Eosinophils orchestrate cancer rejection by normalizing tumor vessels and enhancing infiltration of CD8(+) T cells.

Tumor-associated eosinophilia is frequently observed in cancer. However, despite numerous studies of patients with cancer and mouse models of cancer, it has remained uncertain if eosinophils contribute to tumor immunity or are mere bystander cells. Here we report that activated eosinophils were essential for tumor rejection in the presence of tumor-specific CD8(+) T cells. Tumor-homing eosinophils secreted chemoattractants that guided T cells into the tumor, which resulted in tumor eradication and survival. Activated eosinophils initiated substantial changes in the tumor microenvironment, including macrophage polarization and normalization of the tumor vasculature, which are known to promote tumor rejection. Thus, our study presents a new concept for eosinophils in cancer that may lead to novel therapeutic strategies.

Outcomes of Patients Living with HIV Hospitalized due to COVID-19: A 3-Year Nationwide Study (2020-2022).

Scientific reports on the association between human immunodeficiency virus (HIV) in patients with COVID-19 and mortality have not been in agreement. In this nationwide study, we described and analyzed the demographic and clinical characteristics of people living with HIV (PLWH) and established that HIV infection is a risk factor for mortality in patients hospitalized due to COVID-19. We collected data from the National Hospital Data Information System at Hospitalization between 2020 and 2022. We included patients admitted to the hospital with a diagnosis of COVID-19. We established a cohort of patients with PLWH and compared them to patients without HIV (non-PLWH). For multivariate analyses, we performed binary logistic regression, using mortality as the dependent variable. To improve the interpretability of the results we also applied penalized regression and random forest, two well-known machine-learning algorithms. A broad range of comorbidities, as well as sex and age data, were included in the final model as adjusted estimators. Our data of 1,188,160 patients included 6,973 PLWH. The estimated hospitalization rate in this set was between 1.43% and 1.70%, while the rate among the general population was 0.83%. Among patients with COVID-19, HIV infection was a risk factor for mortality with an odds ratio (OR) of 1.25 (95% CI, 1.14-1.37, p < 0.001). PLWH are more likely to be hospitalized due to COVID-19 than are non-PLWH. PLWH are 25% more likely to die due to COVID-19 than non-PLWH. Our results highlight that PLWH should be considered a population at risk for both hospitalization and mortality.

Hospitalization burden and epidemiology of the COVID-19 pandemic in Spain (2020-2021).

BACKGROUND: Spain had some of Europe's highest incidence and mortality rates for coronavirus disease 2019 (COVID-19). Here we describe the epidemiology and trends in hospitalizations, the number of critical patients, and deaths in Spain in 2020 and 2021. METHODS: We performed a descriptive, retrospective, nationwide study using an administrative database, the Minimum Basic Data Set at Hospitalization, which includes 95-97% of discharge reports for patients hospitalized in Spain in 2020 and 2021. We analyzed the number of hospitalizations, admissions to intensive care units, and deaths and their geographic distribution across regions of Spain. RESULTS: As of December 31, 2021, a total of 498,789 patients (1.04% of the entire Spanish population) had needed hospitalization. At least six waves of illness were identified. Men were more prone to hospitalization than women. The median age was 66. A total of 54,340 patients (10.9% of all hospitalizations) had been admitted to the intensive care unit. We identified 71,437 deaths (mortality rate of 14.3% among hospitalized patients). We also observed important differences among regions, with Madrid being the epicenter of hospitalizations and mortality. CONCLUSIONS: We analyzed Spain's response to COVID-19 and describe here its experiences during the pandemic in terms of hospitalizations, critical illness, and deaths. This research highlights changes over several months and waves and the importance of factors such as vaccination, the predominant variant of the virus, and public health interventions in the rise and fall of the outbreaks.

Retrospective Cohort Noninferiority Analysis of Barrier Cream in Preventing Pressure Injuries.

