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Despite the implementation of equality interventions within higher education, progress towards gender parity in science, technology, engineering, and mathematics (STEM) remains slow. Male educators often exhibit poorer engagement with diversity initiatives, potentially contributing to persisting gender disparities in STEM given men's longstanding dominance in these programs. Two experiments investigate how equality interventions should be designed to maximize support from male educators. Experiment 1 (N = 72; M[subscript age] = 39.72, SD[subscript age] = 12.33) used virtual reality to manipulate 2 factors among male academics: (1) exposure to gender inequality and (2) virtually taking the perspective of a female scientist. Using self-report and behavioral measures, viewing an empirical presentation outlining the prevalence of gender issues in STEM yielded the greatest support for equality initiatives following successful perspective-taking. Experiment 2 (N = 120; M[subscript age] = 32.48, SD[subscript age] = 10.36) varied two additional factors among male academics: (1) evidence-based methods to reduce gender biases in STEM (i.e., promoting self-efficacy) and (2) blaming male academics for gender inequalities. Promoting self-efficacy and blaming men for disparities led to greater confidence in male academics' ability to address gender inequalities in their field. Notably, higher self-efficacy accounted for greater support for equality initiatives and internal motives to engage with diversity programs. Findings provide an empirical framework and high-tech training tools for promoting engagement with diversity initiatives among male educators, informing development of interventions within higher education to improve student and faculty experiences in STEM.

Schizophyllum commune is a mushroom-forming fungus notable for its distinctive fruiting bodies with split gills. It is used as a model organism to study mushroom development, lignocellulose degradation and mating type loci. It is a hypervariable species with considerable genetic and phenotypic diversity between the strains. In this study, we systematically phenotyped 16 dikaryotic strains for aspects of mushroom development and 18 monokaryotic strains for lignocellulose degradation. There was considerable heterogeneity among the strains regarding these phenotypes. The majority of the strains developed mushrooms with varying morphologies, although some strains only grew vegetatively under the tested conditions. Growth on various carbon sources showed strain-specific profiles. The genomes of seven monokaryotic strains were sequenced and analyzed together with six previously published genome sequences. Moreover, the related species Schizophyllum fasciatum was sequenced. Although there was considerable genetic variation between the genome assemblies, the genes related to mushroom formation and lignocellulose degradation were well conserved. These sequenced genomes, in combination with the high phenotypic diversity, will provide a solid basis for functional genomics analyses of the strains of S. commune.

Abstract Purpose Convolutional Neural Networks (CNNs) have emerged as transformative tools in the field of radiation oncology, significantly advancing the precision of contouring practices. However, the adaptability of these algorithms across diverse scanners, institutions, and imaging protocols remains a considerable obstacle. This study aims to investigate the effects of incorporating institution-specific datasets into the training regimen of CNNs to assess their generalization ability in real-world clinical environments. Focusing on a data-centric analysis, the influence of varying multi- and single center training approaches on algorithm performance is conducted. Methods nnU-Net is trained using a dataset comprising 161 18F-PSMA-1007 PET images collected from four distinct institutions (Freiburg: n = 96, Munich: n = 19, Cyprus: n = 32, Dresden: n = 14). The dataset is partitioned such that data from each center are systematically excluded from training and used solely for testing to assess the model's generalizability and adaptability to data from unfamiliar sources. Performance is compared through a 5-Fold Cross-Validation, providing a detailed comparison between models trained on datasets from single centers to those trained on aggregated multi-center datasets. Dice Similarity Score, Hausdorff distance and volumetric analysis are used as primary evaluation metrics. Results The mixed training approach yielded a median DSC of 0.76 (IQR: 0.64–0.84) in a five-fold cross-validation, showing no significant differences (p = 0.18) compared to models trained with data exclusion from each center, which performed with a median DSC of 0.74 (IQR: 0.56–0.86). Significant performance improvements regarding multi-center training were observed for the Dresden cohort (multi-center median DSC 0.71, IQR: 0.58–0.80 vs. single-center 0.68, IQR: 0.50–0.80, p 0.05; Freiburg: multi-center 0.78, IQR: 0.53–0.87 vs. single-center 0.71, IQR: 0.53–0.83, p = 0.23). Conclusion CNNs trained for auto contouring intraprostatic GTV in 18F-PSMA-1007 PET on a diverse dataset from multiple centers mostly generalize well to unseen data from other centers. Training on a multicentric dataset can improve performance compared to training exclusively with a single-center dataset regarding intraprostatic 18F-PSMA-1007 PET GTV segmentation. The segmentation performance of the same CNN can vary depending on the dataset employed for training and testing.

