Circuital modelling in muscle tissue impedance measurements.

Electrical impedance spectroscopy (EIS) stands as a widely employed characterization technique for studying muscular tissue in both physio/pathological conditions. This methodology commonly involves modeling tissues through equivalent electrical circuits, facilitating a correlation between electrical parameters and physiological properties. Within existing literature, diverse equivalent electrical circuits have been proposed, varying in complexity and fitting properties. However, to date, none have definitively proven to be the most suiTable for tissue impedance measurements. This study aims to outline a systematic methodology for EIS measurements and to compare the performances of three widely used electrical circuits in characterizing both physiological and pathological muscle tissue conditions. Results highlight that, for optimal fitting with electrical parameters relevant to tissue characterization, the choice of the circuit to be fitted closely hinges on the specific measurement objectives, including measurement parameters and associated physiological features. Naturally, this necessitates a balance between simplicity and fitting accuracy.

Direct Current Stimulation of Prefrontal Cortex Is Not Effective in Progressive Supranuclear Palsy: A Randomized Trial.

BACKGROUND: Progressive supranuclear palsy (PSP) is a rare 4R-tauopathy. Transcranial direct current stimulation (tDCS) may improve specific symptoms. OBJECTIVES: This randomized, double-blinded, sham-controlled trial aimed at verifying the short-, mid-, and long-term effect of multiple sessions of anodal tDCS over the left dorsolateral prefrontal cortex (DLPFC) cortex in PSP. METHODS: Twenty-five patients were randomly assigned to active or sham stimulation (2 mA for 20 minute) for 5 days/week for 2 weeks. Participants underwent assessments at baseline, after the 2-week stimulation protocol, then after 45 days and 3 months from baseline. Primary outcomes were verbal and semantic fluency. The efficacy was verified with analysis of covariance. RESULTS: We failed to detect a significant effect of active stimulation on primary outcomes. Stimulation was associated to worsening of specific behavioral complaints. CONCLUSIONS: A 2-week protocol of anodal left DLPFC tDCS is not effective in PSP. Specific challenges in running symptomatic clinical trials with classic design are highlighted. © 2024 International Parkinson and Movement Disorder Society.

Persistent complement dysregulation with signs of thromboinflammation in active Long Covid.

Long Covid is a debilitating condition of unknown etiology. We performed multimodal proteomics analyses of blood serum from COVID-19 patients followed up to 12 months after confirmed severe acute respiratory syndrome coronavirus 2 infection. Analysis of >6500 proteins in 268 longitudinal samples revealed dysregulated activation of the complement system, an innate immune protection and homeostasis mechanism, in individuals experiencing Long Covid. Thus, active Long Covid was characterized by terminal complement system dysregulation and ongoing activation of the alternative and classical complement pathways, the latter associated with increased antibody titers against several herpesviruses possibly stimulating this pathway. Moreover, markers of hemolysis, tissue injury, platelet activation, and monocyte-platelet aggregates were increased in Long Covid. Machine learning confirmed complement and thromboinflammatory proteins as top biomarkers, warranting diagnostic and therapeutic interrogation of these systems.

SARS-CoV-2 vaccination enhances the effector qualities of spike-specific T cells induced by COVID-19.

T cells are critical for immune protection against severe COVID-19, but it has remained unclear whether repeated exposure to SARS-CoV-2 antigens delivered in the context of vaccination fuels T cell exhaustion or reshapes T cell functionality. Here, we sampled convalescent donors with a history of mild or severe COVID-19 before and after SARS-CoV-2 vaccination to profile the functional spectrum of hybrid T cell immunity. Using combined single-cell technologies and high-dimensional flow cytometry, we found that the frequencies and functional capabilities of spike-specific CD4+ and CD8+ T cells in previously infected individuals were enhanced by vaccination, despite concomitant increases in the expression of inhibitory receptors such as PD-1 and TIM3. In contrast, CD4+ and CD8+ T cells targeting non-spike proteins remained functionally static and waned over time, and only minimal effects were observed in healthy vaccinated donors experiencing breakthrough infections with SARS-CoV-2. Moreover, hybrid immunity was characterized by elevated expression of IFN-γ, which was linked with clonotype specificity in the CD8+ T cell lineage. Collectively, these findings identify a molecular hallmark of hybrid immunity and suggest that vaccination after infection is associated with cumulative immunological benefits over time, potentially conferring enhanced protection against subsequent episodes of COVID-19.