OBJECTIVE: To assess the effectiveness of a dimethicone- and zinc-based barrier cream compared with hyperoxygenated fatty acids in preventing pressure injuries (PIs) in patients at high or very high risk. METHODS: Researchers conducted a retrospective noninferiority study in an inpatient acute care hospital in Spain that included hospitalized patients in nonsurgical departments with impaired mobility. RESULTS: The study authors reviewed 522 patients in a control group (hyperoxygenated fatty acids) and an experimental group (barrier cream) over a period of 7 days. The incidence of PI was 31% in the control group and 31.1% in the experimental group. The hazard ratio for developing PI was 0.84 (confidence interval, 0.61-1.17; P = .32) in the experimental group compared with the control group, meeting the criteria for noninferiority. The Kaplan-Meier estimator indicated no statistically significant difference between groups (log-rank = 0.654). CONCLUSIONS: Dimethicone- and zinc-based barrier cream was not inferior to hyperoxygenated fatty acids in preventing PIs in hospitalized patients at high or very high risk of developing them during their hospital stay.

Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest.

Nonalcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome and is the most common cause of chronic liver disease in developed countries. Certain conditions, including mild inflammation biomarkers, dyslipidemia, and insulin resistance, can trigger a progression to nonalcoholic steatohepatitis (NASH), a condition characterized by inflammation and liver cell damage. We demonstrate the usefulness of machine learning with a case study to analyze the most important features in random forest (RF) models for predicting patients at risk of developing NASH. We collected data from patients who attended the Cardiovascular Risk Unit of Mostoles University Hospital (Madrid, Spain) from 2005 to 2021. We reviewed electronic health records to assess the presence of NASH, which was used as the outcome. We chose RF as the algorithm to develop six models using different pre-processing strategies. The performance metrics was evaluated to choose an optimized model. Finally, several interpretability techniques, such as feature importance, contribution of each feature to predictions, and partial dependence plots, were used to understand and explain the model to help obtain a better understanding of machine learning-based predictions. In total, 1525 patients met the inclusion criteria. The mean age was 57.3 years, and 507 patients had NASH (prevalence of 33.2%). Filter methods (the chi-square and Mann-Whitney-Wilcoxon tests) did not produce additional insight in terms of interactions, contributions, or relationships among variables and their outcomes. The random forest model correctly classified patients with NASH to an accuracy of 0.87 in the best model and to 0.79 in the worst one. Four features were the most relevant: insulin resistance, ferritin, serum levels of insulin, and triglycerides. The contribution of each feature was assessed via partial dependence plots. Random forest-based modeling demonstrated that machine learning can be used to improve interpretability, produce understanding of the modeled behavior, and demonstrate how far certain features can contribute to predictions.

Pulse Wave Velocity and Machine Learning to Predict Cardiovascular Outcomes in Prediabetic and Diabetic Populations.

Few studies have addressed the predictive value of arterial stiffness determined by pulse wave velocity (PWV) in a high-risk population with no prevalent cardiovascular disease and with obesity, hypertension, hyperglycemia, and preserved kidney function. This longitudinal, retrospective study enrolled 88 high-risk patients and had a follow-up time of 12.4 years. We collected clinical and laboratory data, as well as information on arterial stiffness parameters using arterial tonometry and measurements from ambulatory blood pressure monitoring. We considered nonfatal, incident cardiovascular events as the primary outcome. Given the small size of our dataset, we used survival analysis (i.e., Cox proportional hazards model) combined with a machine learning-based algorithm/penalization method to evaluate the data. Our predictive model, calculated with Cox regression and least absolute shrinkage and selection operator (LASSO), included body mass index, diabetes mellitus, gender (male), and PWV. We recorded 16 nonfatal cardiovascular events (5 myocardial infarctions, 5 episodes of heart failure, and 6 strokes). The adjusted hazard ratio for PWV was 1.199 (95% confidence interval: 1.09-1.37, p < 0.001). Arterial stiffness was a predictor of cardiovascular disease development, as determined by PWV in a high-risk population. Thus, in obese, hypertensive, hyperglycemic patients with preserved kidney function, PWV can serve as a prognostic factor for major adverse cardiac events.

Glucose time series complexity as a predictor of type 2 diabetes.