Background: Children and adolescents in residential care have more adverse childhood experiences (ACEs) than those living in any other type of care. However, there is scant research on risk factors accounting for this accumulation. Objective: To establish the prevalence of ACEs in child residential care and to explore predictors of accumulation and clustering patterns. Method: A cluster-randomized sample of 1275 children ages 5-18 years was drawn from 78 residential care facilities across Romania. Clinical psychologists from Child Protective Services (CPS) aided in the collection of ACEs, family, health and history data. Network analysis was used to explore ACE clustering patterns, while a Zero-Inflated Negative Binomial regression provided insights into covariate risk factors. Results: Within the network, influential ACEs were found to be consistent with previous research. However, using expanded ACEs on a high exposure sample, two new clusters emerged: (1) ACEs reported by children at the highest mean ages (abused by a romantic partner, arrested or incarcerated, sexually abused), (2) ACEs pertaining to social exclusion (seriously medically ill, discriminated, bullied). Several risk factors significantly predicted ACE accumulation: craniocerebral trauma, prenatal exposure to drugs, cognitive delay, conduct disorder and frequent change of homes. Institutional placement and special education were found as protective factors. Conclusions: A child in residential care that was institutionalized for reasons other than neglect or abuse had 80% probability of not being ever exposed to any ACEs. Nevertheless, CPS can utilize important risk factors and influential past ACEs to detect children at higher risks of further adversity.

The escalating demand for meat driven by the increase of the global population requires substantial advancements in the beef sector. Among the various cattle breeds bred for meat, Black Angus (BA) is renowned for its adaptability and commendable performance in meat production. In contrast with BA, the Romanian Spotted (RS) is a local study breed which constitutes a significant proportion of Romania's livestock and it could become an alternative to BA in a localized market. This study aims to assess the suitability of RS beef to dry-ageing in comparison to BA by investigating the impact of dry-aging (DA) and wet-aging (WA) techniques on two distinct anatomical parts, specifically Longissimus thoracis (LT) and Longissimus lumborum (LL), of each breed. The quality of the aged products was assessed in terms of microbiological characteristics, fatty acid composition and the changes in their values at the end of the ageing process compared with the initially quality of the raw meat. The study showed significant changes in the fatty acid composition profile after ageing, for both types of ageing, and for both breeds. Furthermore, this study highlights the possibility of valorizing local breeds in the production of aged beef.

Abstract Amyloid polymorphism is a hallmark of almost all amyloid species, yet the mechanisms underlying the formation of amyloid polymorphs and their complex architectures remain elusive. Commonly, two main mesoscopic topologies are found in amyloid polymorphs characterized by non‐zero Gaussian and mean curvatures: twisted ribbons and helical fibrils, respectively. Here, a rich heterogeneity of configurations is demonstrated on insulin amyloid fibrils, where protofilament packing can occur, besides the common polymorphs, also in a combined mode forming mixed‐curvature polymorphs. Through AFM statistical analysis, an extended array of heterogeneous architectures that are rationalized by mesoscopic theoretical arguments are identified. Notably, an unusual fibrillization pathway is also unraveled toward mixed‐curvature polymorphs via the widespread recruitment and intertwining of protofilaments and protofibrils. The results present an original view of amyloid polymorphism and advance the fundamental understanding of the fibrillization mechanism from single protofilaments into mature amyloid fibrils.