Memory profiles distinguish cross-reactive and virus-specific T cell immunity to mpox.

Mpox represents a persistent health concern with varying disease severity. Reinfections with mpox virus (MPXV) are rare, possibly indicating effective memory responses to MPXV or related poxviruses, notably vaccinia virus (VACV) from smallpox vaccination. We assessed cross-reactive and virus-specific CD4+ and CD8+ T cells in healthy individuals and mpox convalescent donors. Cross-reactive T cells were most frequently observed in healthy donors over 45 years. Notably, long-lived memory CD8+ T cells targeting conserved VACV/MPXV epitopes were identified in older individuals more than four decades after VACV exposure and exhibited stem-like characteristics, defined by T cell factor-1 (TCF-1) expression. In mpox convalescent donors, MPXV-reactive CD4+ and CD8+ T cells were more prevalent than in controls, demonstrating enhanced functionality and skewing toward effector phenotypes, which correlated with milder disease. Collectively, we report robust effector memory MPXV-specific T cell responses in mild mpox and long-lived TCF-1+ VACV/MPXV-specific CD8+ T cells decades after smallpox vaccination.

Human memory B cells show plasticity and adopt multiple fates upon recall response to SARS-CoV-2.

The B cell response to different pathogens uses tailored effector mechanisms and results in functionally specialized memory B (Bm) cell subsets, including CD21+ resting, CD21-CD27+ activated and CD21-CD27- Bm cells. The interrelatedness between these Bm cell subsets remains unknown. Here we showed that single severe acute respiratory syndrome coronavirus 2-specific Bm cell clones showed plasticity upon antigen rechallenge in previously exposed individuals. CD21- Bm cells were the predominant subsets during acute infection and early after severe acute respiratory syndrome coronavirus 2-specific immunization. At months 6 and 12 post-infection, CD21+ resting Bm cells were the major Bm cell subset in the circulation and were also detected in peripheral lymphoid organs, where they carried tissue residency markers. Tracking of individual B cell clones by B cell receptor sequencing revealed that previously fated Bm cell clones could redifferentiate upon antigen rechallenge into other Bm cell subsets, including CD21-CD27- Bm cells, demonstrating that single Bm cell clones can adopt functionally different trajectories.

Hypes, hopes, and the way forward for microalgal biotechnology.

The urge for food security and sustainability has advanced the field of microalgal biotechnology. Microalgae are microorganisms able to grow using (sun)light, fertilizers, sugars, CO2, and seawater. They have high potential as a feedstock for food, feed, energy, and chemicals. Microalgae grow faster and have higher areal productivity than plant crops, without competing for agricultural land and with 100% efficiency uptake of fertilizers. In comparison with bacterial, fungal, and yeast single-cell protein production, based on hydrogen or sugar, microalgae show higher land-use efficiency. New insights are provided regarding the potential of microalgae replacing soy protein, fish oil, and palm oil and being used as cell factories in modern industrial biotechnology to produce designer feed, recombinant proteins, biopharmaceuticals, and vaccines.

Expression of glycerol-3-phosphate acyltransferase increases non-polar lipid accumulation in Nannochloropsis oceanica.