BACKGROUND: Complexity analysis of glucose profile may provide valuable information about the gluco-regulatory system. We hypothesized that a complexity metric (detrended fluctuation analysis, DFA) may have a prognostic value for the development of type 2 diabetes in patients at risk. METHODS: A total of 206 patients with any of the following risk factors (1) essential hypertension, (2) obesity or (3) a first-degree relative with a diagnosis of diabetes were included in a survival analysis study for a diagnosis of new onset type 2 diabetes. At inclusion, a glucometry by means of a Continuous Glucose Monitoring System was performed, and DFA was calculated for a 24-h glucose time series. Patients were then followed up every 6 months, controlling for the development of diabetes. RESULTS: In a median follow-up of 18 months, there were 18 new cases of diabetes (58.5 cases/1000 patient-years). DFA was a significant predictor for the development of diabetes, with ten events in the highest quartile versus one in the lowest (log-rank test chi2 = 9, df = 1, p = 0.003), even after adjusting for other relevant clinical and biochemical variables. In a Cox model, the risk of diabetes development increased 2.8 times for every 0.1 DFA units. In a multivariate analysis, only fasting glucose, HbA1c and DFA emerged as significant factors. CONCLUSIONS: Detrended fluctuation analysis significantly performed as a harbinger of type 2 diabetes development in a high-risk population. Complexity analysis may help in targeting patients who could be candidates for intensified treatment. Copyright © 2016 The Authors. Diabetes/Metabolism Research and Reviews Published by John Wiley & Sons Ltd.

Antibody therapy to human L1CAM in a transgenic mouse model blocks local tumor growth but induces EMT.

L1 cell adhesion molecule (L1CAM) is overexpressed in many human cancers, confers bad prognosis and augments cell motility, invasion and metastasis. Results from xenograft mouse models suggested that L1CAM antibodies might be promising tools for cancer therapy. Here, we generated human L1CAM-transgenic mice to study therapeutic efficacy and putative side effects in a model system. We established three transgenic lines (M2, M3 and F4) expressing the human L1CAM transgene in brain, kidney and colon with decreasing intensity (M2, M3 > F4). The expression pattern was similar to that of L1CAM in humans. No interference of the transgene with the expression of endogenous L1CAM was observed. Immunohistochemical analysis revealed correct expression of the transgene in mouse cortex and collective duct of the kidney. Injection of (125)I-labeled L1CAM antibodies resulted in specific enrichment in the kidney but not in the brain. The injection of the therapeutic anti-human L1CAM mAb L1-9.3/2a into transgenic mice even at high doses did not cause behavioral changes or other side effects. Similar results were obtained using a mouse specific L1CAM mAb in normal mice. Tumor therapy experiments were performed using syngeneic mouse tumor cells (RET melanoma and Panc02 pancreatic adenocarcinoma) transduced with human L1CAM. MAb L1-9.3/2a efficiently and specifically attenuated local tumor growth in both model systems without apparent side effects. The therapeutic effect was dependent on immune effector mechanisms. Analysis of Panc02-huL1CAM tumors after therapy showed elevated levels of EGF and evidence of immune-induced epithelial-mesenchymal transition. The results suggest that our transgenic mice are valuable tools to study L1CAM-based antibody therapy.

A transcriptome-proteome integrated network identifies endoplasmic reticulum thiol oxidoreductase (ERp57) as a hub that mediates bone metastasis.