Abstract Objective Infantile epileptic spasms (IS) are epileptic seizures that are associated with increased risk for developmental impairments, adult epilepsies, and mortality. Here, we investigated coherence‐based network dynamics in scalp EEG of infants with IS to identify frequency‐dependent networks associated with spasms. We hypothesized that there is a network of increased fast ripple connectivity during the electrographic onset of clinical spasms, which is distinct from controls. Methods We retrospectively analyzed peri‐ictal and interictal EEG recordings of 14 IS patients. The data was compared with 9 age‐matched controls. Wavelet phase coherence (WPC) was computed between 0.2 and 400 Hz. Frequency‐ and time‐dependent brain networks were constructed using this coherence as the strength of connection between two EEG channels, based on graph theory principles. Connectivity was evaluated through global efficiency (GE) and channel‐based closeness centrality (CC), over frequency and time. Results GE in the fast ripple band (251–400 Hz) was significantly greater following the onset of spasms in all patients (P central > anterior, Kruskal‐Wallis P

Wood-decaying fungi of the class Agaricomycetes (phylum Basidiomycota) are saprotrophs that break down lignocellulose and play an important role in nutrient recycling. They secrete a wide range of extracellular plant cell wall degrading enzymes that break down cellulose, hemicellulose, and lignin, the main building blocks of plant biomass. Although the production of these enzymes is regulated mainly at the transcriptional level, no activating regulators have been identified in any wood-decaying fungus in the class Agaricomycetes. We studied the regulation of cellulase expression in the wood-decaying fungus Schizophyllum commune. Comparative genomics and transcriptomics on two wild isolates revealed a Zn2Cys6-type transcription factor gene (roc1) that was highly upregulated during growth on cellulose, compared to glucose. It is only conserved in the class Agaricomycetes. A roc1 knockout strain showed an inability to grow on medium with cellulose as sole carbon source, and growth on cellobiose and xylan (other components of wood) was inhibited. Growth on non-wood-related carbon sources was not inhibited. Cellulase gene expression and enzyme activity were reduced in the Δroc1 strain. ChIP-Seq identified 1474 binding sites of the Roc1 transcription factor. Promoters of genes involved in lignocellulose degradation were enriched with these binding sites, especially those of LPMO (lytic polysaccharide monooxygenase) CAZymes, indicating that Roc1 directly regulates these genes. A conserved motif was identified as the binding site of Roc1, which was confirmed by a functional promoter analysis. Together, Roc1 is a key regulator of cellulose degradation and the first identified in wood-decaying fungi in the phylum Basidiomycota. IMPORTANCE Wood-degrading fungi in the phylum Basidiomycota play a crucial role in nutrient recycling by breaking down all components of wood. Fungi have evolved transcriptional networks that regulate expression of wood-degrading enzymes, allowing them to prioritize one nutrient source over another. However, to date all these transcription factors have been identified in the phylum Ascomycota, which is only distantly related to the phylum Basidiomycota. Here, we identified the transcription factor Roc1 as a key regulator of cellulose degradation in the mushroom-forming and wood-degrading fungus Schizophyllum commune. Roc1 is highly conserved in the phylum Basidiomycota. Using comparative genomics, transcriptomics, ChIP-Seq and promoter analysis we have identified direct targets of Roc1, as well as other aspects of the transcriptional response to cellulose.

Sodium–glucose cotransporter 2 inhibitors (SGLT2i), a novel class of glucose-lowering drugs, have revolutionized the management of heart failure with reduced and preserved ejection fraction, regardless of the presence of diabetes, and are currently incorporated in the heart failure guidelines. While these drugs have consistently demonstrated their ability to decrease heart failure hospitalizations in several landmark clinical trials, their cardioprotective effects are far from having been completely elucidated. In the past decade, a growing body of experimental research has sought to address the molecular and cellular mechanisms of SGLT2i in order to provide a better understanding of the off-target acute and chronic cardiac benefits, beyond the on-target renal effect responsible for blood glucose reduction. The present narrative review addresses the direct cardioprotective effects of SGLT2i, delving into the off-target mechanisms of the drugs currently approved for heart failure therapy, and provides insights into future perspectives.

