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Abstract Aims Acute myocarditis, although a rare disease, can be associated with sudden cardiac death or the need for transplantation in both children and young adults. To date, there is no definitive evidence to support the routine use of immunosuppressive therapy or treatment targeting inflammation in patients with myocarditis. Animal models of cardiovascular (CV), as well as neurological diseases, have demonstrated that cannabidiol has significant anti‐inflammatory properties and may represent a promising therapy in acute myocarditis. This efficacy has been shown in a murine model of autoimmune myocarditis as well as in in vitro and in vivo models of heart failure (HF). Methods and results We present the rationale and design of the ARCHER Trial, an international multicentre, double‐blind, randomized, placebo‐controlled, phase II study examining the safety and efficacy of a pharmaceutically produced cannabidiol formulation, in patients with mild to moderate acute myocarditis. Eligible patients are those with acute myocarditis, randomized within 10 days of the diagnostic cardiac MRI (CMR), which has met defined diagnostic criteria for myocarditis. Oral treatment (cannabidiol or placebo) is titrated from 2.5 mg/kg of body weight up to 10 mg/kg of body weight b.i.d. (or highest tolerated dose) and taken for 12 weeks in addition to standard of care therapy for HF. The primary endpoints are defined as changes in global longitudinal strain (GLS) and extra cellular volume (ECV), measured by CMR at 12 weeks. Assuming 80% power, a 5% alpha risk and 25% missing CMR follow‐up data at Week 12, 100 patients are required to demonstrate the desired treatment effect of 18%. The change in left ventricular ejection fraction (LVEF) from baseline to Week 12 was selected as the secondary endpoint. Additional exploratory endpoints include changes in hs‐troponin, NT‐proBNP, markers of inflammation and endothelial function during the 12‐week treatment period. The trial is ongoing but is now more than 50% recruited. As enrolment in the trial continues, no interim data are available for inclusion in this Design paper. Conclusions The ongoing ARCHER Trial is an international, multicentre, double‐blind, randomized, placebo‐controlled phase II study, designed to determine the effect of a pharmaceutically produced cannabidiol formulation on CMR parameters in patients presenting with acute myocarditis. Enrolment of 100 patients is expected to conclude in Q3 2024. Study results will be available in early 2025.

Goal: Organ perfusion is a vast subject with several techniques, the most common being Hypothermic Machine Perfusion (HMP) and Normothermic Machine Perfusion (NMP). In this paper, a new machine developed at the Biolab of the University of Florence has been tested to validate its capability to perform a well-controlled Oxygenated Rewarming (COR) phase and maintain stability during the NMP phase. Methods: The tests (n = 5) were conducted on fresh porcine livers and evaluated according to liver perfusion standards. The developed machine is based on a previous version, with an integrated control and sensor system and a complete mechanical and electronic redesign. Results: The results demonstrate the excellent usability of the machine and its ability to effectively maintain the organs in good condition. The new system performed well, and the measures made on the livers were satisfactory for good preservation of the organ. Conclusion: This study showed the effectiveness of the developed machine. Future development of the system will include a more sophisticated control system to ensure the correct parameters for perfusion.

Abstract Huntington’s disease (HD) causes selective degeneration of striatal and cortical neurons, resulting in cell mosaicism of coexisting still functional and dysfunctional cells. The impact of non-cell autonomous mechanisms between these cellular states is poorly understood. Here we generated telencephalic organoids with healthy or HD cells, grown separately or as mosaics of the two genotypes. Single-cell RNA sequencing revealed neurodevelopmental abnormalities in the ventral fate acquisition of HD organoids, confirmed by cytoarchitectural and transcriptional defects leading to fewer GABAergic neurons, while dorsal populations showed milder phenotypes mainly in maturation trajectory. Healthy cells in mosaic organoids restored HD cell identity, trajectories, synaptic density, and communication pathways upon cell-cell contact, while showing no significant alterations when grown with HD cells. These findings highlight cell-type-specific alterations in HD and beneficial non-cell autonomous effects of healthy cells, emphasizing the therapeutic potential of modulating cell-cell communication in disease progression and treatment.

The reproductive state of wild, free-swimming tiger sharks (Galeocerdo cuvier), was assessed using underwater ultrasonography at a diving site in Fuvahmulah, a Maldivian atoll within the central Indian Ocean. The presence of embryos were observed in 93% of the adult sharks (26/28) and two distinct embryonic size groups were observed within the subset of scanned adult females. The results suggest that the observed dive site functions as a gestation ground and builds upon previous work that emphasizes the importance of dive sites for the collection of biological data for shark conservation and management.