Microalgae are considered a suitable production platform for high-value lipids and oleochemicals. Several species including Nannochloropsis oceanica produce large amounts of essential [Formula: see text]-3 polyunsaturated fatty acids (PUFAs) which are integral components of food and feed and have been associated with health-promoting effects. N. oceanica can further accumulate high contents of non-polar lipids with chemical properties that render them a potential replacement for plant oils such as palm oil. However, biomass and lipid productivities obtained with microalgae need to be improved to reach commercial feasibility. Genetic engineering can improve biomass and lipid productivities, for instance by increasing carbon flux to lipids. Here, we report the overexpression of glycerol-3-phosphate acyltransferase (GPAT) in N. oceanica during favorable growth conditions as a strategy to increase non-polar lipid content. Transformants overproducing either an endogenous (NoGPAT) or a heterologous (Acutodesmus obliquus GPAT) GPAT enzyme targeted to the endoplasmic reticulum had up to 42% and 51% increased non-polar lipid contents, respectively, compared to the wild type. Biomass productivities of transformant strains were not substantially impaired, resulting in lipid productivities that were increased by up to 37% and 42% for NoGPAT and AoGPAT transformants, respectively. When exposed to nutrient stress, transformants and wild type had similar lipid contents, suggesting that GPAT enzyme exerts strong flux control on lipid synthesis in N. oceanica under favorable growth conditions. NoGPAT transformants further accumulated PUFAs in non-polar lipids, reaching a total of 6.8% PUFAs per biomass, an increase of 24% relative to the wild type. Overall, our results indicate that GPAT is an interesting target for engineering of lipid metabolism in microalgae, in order to improve non-polar lipid and PUFAs accumulation in microalgae.

Effect of phosphorus limitation on Se uptake efficiency in the microalga Nannochloropsis oceanica.

Microalgae are considered an efficient accumulator and promising source of Se for feed additive purposes. This study aimed at investigating, for the first time, the effect of phosphorus limitation on Se accumulation and uptake efficiency in N.oceanica. A range of phosphorus concentrations (0-2470 µM) were tested in either the presence or absence of sodium selenite (0, 5, 30 µM). Se accumulation was increased up to 16-fold and Se uptake efficiency was increased up to 3.6-fold under phosphorus growth-limiting concentrations. N.oceanica was then cultivated in a 1.8L flat-panel photobioreactor in batch operation under two phosphorus growth-limiting concentrations (250 and 750 µM) where the accumulation of Se in the microalgal biomass, as well as its presence in the spent medium were analysed. This study is the first to investigate the effect of phosphorus limitation for increasing Se accumulation in microalgae, and to prevent the release of Se in wastewater.

Machine learning can predict mild cognitive impairment in Parkinson's disease.

Background: Clinical markers of cognitive decline in Parkinson's disease (PD) encompass several mental non-motor symptoms such as hallucinations, apathy, anxiety, and depression. Furthermore, freezing of gait (FOG) and specific gait alterations have been associated with cognitive dysfunction in PD. Finally, although low cerebrospinal fluid levels of amyloid-ß42 have been found to predict cognitive decline in PD, hitherto PET imaging of amyloid-ß (Aß) failed to consistently demonstrate the association between Aß plaques deposition and mild cognitive impairment in PD (PD-MCI). Aim: Finding significant features associated with PD-MCI through a machine learning approach. Patients and methods: Patients were assessed with an extensive clinical and neuropsychological examination. Clinical evaluation included the assessment of mental non-motor symptoms and FOG using the specific items of the MDS-UPDRS I and II. Based on the neuropsychological examination, patients were classified as subjects without and with MCI (noPD-MCI, PD-MCI). All patients were evaluated using a motion analysis system. A subgroup of PD patients also underwent amyloid PET imaging. PD-MCI and noPD-MCI subjects were compared with a univariate statistical analysis on demographic data, clinical features, gait analysis variables, and amyloid PET data. Then, machine learning analysis was performed two times: Model 1 was implemented with age, clinical variables (hallucinations/psychosis, depression, anxiety, apathy, sleep problems, FOG), and gait features, while Model 2, including only the subgroup performing PET, was implemented with PET variables combined with the top five features of the former model. Results: Seventy-five PD patients were enrolled (33 PD-MCI and 42 noPD-MCI). PD-MCI vs. noPD-MCI resulted in older and showed worse gait patterns, mainly characterized by increased dynamic instability and reduced step length; when comparing amyloid PET data, the two groups did not differ. Regarding the machine learning analyses, evaluation metrics were satisfactory for Model 1 overcoming 80% for accuracy and specificity, whereas they were disappointing for Model 2. Conclusions: This study demonstrates that machine learning implemented with specific clinical features and gait variables exhibits high accuracy in predicting PD-MCI, whereas amyloid PET imaging is not able to increase prediction. Additionally, our results prompt that a data mining approach on certain gait parameters might represent a reliable surrogate biomarker of PD-MCI.