Abstract The prevention and control of bacterial contamination on ready‐to‐eat (RTE) fresh produce is an essential task to ensure food safety. Therefore, the development of novel and effective decontamination technologies to ensure microbiological safety of fruits and vegetables has gained considerable attention and new sanitisation methods are needed. The antimicrobial activity of essential oils (EOs) is well documented, but their application in fresh produce remains a challenge due to their hydrophobic nature. Thus, nanoemulsions efficiently contribute to support the use of EOs in foods by enhancing their dispersibility, their contact area and facilitating the introduction into bacterial cells. The combination of these factors ultimately increases their antimicrobial activity. Quantitative microbial risk assessment (QMRA) is gaining more attention as an effective tool to assess and prevent potential risks associated with food‐borne pathogens. In this context, the current project aims to study the effectiveness of different washing methods based on nanoemulsified EOs, comparing them against traditional methods, using a QMRA model for Escherichia coli O157:H7 on cherry tomatoes. Different simulations within a stochastic risk assessment model were implemented using the biorisk package for R, aiming to describe microbial behaviour and biological risk along the Romanian and Spanish food supply chains of RTE fresh produce. Nanoemulsions were prepared using oregano and rosemary EOs, each from Romania and Spain. The four nanoemulsions were evaluated as decontamination treatments to control the growth of E. coli O157:H7 on artificially contaminated cherry tomatoes. The decontamination treatments showed encouraging results, comparable to commonly used chlorine solutions. Therefore, oregano and rosemary nanoemulsions are promising and could be a feasible alternative for chlorine solutions in the reduction of microbiological contaminants.

Somatic frameshift mutations in exon 9 of calreticulin (CALR) gene are recognized as disease drivers in primary myelofibrosis (PMF), one of the three classical Philadelphia-negative myeloproliferative neoplasms (MPNs). Type 1/type 1-like CALR mutations particularly confer a favorable prognostic and survival advantage in PMF patients. We report an unusual case of PMF incidentally diagnosed in a 68-year-old woman known with hepatitis C virus (HCV) cirrhosis who developed a progressive painful splenomegaly, without anomalies in blood cell counts. While harboring a type 1 CALR mutation, the patient underwent a leukemic transformation in less than 1 year from diagnosis, with a lethal outcome. Analysis of paired DNA samples from chronic and leukemic phases by a targeted next-generation sequencing (NGS) panel and single-nucleotide polymorphism (SNP) microarray revealed that the leukemic clone developed from the CALR-mutated clone through the acquisition of genetic events in the RAS signaling pathway: an increased variant allele frequency of the germline NRAS Y64D mutation present in the chronic phase (via an acquired uniparental disomy of chromosome 1) and gaining NRAS G12D in the blast phase. SNP microarray analysis showed five clinically significant copy number losses at regions 7q22.1, 8q11.1-q11.21, 10p12.1-p11.22, 11p14.1-p11.2, and Xp11.4, revealing a complex karyotype already in the chronic phase. We discuss how additional mutations, detected by NGS, as well as HCV infection and antiviral therapy, might have negatively impacted this type 1 CALR-mutated PMF. We suggest that larger studies are required to determine if more careful monitoring would be needed in MPN patients also carrying HCV and receiving anti-HCV treatment.

Abstract The oxygen evolution reaction (OER) as one half‐cell reaction of electrochemical water splitting has a fundamental impact on water splitting efficiency and thus on the competitiveness of electrochemically generated hydrogen in the energy market. Nickel‐iron layered double hydroxides (NiFe LDH) are among the most promising electrocatalysts for efficient OER under alkaline conditions. Despite intensive research, correlations of the material properties and the resulting kinetically limiting surface processes are poorly investigated. This work focuses on the kinetic behavior of NiFe LDH catalysts containing different anions in the basal spacing in alkaline OER. Steady‐state Tafel plots, impedance measurements as well as reaction order plots were used to elucidate differences in the catalytic performance. All catalysts showed a dual Tafel behavior and fractional reaction orders. For kinetic modelling, the physisorbed hydrogen peroxide mechanism and Temkin adsorption model were adopted to fit experimental data. Our study showed that the intercalated anions affect the kinetics of rate determining steps. The hypophosphite intercalated LDH possessed the highest OER activity and the first step as rate determining. While for both carbonate and borate intercalated NiFe LDH, the second step proved to be rate determining in the low Tafel region, while the first step was found to be rate‐limiting in the high Tafel region.