Bone metastasis is the most common distant relapse in breast cancer. The identification of key proteins involved in the osteotropic phenotype would represent a major step toward the development of new prognostic markers and therapeutic improvements. The aim of this study was to characterize functional phenotypes that favor bone metastasis in human breast cancer. We used the human breast cancer cell line MDA-MB-231 and its osteotropic BO2 subclone to identify crucial proteins in bone metastatic growth. We identified 31 proteins, 15 underexpressed and 16 overexpressed, in BO2 cells compared with parental cells. We employed a network-modeling approach in which these 31 candidate proteins were prioritized with respect to their potential in metastasis formation, based on the topology of the protein-protein interaction network and differential expression. The protein-protein interaction network provided a framework to study the functional relationships between biological molecules by attributing functions to genes whose functions had not been characterized. The combination of expression profiles and protein interactions revealed an endoplasmic reticulum-thiol oxidoreductase, ERp57, functioning as a hub that retained four down-regulated nodes involved in antigen presentation associated with the human major histocompatibility complex class I molecules, including HLA-A, HLA-B, HLA-E, and HLA-F. Further analysis of the interaction network revealed an inverse correlation between ERp57 and vimentin, which influences cytoskeleton reorganization. Moreover, knockdown of ERp57 in BO2 cells confirmed its bone organ-specific prometastatic role. Altogether, ERp57 appears as a multifunctional chaperone that can regulate diverse biological processes to maintain the homeostasis of breast cancer cells and promote the development of bone metastasis.

Insulin resistance is a cardiovascular risk factor in hypertensive adults without type 2 diabetes mellitus.

BACKGROUND: Metabolic syndrome refers to the association among several cardiovascular risk factors: obesity, dyslipidemia, hyperglycemia, and hypertension. It is associated with increased cardiovascular risk and the development of type 2 diabetes mellitus. Insulin resistance is the underlying mechanism of metabolic syndrome, although its role in increased cardiovascular risk has not been directly identified. OBJECTIVE: We investigated the association between insulin resistance and increased cardiovascular risk in hypertensive adults without diabetes mellitus. DESIGN AND PARTICIPANTS: We enrolled participants without diabetes from an outpatient setting in a retrospective, longitudinal study. Several demographic, clinical, and laboratory parameters were recorded during the observation period. Plasma insulin and homeostatic model assessment for insulin resistance (HOMA-IR) were used to determine insulin resistance and four cardiovascular events (acute coronary disease, acute cerebrovascular disease, incident heart failure, and cardiovascular mortality) were combined into a single outcome. Logistic regression and Cox proportional hazards models were fitted to evaluate the association between covariates and outcomes. RESULTS: We included 1899 hypertensive adults without diabetes with an average age of 53 years (51.3% women, 23% had prediabetes, and 64.2% had metabolic syndrome). In a logistic regression analysis, male sex (odds ratio, OR = 1.66) having high levels of low-density lipoprotein (LDL, OR = 1.01), kidney function (OR = 0.97), and HOMA-IR (OR = 1.06) were associated with the incidence of cardiovascular events; however, in a survival multivariate analysis, only HOMA-IR (hazard ratio, HR 1.4, 95% confidence interval, CI: 1.05-1.87, p = 0.02) and body mass index (HR 1.05, 95% CI: 1.02-1.08, p = 0.002) were considered independent prognostic variables for the development of incident cardiovascular events. CONCLUSION: Insulin resistance and obesity are useful for assessing cardiovascular risk in hypertensive people without diabetes but with preserved kidney function. This work demonstrates the predictive value of the measurement of insulin, and therefore of insulin resistance, in an outpatient setting and attending to high-risk patients.

Outcomes and Patterns of Evolution of Patients with Hematological Malignancies during the COVID-19 Pandemic: A Nationwide Study (2020-2022).

Background: Early reports suggest that hematological malignancy (HM) is a relevant risk factor for morbidity and mortality in COVID-19. We investigated the characteristics, outcomes, and risk factors for mortality in patients hospitalized with HM and COVID-19. Methods: We conducted a retrospective, nationwide study using data from hospitalized patients that were provided by the Spanish Ministry of Health including all patients admitted to a Spanish hospital from 2020 to 2022 with a COVID-19 diagnosis. A descriptive analysis and correlational analyses were conducted. Logistic regression was used to assess mortality in these patients and to calculate odds ratios (ORs). Results: We collected data on 1.2 million patients with COVID-19, including 34,962 patients with HMs. The incidence of hospitalization for patients with HMs was 5.8%, and the overall mortality rate was higher than for patients without HMs (19.8% versus 12.7%, p = 0.001). Mortality rates were higher for patients with lymphomas, multiple myelomas, and leukemias than for those with myeloproliferative disorders. Having HMs was a risk factor for mortality, with OR = 1.7 (95% CI 1.66-1.75, p = 0.001). By subtype, non-Hodgkin lymphomas were the highest risk factor for mortality (OR = 1.7), followed by leukemias (OR = 1.6), Hodgkin lymphomas (OR = 1.58), and plasma cell dyscrasias (OR = 1.24). Conclusions: This study identifies the different clinical profiles of patients with HMs who are at a high risk for mortality when hospitalized with COVID-19. As members of a vulnerable population, these patients deserve special prophylactic and therapeutic measures to minimize the effects of SARS-CoV-2 infection.