A machine learning approach to characterize patients with asthma exacerbation attending an acute care setting.

BACKGROUND: One of the main problems in poorly controlled asthma is the access to the Emergency Department (ED). Using a machine learning (ML) approach, the aim of our study was to identify the main predictors of severe asthma exacerbations requiring hospital admission. METHODS: Consecutive patients with asthma exacerbation were screened for inclusion within 48 hours of ED discharge. A k-means clustering algorithm was implemented to evaluate a potential distinction of different phenotypes. K-Nearest Neighbor (KNN) as instance-based algorithm and Random Forest (RF) as tree-based algorithm were implemented in order to classify patients, based on the presence of at least one additional access to the ED in the previous 12 months. RESULTS: To train our model, we included 260 patients (31.5% males, mean age 47.6 years). Unsupervised ML identified two groups, based on eosinophil count. A total of 86 patients with eosinophiles ≥370 cells/µL were significantly older, had a longer disease duration, more restrictions to daily activities, and lower rate of treatment compared to 174 patients with eosinophiles <370 cells/µL. In addition, they reported lower values of predicted FEV1 (64.8±12.3% vs. 83.9±17.3%) and FEV1/FVC (71.3±9.3 vs. 78.5±6.8), with a higher amount of exacerbations/year. In supervised ML, KNN achieved the best performance in identifying frequent exacerbators (AUROC: 96.7%), confirming the importance of spirometry parameters and eosinophil count, along with the number of prior exacerbations and other clinical and demographic variables. CONCLUSIONS: This study confirms the key prognostic value of eosinophiles in asthma, suggesting the usefulness of ML in defining biological pathways that can help plan personalized pharmacological and rehabilitation strategies.

T-cell recovery and evidence of persistent immune activation 12 months after severe COVID-19.

BACKGROUND: T-cell lymphopenia and functional impairment is a hallmark of severe acute coronavirus disease 2019 (COVID-19). How T-cell numbers and function evolve at later timepoints after clinical recovery remains poorly investigated. METHODS: We prospectively enrolled and longitudinally sampled 173 individuals with asymptomatic to critical COVID-19 and analyzed phenotypic and functional characteristics of T cells using flow cytometry, 40-parameter mass cytometry, targeted proteomics, and functional assays. RESULTS: The extensive T-cell lymphopenia observed particularly in patients with severe COVID-19 during acute infection had recovered 6 months after infection, which was accompanied by a normalization of functional T-cell responses to common viral antigens. We detected persisting CD4+ and CD8+ T-cell activation up to 12 months after infection, in patients with mild and severe COVID-19, as measured by increased HLA-DR and CD38 expression on these cells. Persistent T-cell activation after COVID-19 was independent of administration of a COVID-19 vaccine post-infection. Furthermore, we identified a subgroup of patients with severe COVID-19 that presented with persistently low CD8+ T-cell counts at follow-up and exhibited a distinct phenotype during acute infection consisting of a dysfunctional T-cell response and signs of excessive pro-inflammatory cytokine production. CONCLUSION: Our study suggests that T-cell numbers and function recover in most patients after COVID-19. However, we find evidence of persistent T-cell activation up to 12 months after infection and describe a subgroup of severe COVID-19 patients with persistently low CD8+ T-cell counts exhibiting a dysregulated immune response during acute infection.

Autoantibodies in COVID-19 correlate with antiviral humoral responses and distinct immune signatures.