Background: Pharmacovigilance (PV) is the science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other medicine/vaccine-related problem. Since its inception in the 1960s, PV has undergone continuous evolution, progressing from a basic level mainly focused on the collection and analysis of cases in its earliest years to a complex system regulated by rigorous standards and laws with modern PV. In recent years, PV has faced the challenge of adapting to rapid scientific advancements, the complexity of the pharma industry, and the digital revolution. To better understand the current state and future developments of PV within pharma companies, the PV working group “Ernesto Montagna” of the Italian Society of Pharmaceutical Medicine (SIMeF ETS) conducted a national survey in Italy. Objectives: The main objective of this survey was to explore the current state and future developments of PV within Pharmaceutical Companies in Italy. Design: This study was designed as a national survey targeting members of the Italian Society of Pharmaceutical Medicine (SIMeF ETS). Methods: The survey utilized computer-assisted web interview (CAWI) technology to collect data from SIMeF members across affiliate and corporate companies, aiming to explore expectations for PV. A simplified version of the questionnaire was also sent to members of the Clinical Research and Medical Affairs (RICMA) and Real-World Evidence working groups of SIMeF to gather input from RICMA professionals regarding the role of PV in pharma companies. Results: The survey revealed that PV in pharma companies is undergoing a transformation, with the potential for greater strategic alignment with business objectives and stakeholder focus. However, there is still room for improvement, particularly in terms of perception within other company departments. It is evident that PV’s evolution has only just begun. Conclusion: A critical factor in the evolution of PV is the adoption of a holistic and comprehensive approach to activities and processes. Scientific associations such as SIMeF can play a valuable role in cultivating new skills and capabilities among PV professionals, assisting, and supporting this change.

Complex clinical cases characterized by the coexistence of multiple pathologies and, consequently, multiple signs and symptoms shared by various pathological entities can hide pitfalls that may lead to incorrect diagnostic conclusions due to cognitive biases. Here, we present the case of a man who presents hyperpyrexia, progressive pancytopenia, and splenomegaly in the context of a SARS-CoV-2-related infection; the final diagnosis will be different from the initial diagnostic hypothesis.

Background: In middle-income countries, costs limit widespred use of left ventricular assist device (LVAD) as a strategy for end-stage heart failure. We aim to describe the experience of a LVAD program in a middle-income country in a hub-and-spoke model. Methods: Patients fulfilling strict inclusion and exclusion criteria were referred from different centers in Brazil for LVAD implantation through a philanthropy program financed via a Brazilian Federal Government tax exemption structure. LVAD implantation was performed in a hub-and-spoke model with a single implanting center. Data were collected retrospectively using hospital records and telephone contact with other centers. Patients who received LVAD implants external to the philanthropic program, either at this or other Brazilian centers, were not included. Results: Between January 1, 2013 and December 31, 2020, 20 adult patients underwent long-term continuous flow LVAD implantation with decentralization of postimplant patient care in regional centers. Patients were referred from 11 centers from 7 states in Brazil and underwent LVAD implantation through a philanthropy program. The median age was 52.5 years and 85% were Interagency Registry for Mechanically Assisted Circulatory Support profile 3 patients. Two patients had Chagas cardiomyopathy. The overall survival censored for competing risks at 1 and 2 years were 90% and 84%, respectively. Three patients (15%) underwent heart transplantation in the first 2 years after LVAD implantation. Twelve patients returned to their original centers and were followed remotely. Conclusions: This study presents a successful LVAD implantation program in a hub-and-spoke model in Brazil. Centralization of LVAD implantation with decentralization of postimplant patient care in regional centers is feasible and safe, enabling optimal allocation of resources in middle-income countries.

Segmental arterial mediolysis (SAM) is an uncommon, nonatherosclerotic, noninflammatory, large- to medium-sized arteriopathy first described in 1976, affecting splanchnic branches of the aorta. The clinical presentation ranges from asymptomatic to severe, life-threatening intra-abdominal hemorrhage and shock. A healthy 53-year-old woman with abdominal pain was referred to our facility for further management of superior mesenteric artery dissection diagnosed in the emergency room. A computed tomography scan revealed a dissection in both renal arteries, leading to ischemic degeneration in the left apical pole of the kidney. Additionally, three aneurysmatic dilatations, ectasia of intrahepatic vessels, and irregularities in the wall of the iliac arteries were identified. No other pathological lesions were observed. Based on imaging and the absence of inflammatory and autoimmune markers, a diagnosis of SAM was made. Since an endovascular intervention was excluded and both anticoagulation and antiplatelet therapy were initiated, the patient has undergone close clinical and radiological follow-up.