Abstract Backround Accurate surrogate parameters for radio resistance are warranted for individualized radiotherapy (RT) concepts in prostate cancer (PCa). The purpose of this study was to assess intertumoral heterogeneity in terms of radio resistance using an ex-vivo γH2AX assay after irradiation of prostate biopsy cores and to investigate its correlation with clinical features of respective patients as well as imaging and genomic features of tumor areas. Methods Twenty one patients with histologically-proven PCa and pre-therapeutic multiparametric resonance imaging and prostate-specific membrane antigen positron emission tomography were included in the study. Biopsy cores were collected from 26 PCa foci. Residual γH2AX foci were counted 24 h after ex-vivo irradiation (with 0 and 4 Gy) of biopsy specimen and served as a surrogate for radio resistance. Clinical, genomic (next generation sequencing) and imaging features were collected and their association with the radio resistance was studied. Results In total 18 PCa lesions from 16 patients were included in the final analysis. The median γH2AX foci value per PCa lesion was 3.12. According to this, the patients were divided into two groups (radio sensitive vs. radio resistant) with significant differences in foci number (p

Biological invasions are one of the main drivers of modern human-induced species losses. Research on the distribution of alien species and their pathways of introduction is essential for understanding and tackling the invasion process. A comprehensive overview on invasive alien plant (IAP) species in Romania is lacking. With this paper, we aim to contribute to filling this gap and to provide a visualization of national patterns regarding plant species invasions, geographical origins and pathways of introductions. Based on plant species occurrence records in the published literature and herbaria we compiled a national database of 102 invasive and potentially invasive alien plant species. We georeferenced 42776 IAP species occurrences and performed an analysis of their spatial patterns. The spatial analyses revealed a biased sampling, with clear hotspots of increased sampling efforts around urban areas. We used chord diagrams to visualize the pathway of introduction and geographical origins of the IAP species, which revealed that species in Romania originate mainly in North and Central America, while the dominant pathway of plant introduction was horticulture. Our results provide an important baseline in drafting management and action plans, as invasive alien plant species represent a priority for the European Union through the Biodiversity Strategy for 2030, and a good starting point for various analyses as the database is further developed and regularly updated.

Mechanistic models have been used for centuries to describe complex interconnected processes, including biological ones. As the scope of these models has widened, so have their computational demands. This complexity can limit their suitability when running many simulations or when real-time results are required. Surrogate machine learning (ML) models can be used to approximate the behaviour of complex mechanistic models, and once built, their computational demands are several orders of magnitude lower. This paper provides an overview of the relevant literature, both from an applicability and a theoretical perspective. For the latter, the paper focuses on the design and training of the underlying ML models. Application-wise, we show how ML surrogates have been used to approximate different mechanistic models. We present a perspective on how these approaches can be applied to models representing biological processes with potential industrial applications (e.g., metabolism and whole-cell modelling) and show why surrogate ML models may hold the key to making the simulation of complex biological systems possible using a typical desktop computer.

Tardigrades are unique micro‐organisms with a high tolerance to desiccation. The protection of their cells against desiccation involves tardigrade‐specific proteins, which include the so‐called cytoplasmic abundant heat soluble (CAHS) proteins. As a first step towards the design of peptides capable of mimicking the cytoprotective properties of CAHS proteins, we have synthesized several model peptides with sequences selected from conserved CAHS motifs and investigated to what extent they exhibit the desiccation‐induced structural changes of the full‐length proteins. Using circular dichroism spectroscopy, two‐dimensional infrared spectroscopy, and molecular dynamics simulations, we have found that the CAHS model peptides are mostly disordered, but adopt a more α$$ \alpha $$‐helical structure upon addition of 2,2,2‐trifluoroethanol, which mimics desiccation. This structural behavior is similar to that of full‐length CAHS proteins, which also adopt more ordered conformations upon desiccation. We also have investigated the surface activity of the peptides at the air/water interface, which also mimics partial desiccation. Interestingly, sum‐frequency generation spectroscopy shows that all model peptides are surface active and adopt a helical structure at the air/water interface. Our results suggest that amino acids with high helix‐forming propensities might contribute to the propensity of these peptides to adopt a helical structure when fully or partially dehydrated. Thus, the selected sequences retain part of the CAHS structural behavior upon desiccation, and might be used as a basis for the design of new synthetic peptide‐based cryoprotective materials. [ABSTRACT FROM AUTHOR]

