARS-CoV-2 positive and 314 SARS-CoV-2 negative subjects with a COVID-19-like clinical presentation. Findings: We identified a set of eight on-admission parameters: white blood cells, antibody-synthesizing lymphocytes, ratios of basophils/lymphocytes, platelets/neutrophils, and monocytes/lymphocytes, procalcitonin, creatinine, and C-reactive protein. The medical decision tree built using these parameters differentiated between SARS-CoV-2 positive and negative patients with up to 90-100% accuracy. Next, we determined that COVID-19 patients who on hospital admission were older, had higher procalcitonin, C-reactive protein, and troponin I together with lower hemoglobin and platelets/neutrophils ratio, were at highest risk of death from COVID-19. Finally, we identified patterns of changes in C-reactive protein, white blood cells, and D-Dimer that predicted the disease outcome. Interpretation: Our study provides sets of easily obtainable parameters that allow to assess a SARS-CoV-2 patient’s status prior to RT-PCR results and the dynamics of the disease, based on which the hospital logistics and treatment can be planned. Funding Information: This work has been supported by the Medical University of Lodz, Poland, by the Swiss Institute of Allergy and Asthma Research (SIAF), and by the Center for Data Analysis, Visualization and Simulation (DAViS) that is funded by the Swiss canton of Grisons. Declaration of Interests: MiSo reports research grants from Swiss National Science Foundation, GSK, Novartis and speakers fee from AstraZeneca and a board secretary position of the Basic and Clinical Immunology Section of the European Academy of Allergy and Clinical Immunology (EAACI). JM, FS, DZ, MaS, and KB report no competing interests. Ethics Approval Statement: The project has been accepted by the Ethical Committee of Medical University of Lodz, Poland (Nr. 228 RNN/126/20/KE). Keywords: COVID-19 prognosis, laboratory parameters, machine learning, predictive features, SARS-CoV-2 diagnosis

Machine Learning Successfully Detects COVID-19 Patients Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters

A rare case of Rosai-Dorfman disease mimicking a malignant lymphoproliferative process and IgG4-related disease

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Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study

Sirtuin 1 and Sirtuin 2 Plasma Concentrations in Patients with Ascending Aortic Dissection and Ascending Aortic Aneurysm

Machine Learning Successfully Detects COVID-19 Patients Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters

A rare case of Rosai-Dorfman disease mimicking a malignant lymphoproliferative process and IgG4-related disease