Abstract Traumatic brain injury leads to a highly orchestrated immune- and glial cell response partially responsible for long-lasting disability and the development of secondary neurodegenerative diseases. A holistic understanding of the mechanisms controlling the responses of specific cell types and their crosstalk is required to develop an efficient strategy for better regeneration. Here, we combine spatial and single-cell transcriptomics to chart the transcriptomic signature of the injured male murine cerebral cortex, and identify specific states of different glial cells contributing to this signature. Interestingly, distinct glial cells share a large fraction of injury-regulated genes, including inflammatory programs downstream of the innate immune-associated pathways Cxcr3 and Tlr1/2. Systemic manipulation of these pathways decreases the reactivity state of glial cells associated with poor regeneration. The functional relevance of the discovered shared signature of glial cells highlights the importance of our resource enabling comprehensive analysis of early events after brain injury.

BackgroundIn kidney transplant (KT) patients, magnesium (Mg2+) deficiency is widespread. It is often encountered early after KT, may persist longer, and is frequently promoted by calcineurin inhibitors (CNIs) and tubular leakage. Studies demonstrated an association between post-KT hypomagnesemia and allograft dysfunction. The concentration of the active form, the ionized Mg2+ (iMg2+), is not measured clinically, and total Mg2+ (tMg2+) and iMg2+ correlations are conflicting. We assess the cross-sectional prevalence of hypomagnesemia in KT patients. The correlation of demographic and anthropometric parameters was also studied.MethodsA prospective, single-center analysis of KT patients was conducted at the University Hospital of Bern, Switzerland (March 2023–August 2023). Blood samples were collected at least twice for the majority of patients. tMg2+ has been quantified from a plasma sample at the Clinical Chemistry Department of the University Hospital of Bern. The PRIME® ES analyzer (Nova Biomedical, USA) provided results for iMg2+. The following co-variables were considered: age, comorbidities, kidney disease, KT history, estimated glomerular filtration rate (eGFR), and treatment (including Mg2+ supplementation and immunosuppression).ResultsA total of 208 measurements in 104 patients were performed [once in 9/104 patients (8.7%), twice in 86/104 (82.7%), and three times in 9/104 (8.7%)]. Compared to that in healthy volunteers (51 measurements in 51 participants), mean iMg2+ was significantly lower in KT patients {KT: 0.46 mmol/L [interquartile range (IQR): 0.40–0.50], volunteers: 0.57 mmol/L (IQR 0.54–0.61), p < 0.01}. Overall, iMg2+ and tMg2+ showed strong category agreement (r2 = 0.93, p < 0.01). In linear regression, low iMg2+ correlated with CNI exposure. For 110/208 measurements (52.9%), a reduced iMg2+ (cutoff: 0.42 mmol/L) was shown. In 58/208 (27.9%), both values were reduced, and 52/208 (25%) had isolated reduced iMg2+. In principal component analysis, patients with isolated low iMg2+ clustered with patients with low iMg2+ and tMg2+.ConclusioniMg2+ and tMg2+ were strongly correlated. A substantial proportion of patients show isolated low iMg2+. Currently, it is unclear if these patients suffer from Mg2+ deficiency.

Background Patients in serious condition due to COVID-19 often require special care in intensive care units (ICUs). This disease has affected over 758 million people and resulted in 6.8 million deaths worldwide. Additionally, the progression of the disease may vary from individual to individual, that is, it is essential to identify the clinical parameters that indicate a good prognosis for the patient. Machine learning (ML) algorithms have been used for analyzing complex medical data and identifying prognostic indicators. However, there is still an urgent need for a model to elucidate the predictors related to patient outcomes. Therefore, this research aimed to verify, through ML, the variables involved in the discharge of patients admitted to the ICU due to COVID-19. Methods In this study, 126 variables were collected with information on demography, hospital length stay and outcome, chronic diseases and tumors, comorbidities and risk factors, complications and adverse events, health care, and vital indicators of patients admitted to an ICU in southern Brazil. These variables were filtered and then selected by a ML algorithm known as decision trees to identify the optimal set of variables for predicting patient discharge using logistic regression. Finally, a confusion matrix was performed to evaluate the model’s performance for the selected variables. Results Of the 532 patients evaluated, 180 were discharged: female (16.92%), with a central venous catheter (23.68%), with a bladder catheter (26.13%), and with an average of 8.46- and 23.65-days using bladder catheter and submitted to mechanical ventilation, respectively. In addition, the chances of discharge increase by 14% for each additional day in the hospital, by 136% for female patients, 716% when there is no bladder catheter, and 737% when no central venous catheter is used. However, the chances of discharge decrease by 3% for each additional year of age and by 9% for each other day of mechanical ventilation. The performance of the training data presented a balanced accuracy of 0.81, sensitivity of 0.74, specificity of 0.88, and the kappa value was 0.64. The test performance had a balanced accuracy of 0.85, sensitivity 0.75, specificity 0.95, and kappa value of 0.73. The McNemar test found that there were no significant differences in the error rates in the training and test data, suggesting good classification. This work showed that female, the absence of a central venous catheter and bladder catheter, shorter mechanical ventilation, and bladder catheter duration were associated with a greater chance of hospital discharge. These results may help develop measures that lead to a good prognosis for the patient.