Amyloid polymorphism is a hallmark of almost all amyloid species, yet the mechanisms underlying the formation of amyloid polymorphs and their complex architectures remain elusive. Commonly, two main mesoscopic topologies are found in amyloid polymorphs characterized by non‐zero Gaussian and mean curvatures: twisted ribbons and helical fibrils, respectively. Here, a rich heterogeneity of configurations is demonstrated on insulin amyloid fibrils, where protofilament packing can occur, besides the common polymorphs, also in a combined mode forming mixed‐curvature polymorphs. Through AFM statistical analysis, an extended array of heterogeneous architectures that are rationalized by mesoscopic theoretical arguments are identified. Notably, an unusual fibrillization pathway is also unraveled toward mixed‐curvature polymorphs via the widespread recruitment and intertwining of protofilaments and protofibrils. The results present an original view of amyloid polymorphism and advance the fundamental understanding of the fibrillization mechanism from single protofilaments into mature amyloid fibrils. [ABSTRACT FROM AUTHOR]

Purpose: Convolutional Neural Networks (CNNs) have emerged as transformative tools in the field of radiation oncology, significantly advancing the precision of contouring practices. However, the adaptability of these algorithms across diverse scanners, institutions, and imaging protocols remains a considerable obstacle. This study aims to investigate the effects of incorporating institution-specific datasets into the training regimen of CNNs to assess their generalization ability in real-world clinical environments. Focusing on a data-centric analysis, the influence of varying multi- and single center training approaches on algorithm performance is conducted. Methods: nnU-Net is trained using a dataset comprising 161 18F-PSMA-1007 PET images collected from four distinct institutions (Freiburg: n = 96, Munich: n = 19, Cyprus: n = 32, Dresden: n = 14). The dataset is partitioned such that data from each center are systematically excluded from training and used solely for testing to assess the model's generalizability and adaptability to data from unfamiliar sources. Performance is compared through a 5-Fold Cross-Validation, providing a detailed comparison between models trained on datasets from single centers to those trained on aggregated multi-center datasets. Dice Similarity Score, Hausdorff distance and volumetric analysis are used as primary evaluation metrics. Results: The mixed training approach yielded a median DSC of 0.76 (IQR: 0.64–0.84) in a five-fold cross-validation, showing no significant differences (p = 0.18) compared to models trained with data exclusion from each center, which performed with a median DSC of 0.74 (IQR: 0.56–0.86). Significant performance improvements regarding multi-center training were observed for the Dresden cohort (multi-center median DSC 0.71, IQR: 0.58–0.80 vs. single-center 0.68, IQR: 0.50–0.80, p < 0.001) and Cyprus cohort (multi-center 0.74, IQR: 0.62–0.83 vs. single-center 0.72, IQR: 0.54–0.82, p < 0.01). While Munich and Freiburg also showed performance improvements with multi-center training, results showed no statistical significance (Munich: multi-center DSC 0.74, IQR: 0.60–0.80 vs. single-center 0.72, IQR: 0.59–0.82, p > 0.05; Freiburg: multi-center 0.78, IQR: 0.53–0.87 vs. single-center 0.71, IQR: 0.53–0.83, p = 0.23). Conclusion: CNNs trained for auto contouring intraprostatic GTV in 18F-PSMA-1007 PET on a diverse dataset from multiple centers mostly generalize well to unseen data from other centers. Training on a multicentric dataset can improve performance compared to training exclusively with a single-center dataset regarding intraprostatic 18F-PSMA-1007 PET GTV segmentation. The segmentation performance of the same CNN can vary depending on the dataset employed for training and testing. [ABSTRACT FROM AUTHOR]