Discovery of BAY-405: An Azaindole-Based MAP4K1 Inhibitor for the Enhancement of T-Cell Immunity against Cancer.

Mitogen-activated protein kinase kinase kinase kinase 1 (MAP4K1) is a serine/threonine kinase that acts as an immune checkpoint downstream of T-cell receptor stimulation. MAP4K1 activity is enhanced by prostaglandin E2 (PGE2) and transforming growth factor beta (TGFß), immune modulators commonly present in the tumor microenvironment. Therefore, its pharmacological inhibition is an attractive immuno-oncology concept for inducing therapeutic T-cell responses in cancer patients. Here, we describe the systematic optimization of azaindole-based lead compound 1, resulting in the discovery of potent and selective MAP4K1 inhibitor 38 (BAY-405) that displays nanomolar potency in biochemical and cellular assays as well as in vivo exposure after oral dosing. BAY-405 enhances T-cell immunity and overcomes the suppressive effect of PGE2 and TGFß. Treatment of tumor-bearing mice shows T-cell-dependent antitumor efficacy. MAP4K1 inhibition in conjunction with PD-L1 blockade results in a superior antitumor impact, illustrating the complementarity of the single agent treatments.

Impact and Effectiveness of COVID-19 Vaccines Based on Machine Learning Analysis of a Time Series: A Population-Based Study.

Background: Although confirmed cases of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been declining since late 2020 due to general vaccination, little research has been performed regarding the impact of vaccines against SARS-CoV-2 in Spain in terms of hospitalizations and deaths. Objective: Our aim was to identify the reduction in severity and mortality of coronavirus disease 2019 (COVID-19) at a nationwide level due to vaccination. Methods: We designed a retrospective, population-based study to define waves of infection and to describe the characteristics of the hospitalized population. We also studied the rollout of vaccination and its relationship with the decline in hospitalizations and deaths. Finally, we developed two mathematical models to estimate non-vaccination scenarios using machine learning modeling (with the ElasticNet and RandomForest algorithms). The vaccination and non-vaccination scenarios were eventually compared to estimate the number of averted hospitalizations and deaths. Results: In total, 498,789 patients were included, with a global mortality of 14.3%. We identified six waves or epidemic outbreaks during the observed period. We established a strong relationship between the beginning of vaccination and the decline in both hospitalizations and deaths due to COVID-19 in all age groups. We also estimated that vaccination prevented 170,959 hospitalizations (CI 95% 77,844-264,075) and 24,546 deaths (CI 95% 2548-46,543) in Spain between March 2021 and December 2021. We estimated a global reduction of 9.19% in total deaths during the first year of COVID-19 vaccination. Conclusions: Demographic and clinical profiles changed over the first months of the pandemic. In Spain, patients over 80 years old and other age groups obtained clinical benefit from early vaccination. The severity of COVID-19, in terms of hospitalizations and deaths, decreased due to vaccination. Our use of machine learning models provided a detailed estimation of the averted burden of the pandemic, demonstrating the effectiveness of vaccination at a population-wide level.

Pericyte phenotype switching alleviates immunosuppression and sensitizes vascularized tumors to immunotherapy in preclinical models.