BACKGROUND: Several autoimmune features occur during coronavirus disease 2019 (COVID-19), with possible implications for disease course, immunity, and autoimmune pathology. In this study, we longitudinally screened for clinically relevant systemic autoantibodies to assess their prevalence, temporal trajectory, and association with immunity, comorbidities, and severity of COVID-19. METHODS: We performed highly sensitive indirect immunofluorescence assays to detect antinuclear antibodies (ANA) and antineutrophil cytoplasmic antibodies (ANCA), along with serum proteomics and virome-wide serological profiling in a multicentric cohort of 175 COVID-19 patients followed up to 1 year after infection, eleven vaccinated individuals, and 41 unexposed controls. RESULTS: Compared with healthy controls, similar prevalence and patterns of ANA were present in patients during acute COVID-19 and recovery. However, the paired analysis revealed a subgroup of patients with transient presence of certain ANA patterns during acute COVID-19. Furthermore, patients with severe COVID-19 exhibited a high prevalence of ANCA during acute disease. These autoantibodies were quantitatively associated with higher SARS-CoV-2-specific antibody titers in COVID-19 patients and in vaccinated individuals, thus linking autoantibody production to increased antigen-specific humoral responses. Notably, the qualitative breadth of antibodies cross-reactive with other coronaviruses was comparable in ANA-positive and ANA-negative individuals during acute COVID-19. In autoantibody-positive patients, multiparametric characterization demonstrated an inflammatory signature during acute COVID-19 and alterations of the B-cell compartment after recovery. CONCLUSION: Highly sensitive indirect immunofluorescence assays revealed transient autoantibody production during acute SARS-CoV-2 infection, while the presence of autoantibodies in COVID-19 patients correlated with increased antiviral humoral immune responses and inflammatory immune signatures.

A Machine Learning Approach to Predict the Rehabilitation Outcome in Convalescent COVID-19 Patients.

BACKGROUND: After the acute disease, convalescent coronavirus disease 2019 (COVID-19) patients may experience several persistent manifestations that require multidisciplinary pulmonary rehabilitation (PR). By using a machine learning (ML) approach, we aimed to evaluate the clinical characteristics predicting the effectiveness of PR, expressed by an improved performance at the 6-min walking test (6MWT). METHODS: Convalescent COVID-19 patients referring to a Pulmonary Rehabilitation Unit were consecutively screened. The 6MWT performance was partitioned into three classes, corresponding to different degrees of improvement (low, medium, and high) following PR. A multiclass supervised classification learning was performed with random forest (RF), adaptive boosting (ADA-B), and gradient boosting (GB), as well as tree-based and k-nearest neighbors (KNN) as instance-based algorithms. RESULTS: To train and validate our model, we included 189 convalescent COVID-19 patients (74.1% males, mean age 59.7 years). RF obtained the best results in terms of accuracy (83.7%), sensitivity (84.0%), and area under the ROC curve (94.5%), while ADA-B reached the highest specificity (92.7%). CONCLUSIONS: Our model enables a good performance in predicting the rehabilitation outcome in convalescent COVID-19 patients.

Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome.

Following acute infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) a significant proportion of individuals develop prolonged symptoms, a serious condition termed post-acute coronavirus disease 2019 (COVID-19) syndrome (PACS) or long COVID. Predictors of PACS are needed. In a prospective multicentric cohort study of 215 individuals, we study COVID-19 patients during primary infection and up to one year later, compared to healthy subjects. We discover an immunoglobulin (Ig) signature, based on total IgM and IgG3 levels, which - combined with age, history of asthma bronchiale, and five symptoms during primary infection - is able to predict the risk of PACS independently of timepoint of blood sampling. We validate the score in an independent cohort of 395 individuals with COVID-19. Our results highlight the benefit of measuring Igs for the early identification of patients at high risk for PACS, which facilitates the study of targeted treatment and pathomechanisms of PACS.

Marrow aspiration in aged mice: intramedullary osteogenesis, reduced mechano-adaptation, increased marrow fat.