IntroductionAccurate detection and segmentation of the intraprostatic gross tumor volume (GTV) is pivotal for radiotherapy (RT) in primary prostate cancer (PCa) since it influences focal therapy target volumes and the patients’ cT stage. The study aimed to compare the performance of multiparametric resonance imaging (mpMRI) with [18F] PSMA-1007 positron emission tomography (PET) for intraprostatic GTV detection as well as delineation and to evaluate their respective influence on RT concepts.Materials and MethodsIn total, 93 patients from two German University Hospitals with [18F] PSMA-1007-PET/CT and MRI (Freiburg) or [18F] PSMA-1007-PET/MRI (Dresden) were retrospectively enrolled. Validated contouring techniques were applied for GTV-PET and -MRI segmentation. Absolute tumor volume and cT status were determined for each imaging method. The PCa distribution from histopathological reports based on biopsy cores and surgery specimen was used as reference in terms of laterality (unilateral vs. bilateral).ResultsIn the Freiburg cohort (n = 84), mpMRI and PET detected in median 2 (range: 1–5) and 3 (range: 1–8) GTVs, respectively (p < 0.01). The median GTV-MRI was significantly smaller than the GTV-PET, measuring 2.05 vs. 3.65 ml (p = 0.0005). PET had a statistically significant higher concordance in laterality with surgery specimen compared to mpMRI (p = 0.04) and biopsy (p < 0.01), respectively. PSMA PET led to more cT2c and cT3b stages, whereas cT3a stage was more pronounced in mpMRI. Based on the cT stage derived from mpMRI and PET information, 21 and 23 as well as 59 and 60 patients, respectively, were intermediate- and high-risk according to the National Comprehensive Cancer Network (NCCN) v1.2022 criteria. In the Dresden cohort (n = 9), similar results were observed.ConclusionIntraprostatic GTV segmentation based on [18F] PSMA-1007 PET results in more and larger GTVs compared to mpMRI. This influences focal RT target volumes and cT stage definition, but not the NCCN risk group.

ABSTRACT Wood-decaying fungi of the class Agaricomycetes (phylum Basidiomycota) are saprotrophs that break down lignocellulose and play an important role in nutrient recycling. They secrete a wide range of extracellular plant cell wall degrading enzymes that break down cellulose, hemicellulose, and lignin, the main building blocks of plant biomass. Although the production of these enzymes is regulated mainly at the transcriptional level, no activating regulators have been identified in any wood-decaying fungus in the class Agaricomycetes. We studied the regulation of cellulase expression in the wood-decaying fungus Schizophyllum commune. Comparative genomics and transcriptomics on two wild isolates revealed a Zn2Cys6-type transcription factor gene (roc1) that was highly upregulated during growth on cellulose, compared to glucose. It is only conserved in the class Agaricomycetes. A roc1 knockout strain showed an inability to grow on medium with cellulose as sole carbon source, and growth on cellobiose and xylan (other components of wood) was inhibited. Growth on non-wood-related carbon sources was not inhibited. Cellulase gene expression and enzyme activity were reduced in the Δroc1 strain. ChIP-Seq identified 1474 binding sites of the Roc1 transcription factor. Promoters of genes involved in lignocellulose degradation were enriched with these binding sites, especially those of LPMO (lytic polysaccharide monooxygenase) CAZymes, indicating that Roc1 directly regulates these genes. A conserved motif was identified as the binding site of Roc1, which was confirmed by a functional promoter analysis. Together, Roc1 is a key regulator of cellulose degradation and the first identified in wood-decaying fungi in the phylum Basidiomycota. IMPORTANCE Wood-degrading fungi in the phylum Basidiomycota play a crucial role in nutrient recycling by breaking down all components of wood. Fungi have evolved transcriptional networks that regulate expression of wood-degrading enzymes, allowing them to prioritize one nutrient source over another. However, to date all these transcription factors have been identified in the phylum Ascomycota, which is only distantly related to the phylum Basidiomycota. Here, we identified the transcription factor Roc1 as a key regulator of cellulose degradation in the mushroom-forming and wood-degrading fungus Schizophyllum commune. Roc1 is highly conserved in the phylum Basidiomycota. Using comparative genomics, transcriptomics, ChIP-Seq and promoter analysis we have identified direct targets of Roc1, as well as other aspects of the transcriptional response to cellulose.

Sodium–glucose cotransporter 2 inhibitors (SGLT2i), a novel class of glucose-lowering drugs, have revolutionized the management of heart failure with reduced and preserved ejection fraction, regardless of the presence of diabetes, and are currently incorporated in the heart failure guidelines. While these drugs have consistently demonstrated their ability to decrease heart failure hospitalizations in several landmark clinical trials, their cardioprotective effects are far from having been completely elucidated. In the past decade, a growing body of experimental research has sought to address the molecular and cellular mechanisms of SGLT2i in order to provide a better understanding of the off-target acute and chronic cardiac benefits, beyond the on-target renal effect responsible for blood glucose reduction. The present narrative review addresses the direct cardioprotective effects of SGLT2i, delving into the off-target mechanisms of the drugs currently approved for heart failure therapy, and provides insights into future perspectives. [ABSTRACT FROM AUTHOR]