T cell-based immunotherapies are a promising therapeutic approach for multiple malignancies, but their efficacy is limited by tumor hypoxia arising from dysfunctional blood vessels. Here, we report that cell-intrinsic properties of a single vascular component, namely the pericyte, contribute to the control of tumor oxygenation, macrophage polarization, vessel inflammation, and T cell infiltration. Switching pericyte phenotype from a synthetic to a differentiated state reverses immune suppression and sensitizes tumors to adoptive T cell therapy, leading to regression of melanoma in mice. In melanoma patients, improved survival is correlated with enhanced pericyte maturity. Importantly, pericyte plasticity is regulated by signaling pathways converging on Rho kinase activity, with pericyte maturity being inducible by selective low-dose therapeutics that suppress pericyte MEK, AKT, or notch signaling. We also show that low-dose targeted anticancer therapy can durably change the tumor microenvironment without inducing adaptive resistance, creating a highly translatable pathway for redosing anticancer targeted therapies in combination with immunotherapy to improve outcome.

Near infrared spectroscopy (NIRS) and machine learning as a promising tandem for fast viral detection in serum microsamples: A preclinical proof of concept.

Fast detection of viral infections is a key factor in the strategy for the prevention of epidemics expansion and follow-up. Hepatitis C is paradigmatic within viral infectious diseases and major challenges to elimination still remain. Near infrared spectroscopy (NIRS) is an inexpensive, clean, safe method for quickly detecting viral infection in transmission vectors, aiding epidemic prevention. Our objective is to evaluate the combined potential of machine learning and NIRS global molecular fingerprint (GMF) from biobank sera as an efficient method for HCV activity discrimination in serum. GMF of 151 serum biobank microsamples from hepatitis C patients were obtained with a FT-NIR spectrophotometer in reflectance mode. Multiple scatter correction, smoothing and Saviztsky-Golay second derivative were applied. Spectral analysis included Principal Component Analysis (PCA), Bootstrap and L1-penalized classification. Microsamples of 70 µl were sufficient for GMF acquisition. Bootstrap evidenced significant difference between HCV PCR positive and negative sera. PCA renders a neat discrimination between HCV PCR-positive and negative samples. PCA loadings together with L1-penalized classification allow the identification of discriminative bands. Active virus positive sera are associated to free molecular water, whereas water in solvation shells is associated to HCV negative samples. Divergences in the water matrix structure and the lipidome between HCV negative and positive sera, as well as the relevance of prooxidants and glucose metabolism are reported as potential biomarkers of viral activity. Our proof of concept demonstrates that NIRS GMF of hepatitis C patients' sera aided by machine learning allows for efficient discrimination of viral presence and simultaneous potential biomarker identification.

Insulin Resistance and Metabolic Syndrome as Risk Factors for Hospitalization in Patients with COVID-19: Pilot Study on the Use of Machine Learning.

Aim: Conditions linked to metabolic syndrome, such as obesity, hypertension, insulin resistance, and dyslipidemia, are common in patients with severe coronavirus disease 2019 (COVID-19). These conditions can act synergistically to contribute to negative outcomes. We describe and analyze the relationship between metabolic syndrome and COVID-19 severity in terms of risk of hospitalization. Methods: We designed a retrospective, cross-sectional study, including patients with confirmed COVID-19 diagnosis. Clinical and laboratory parameters regarding metabolic syndrome were collected. The Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) was used to assess insulin resistance. The outcome was needed for hospitalization. Logistic regression was used to calculate odds ratios, and to determine the association between variables and risk of hospitalization. Advanced approaches using machine learning were also used to identify and interpret the effects of predictors on the proposed outcome. Results: We included 2716 COVID-19 patients with a mean age of 61.8 years. Of these, 48.9% were women, 28.9% had diabetes, and 50.6% were diagnosed with metabolic syndrome. Overall, 212 patients required hospitalization. Patients with metabolic syndrome had a 58% greater chance of hospitalization if they were men, 32% if they had metabolic syndrome, and 23% if they were obese. Machine learning methods identified body mass index, metabolic syndrome, systolic blood pressure, and HOMA-IR as the most relevant features for our predictive model. Conclusion: Metabolic syndrome and its related biomarkers increase the odds for a severe clinical course of COVID-19 and the need for hospitalization. Machine learning methods can aid understanding of the effects of single features when assessing risks for a given outcome.

Hospitalization Burden Related to Herpes Zoster Infection in Spain (2016-2019).