Introduction: With age, the number of adipocytes and osteoclasts increases, the number of osteoblasts decreases, and mechano-adaptation is impaired.Objectives: Using marrow aspiration, which has a known osteogenic effect in young mice, we sought to recruit osteoblast progenitors to mediate the mechano-adaptive response to in vivo tibial loading.Methods: First, we assessed bone formation and marrow adiposity in the tibiae of old mice (>20 months) sacrificed 1, 2, and 4 weeks after unilateral marrow aspiration. Then, we examined the effects of marrow aspiration on mechano-adaptation in aged mice using tibial loading.Results: Two weeks after aspiration, aspirated tibiae had more bone than contralateral tibiae due to the formation of bone in the medullary canal. Two weeks and four weeks after marrow aspiration, the volume of marrow adipose tissue was higher in the aspirated tibiae, compared to contralateral tibiae. Histomorphometry indicated that aspiration increased non-periosteal (endosteal, intracortical, intramedullary) bone formation, compared to the contralateral tibia.  Mice with marrow aspiration had reduced periosteal bone formation in the contralateral tibia, compared to mice that had loading alone. Loading-induced periosteal bone formation was higher in mice that had loading alone, compared to mice that had aspiration + loading, indicating that aspiration further reduced the mechano-adaptive response.Conclusion: These data demonstrate that, in old mice, bone forms in the medullary canal following aspiration. Adiposity is increased following marrow aspiration, and periosteal mechano-adaptation is reduced.

The nucleolus as a genomic safe harbor for strong gene expression in Nannochloropsis oceanica.

Microalgae are used in food and feed, and they are considered a potential feedstock for sustainably produced chemicals and biofuel. However, production of microalgal-derived chemicals is not yet economically feasible. Genetic engineering could bridge the gap to industrial application and facilitate the production of novel products from microalgae. Here, we report the discovery of a novel gene expression system in the oleaginous microalga Nannochloropsis that exploits the highly efficient transcriptional activity of RNA polymerase I and an internal ribosome entry site for translation. We identified the nucleolus as a genomic safe harbor for Pol I transcription and used it to construct transformant strains with consistently strong transgene expression. The new expression system provides an outstanding tool for genetic and metabolic engineering of microalgae and thus will probably make substantial contributions to microalgal research.

Prospects for viruses infecting eukaryotic microalgae in biotechnology.

Besides being considered pathogens, viruses are important drivers of evolution and they can shape large ecological and biogeochemical processes, by influencing host fitness, population dynamics, and community structures. Moreover, they are simple systems that can be used and manipulated to be beneficial and useful for biotechnological applications. In this context, microalgae biotechnology is a growing field of research, which investigated the usage of photosynthetic microorganisms for the sustainable production of food, fuel, chemical, and pharmaceutical sectors. Viruses infecting microalgae have become important subject of ecological studies related to marine and aquatic environments only four decades ago when virus-like-particles associated with bloom-forming algae were discovered. These first findings have opened new questions on evolution and identity. To date, 63 viruses that infect eukaryotic microalgae have been isolated and cultured. In this short review we briefly summarize what is known about viruses infecting eukaryotic microalgae, and how acknowledging their importance can shape future research focussed not only on marine ecology and evolutionary biology but also on biotechnological applications related to microalgae cell factories.

Signature of long-lived memory CD8+ T cells in acute SARS-CoV-2 infection.

Immunological memory is a hallmark of adaptive immunity and facilitates an accelerated and enhanced immune response upon re-infection with the same pathogen1,2. Since the outbreak of the ongoing COVID-19 pandemic, a key question has focused on which SARS-CoV-2-specific T cells stimulated during acute infection give rise to long-lived memory T cells3. Here, using spectral flow cytometry combined with cellular indexing of transcriptomes and T cell receptor sequencing, we longitudinally characterized individual SARS-CoV-2-specific CD8+ T cells of patients with COVID-19 from acute infection to 1 year into recovery and found a distinct signature identifying long-lived memory CD8+ T cells. SARS-CoV-2-specific memory CD8+ T cells persisting 1 year after acute infection express CD45RA, IL-7 receptor-α and T cell factor 1, but they maintain low expression of CCR7, thus resembling CD45RA+ effector memory T cells. Tracking individual clones of SARS-CoV-2-specific CD8+ T cells, we reveal that an interferon signature marks clones that give rise to long-lived cells, whereas prolonged proliferation and mechanistic target of rapamycin signalling are associated with clonal disappearance from the blood. Collectively, we describe a transcriptional signature that marks long-lived, circulating human memory CD8+ T cells following an acute viral infection.