Immune checkpoint inhibitors (ICIs) have transformed the management of a broad spectrum of cancers as metastatic melanoma, non-small lung cancer or renal cell carcinoma. These humanized monoclonal antibodies target immune-regulatory receptors expressed on T lymphocytes, antigen presenting cells and tumor cells and induce an immunological anti-tumor response. If on the one hand these molecules have led to considerable improvement in survival outcomes, on the other hand these therapies can be associated with immune-related adverse effects (irAEs). While these side effects have become well known, the best diagnostic and therapeutic approaches are still under investigation. The authors discuss pathophysiology, clinical presentation and histological features of ICIs renal toxicity. Furthermore, we focus on kidney transplant (KT) recipients, including the therapeutic adaptation approach to immunosuppression and the risk of rejection.

Facial emotion recognition is a crucial process for many applications and is still unresolved. Historically, emotional recognition has usually been achieved through artificial intelligence techniques such as Convolutional Neural Networks. However, this approach is quite expensive in terms of computational power and complexity. To alleviate this problem, we propose a lightweight CNN for facial emotion recognition, called Custom Lightweight CNN-based Model (CLCM), based on the well-known MobileNetV2 architecture. Performance was evaluated on four public datasets, FER-2013, RAF-DB, AffectNet, and CK+, where seven facial emotions were detected. The CLCM model was compared to the well-known MobileNetV2 and ShuffleNetV2 architectures. The CLCM performance results were close to or better than the more complex models. Specifically, in the FER-2013 dataset, CLCM achieved an accuracy of 63%, while MobileNetV2 was 58%, and ShuffleNetV2 65%. In the RAF-DB dataset, CLCM showed 84%, MobileNetV2 73%, and ShuffleNetV2 80%. Lastly, in the AffectNet dataset, MobileNetV2 and ShuffleNetV2 showed an accuracy of 57%, while CLCM showed 54%. The results obtained from this study establish CLCM as one of the efficient models in FER. Although CLCM is a smaller model in terms of parameters (2.3 Million) compared to MobileNetV2 (3.5 Million) and ShuffleNetV2 (3.9 Million), it showed good results in almost all analyses. The reduced CLCM’s computational power allows its application in enhanced human-computer interaction, affective computing, and personalized user experience, especially for real-world scenarios such as psychological and medical assessment, automotive (real-time driver emotional state), and vulnerable individual care where limited resource systems are commonly employed and real-time and reliable response are strongly recommended.

Cardiomyopathies are major causes of heart failure. Chagas disease (CD) is caused by the parasite Trypanosoma cruzi, and it is endemic in Central and South America. Thirty percent of cases evolve into chronic chagas cardiomyopathy (CCC), which has worse prognosis as compared with other cardiomyopathies. In vivo bioenergetic analysis and ex vivo proteomic analysis of myocardial tissues highlighted worse mitochondrial dysfunction in CCC, and previous studies identified nuclear-encoded mitochondrial gene variants segregating with CCC. Here, we assessed the role of the mitochondrial genome through mtDNA copy number variations and mtDNA haplotyping and sequencing from heart or blood tissues of severe, moderate CCC and asymptomatic/indeterminate Chagas disease as well as healthy controls as an attempt to help decipher mitochondrial-intrinsic genetic involvement in Chagas disease development. We have found that the mtDNA copy number was significantly lower in CCC than in heart tissue from healthy individuals, while blood mtDNA content was similar among asymptomatic Chagas disease, moderate, and severe CCC patients. An MtDNA haplogrouping study has indicated that African haplogroups were over represented in the Chagas subject groups in comparison with healthy Brazilian individuals. The European lineage is associated with protection against cardiomyopathy and the macro haplogroup H is associated with increased risk towards CCC. Using mitochondria DNA sequencing, 84 mtDNA-encoded protein sequence pathogenic variants were associated with CCC. Among them, two variants were associated to left ventricular non-compaction and two to hypertrophic cardiomyopathy. The finding that mitochondrial protein-coding SNPs and mitochondrial haplogroups associate with risk of evolving to CCC is consistent with a key role of mitochondrial DNA in the development of chronic chagas disease cardiomyopathy.