INTRODUCTION: Herpes zoster (HZ) and its complications still represent a significant burden for patients and health care systems. In Spain, vaccination is progressively being introduced and recommended for patients between 65 and 80 years old and patients > 18 years of age suffering from certain immunosuppression conditions. The aim of this study is to estimate the number of hospital admissions related to HZ from 2016 to 2019 in Spain. METHODS: Data were collected from the Minimum Basic DataSet (MBDS) and codified according to the Spanish version of the 10th International Classification of Disease (ICD-10-CM codes B02-B02.9). Among others, variables such as sex, age and presence of complications were included. RESULTS: A total of 27,642 hospitalizations were identified (90% in patients > 50 and 45.8% in patients > 80). Women represented 51.2% of the patients, and 59.9% of patients presented complications related to HZ. The hospitalization rate was 17.74, the mortality rate was 1.2, and the case fatality rate was 6.75%. All rates were significantly higher with age, among men and in complicated HZ. Immunosuppression status for which vaccination had been recommended represented 22.7% of the total cases, affecting mostly individuals > 65 and causing more deaths in those > 80 years. The estimated annual cost of hospitalization for herpes zoster was €35,738,285, and the mean cost per patient was €5172. CONCLUSION: The hospitalization burden for HZ is still important in Spain. Data on the current epidemiology are important to evaluate future vaccination strategies.

Secretogranin II influences the assembly and function of MHC class I in melanoma.

Melanoma is the deadliest form of skin cancer showing rising incidence over the past years. New insights into the mechanisms of melanoma progression contributed to the development of novel treatment options, such as immunotherapies. However, acquiring resistance to treatment poses a big problem to therapy success. Therefore, understanding the mechanisms underlying resistance could improve therapy efficacy. Correlating expression levels in tissue samples of primary melanoma and metastases revealed that secretogranin 2 (SCG2) is highly expressed in advanced melanoma patients with poor overall survival (OS) rates. By conducting transcriptional analysis between SCG2-overexpressing (OE) and control melanoma cells, we detected a downregulation of components of the antigen presenting machinery (APM), which is important for the assembly of the MHC class I complex. Flow cytometry analysis revealed a downregulation of surface MHC class I expression on melanoma cells that showed resistance towards the cytotoxic activity of melanoma-specific T cells. IFNγ treatment partially reversed these effects. Based on our findings, we suggest that SCG2 might stimulate mechanisms of immune evasion and therefore be associated with resistance to checkpoint blockade and adoptive immunotherapy.

Differences in Trends in Admissions and Outcomes among Patients from a Secondary Hospital in Madrid during the COVID-19 Pandemic: A Hospital-Based Epidemiological Analysis (2020-2022).

Spain had some of Europe's highest incidence and mortality rates for coronavirus disease 2019 (COVID-19). This study highlights the impact of the COVID-19 pandemic on daily health care in terms of incidence, critical patients, and mortality. We describe the characteristics and clinical outcomes of patients, comparing variables over the different waves. We performed a descriptive, retrospective study using the historical records of patients hospitalized with COVID-19. We describe demographic characteristics, admissions, and occupancy. Time series allowed us to visualize and analyze trends and patterns, and identify several waves during the 27-month period. A total of 3315 patients had been hospitalized with confirmed COVID-19. One-third of these patients were hospitalized during the first weeks of the pandemic. We observed that 4.6% of all hospitalizations had been admitted to the intensive care unit, and we identified a mortality rate of 9.4% among hospitalized patients. Arithmetic- and semi-logarithmic-scale charts showed how admissions and deaths rose sharply during the first weeks, increasing by 10 every few days. We described a single hospital's response and experiences during the pandemic. This research highlights certain demographic profiles in a population and emphasizes the importance of identifying waves when performing research on COVID-19. Our results can extend the analysis of the impact of COVID-19 and can be applied in other contexts, and can be considered when further analyzing the clinical, epidemiological, or demographic characteristics of populations with COVID-19. Our findings suggest that the pandemic should be analyzed not as a whole but rather in different waves.