The bioeconomy, grounded in the shift from fossils to bio-based resources, plays an important role in the Net Zero 2050 scenario. However, even if rooted in circular thinking, bioeconomy business models are not free from environmental, social, and economic concerns. This paper deals with the causes of the unsustainability of business models in the biofuels sector, embracing an unconventional approach that focuses on the uncaptured value. The value uncaptured is the negative aspect of value, and it consists of creating too much or not enough value during the product lifecycle. Value uncaptured can threaten the sustainability of circular business models, which is why it constitutes the ‘dark side’ of circular strategies. Starting from a gap in the existing literature and supported by theoretical background, this study aims to suggest a theoretical framework to identify the causes of the negative value in the biofuel sector. The paper uses a qualitative tool, namely a case study analysis. The findings reveal that circular business models can suffer from value uncaptured, which can take the form of value absence, value destroyed, value surplus, and value missed. Identifying these forms of value can transform them into opportunities for value creation. These results enrich the research on the circular economy with a new and unconventional approach. The elaborated theoretical framework can become a qualitative tool to identify what causes companies’ circular business models to underperform.

Background/Objectives: To analyze the spatial distribution of HPV vaccination coverage in relation to sociodemographic variables in a state of Southern Brazil. Methods: This was an ecological, retrospective study with secondary data from the Department of Information Technology of the Unified Health System/Ministry of Health from 2015 to 2022. The cohort method was used to calculate vaccination coverage. Geographically weighted regression was used for the independent variables. Results: There was a 22.04% reduction in vaccination between the first and second doses. Coverage with the first dose of the vaccine reached 95.17% for the female population, 64.67% for the male population, and 79.57% for both sexes. In 50.62% of cities, coverage exceeded 90% for both sexes. In 80.45% of cities, the recommended coverage for females was achieved. The variable municipal performance was positively significant for the increase in vaccination coverage in 45.45% of the regions for girls, 18.18% for boys, and 36.36% for both sexes. The family health strategy variable was significant in 9.09% of the regions for girls and both sexes. The education variable showed an inverse significance for girls in 40.90%, for boys in 18.18%, and for both sexes in 36.36% of the regions. Conclusions: HPV vaccination declined between the first and second doses, with high first-dose coverage among females and moderate coverage among males. Municipal performance notably impacted coverage, particularly for girls. The family health strategy was relevant in specific regions, while educational factors had a variable effect. Addressing these variables may enhance vaccination coverage and minimize the gap between doses.

Background: Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) infection can cause feared consequences, such as affecting microcirculatory activity. The combined use of HRV analysis, genetic algorithms, and machine learning classifiers can be helpful in better understanding the characteristics of microcirculation that are mainly affected by COVID-19 infection. Methods: This study aimed to verify the presence of microcirculation alterations in patients with COVID-19 infection, performing Heart Rate Variability (HRV) parameters analysis extracted from PhotoPlethysmoGraphy (PPG) signals. The dataset included 97 subjects divided into two groups: healthy (50 subjects) and patients affected by mild-severity COVID-19 (47 subjects). A total of 26 parameters were extracted by the HRV analysis and were investigated using genetic algorithms with three different subject selection methods and five different machine learning classifiers. Results: Three parameters: meanRR, alpha1, and sd2/sd1 were considered significant, combining the results obtained by the genetic algorithm. Finally, machine learning classifications were performed by training classifiers with only those three features. The best result was achieved by the binary Decision Tree classifier, achieving accuracy of 82%, specificity (or precision) of 86%, and sensitivity of 79%. Conclusions: The study’s results highlight the ability to use HRV parameters extraction from PPG signals, combined with genetic algorithms and machine learning classifiers, to determine which features are most helpful in discriminating between healthy and mild-severity COVID-19-affected subjects.