Unveiling ophiuroid biodiversity across North Atlantic habitats via an integrative perspective.

The depths of the North Atlantic Ocean host a species-rich fauna providing heterogeneous habitats from thermal vent fields to cold-water coral reefs. With the increasing threat of destruction of deep-sea habitats due to human impacts, such as demersal fishing and the beginning of deep-sea mining, an analysis of the diversity and distribution of species is crucial for conservation efforts. Brittle stars occur in high biomasses, contributing to the biodiversity of the seafloor. Specimens were collected during several scientific expeditions to gain a more detailed insight into the brittle star diversity in the North Atlantic Ocean. An integrative approach to identify the species with DNA barcoding (mtCOI) in combination with morphological studies revealed 24 species. Most species have been previously identified in the North Atlantic, but sequences for 13 species are newly added to public repositories. Additionally, the MALDI-TOF-MS proteomic analysis was successfully applied for 197 specimens with known COI barcodes. Results are congruent with other molecular species delimitations demonstrating the functionality of proteomics for the identification of brittle stars. This dataset significantly expands our understanding of the taxonomic and genetic diversity of brittle stars and contributes to publicly available data. It emphasizes the importance of considering habitat heterogeneity for large scale patterns of biodiversity.

Standardization of Chemically Selective Atomic Force Microscopy for Metal Oxide Surfaces.

The structures of metal oxide surfaces and inherent defects are vital for a variety of applications in materials science and chemistry. While scanning probe microscopy can reveal atomic-scale details, elemental discrimination usually requires indirect assumptions and extensive theoretical modeling. Here, atomic force microscopy with O-terminated copper tips on a variety of sample systems demonstrates not only a clear and universal chemical contrast but also immediate access to the atomic configuration of defects. The chemically selective contrast is explained by purely electrostatic interactions between the negatively charged tip-apex and the strongly varying electrostatic potential of metal and oxygen sites. These results offer a standardized methodology for the direct characterization of even the most complex metal oxide surfaces, providing fundamental insight into atomic-scale processes in these material systems.

Thyroid scintigraphy: establishing a clinically useful normal range for 99mTc pertechnetate uptake.

PURPOSE: This study aims to establish a normal range for the thyroid uptake derived from 99mTc pertechnetate scans. In particular, variations of uptake with TSH stimulation and other factors such as urinary iodine concentration are taken into account and compared with the calculation of a raw uptake value. METHODS: Clinical multicentric (center A, B and C) prospective study on 125 consecutive healthy patients undergoing thyroid scans for thyroid nodules. Normal functional thyroid status was assured by normal TSH, normal thyroid size, no thyroid antibodies and no symptoms of thyroid functional disorders. Calculations of raw Tc-uptake (uptake) and modified uptake values regarding current TSH value (uptakeTSH1), urinary iodine concentration (uptakeTSH1&uic), gland volume, age, smoking status, weight and tissue thickness ventral to the thyroid were performed. RESULTS: There is a positive correlation of thyroid uptake with TSH allowing for the calculation of a normalized uptake value (uptakeTSH1). The normal range for uptakeTSH1 compares favourable to that for raw uptake in that it yields a clear distinction from thyroid functional disorders. The additional normalization for urinary iodine concentration (uptakeTSH1&uic) may even improve the distinctive power whereas further normalizations such as for gland volume, age and others are not warranted by this study. The 95% CI of uptakeTSH1 for sites A, A&B, and A&B&C were 0.21%-2.06%, 0.22%-2.38% and 0.24%-2.40%. CONCLUSION: A normal range for the thyroid uptake can be established with respect to the current TSH stimulation. This normalization (uptakeTSH1) overcomes the drawback of raw uptake by yielding a clinically useful parameter with obviously high distinctive power against functional thyroid disorders.

Baseline Inventory of Benthic Macrofauna in German Marine Protected Areas (2020-2022) before Closure for Bottom-Contact Fishing.

The response of benthic habitats and organisms to bottom-contact fishing intensity is investigated in marine protected areas (MPAs) of the German EEZ in the North and Baltic Seas. We examined the current state of macrofauna biodiversity in 2020-2022. Comparative analysis for macrofauna (in- and epifauna) inhabiting nine Natura 2000 MPAs constitutes a baseline to assess the effects of bottom-contact fishing exclusion in the future. Aspects of spatial and temporal variability are briefly summarized and discussed. We provide a species list for each region, including 481 taxa, of which 79 were found in both regions, 183 only in the North Sea, and 219 only in the Baltic Sea. The Baltic Sea dataset surprisingly included higher numbers of taxa and revealed more Red List species. The share of major taxonomic groups (polychaetes, bivalves and amphipods) in species richness showed peculiar commonalities between the two regions. In the North Sea, multivariate analysis of community structure revealed significantly higher within-similarity and stronger separation between the considered MPAs compared to the Baltic MPAs. Salinity, temperature and sediment fractions of sand were responsible for over 60% of the variation in the North Sea macrofauna occurrence data. Salinity, mud fraction and bottom-contact fishing were the most important drivers in the Baltic Sea and, together with other considered environmental drivers, were responsible for 53% of the variation. This study identifies aspects of macrofauna occurrence that may be used to assess (causes of) future changes.

Molecular and immunological mechanisms of clonal evolution in multiple myeloma.

Multiple myeloma (MM) is a hematologic malignancy characterized by the proliferation of clonal plasma cells in the bone marrow (BM). It is known that early genetic mutations in post-germinal center B/plasma cells are the cause of myelomagenesis. The acquisition of additional chromosomal abnormalities and distinct mutations further promote the outgrowth of malignant plasma cell populations that are resistant to conventional treatments, finally resulting in relapsed and therapy-refractory terminal stages of MM. In addition, myeloma cells are supported by autocrine signaling pathways and the tumor microenvironment (TME), which consists of diverse cell types such as stromal cells, immune cells, and components of the extracellular matrix. The TME provides essential signals and stimuli that induce proliferation and/or prevent apoptosis. In particular, the molecular pathways by which MM cells interact with the TME are crucial for the development of MM. To generate successful therapies and prevent MM recurrence, a thorough understanding of the molecular mechanisms that drive MM progression and therapy resistance is essential. In this review, we summarize key mechanisms that promote myelomagenesis and drive the clonal expansion in the course of MM progression such as autocrine signaling cascades, as well as direct and indirect interactions between the TME and malignant plasma cells. In addition, we highlight drug-resistance mechanisms and emerging therapies that are currently tested in clinical trials to overcome therapy-refractory MM stages.

Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma.

OBJECTIVE: Considering the essential role of KRAS mutation in NSCLC and the limited experience of PET radiomic features in KRAS mutation, a prediction model was built in our current analysis. Our model aims to evaluate the status of KRAS mutants in lung adenocarcinoma by combining PET radiomics and machine learning. METHOD: Patients were retrospectively selected from our database and screened from the NSCLC radiogenomic dataset from TCIA. The dataset was randomly divided into three subgroups. Two open-source software programs, 3D Slicer and Python, were used to segment lung tumours and extract radiomic features from 18F-FDG-PET images. Feature selection was performed by the Mann-Whitney U test, Spearman's rank correlation coefficient, and RFE. Logistic regression was used to build the prediction models. AUCs from ROCs were used to compare the predictive abilities of the models. Calibration plots were obtained to examine the agreements of observed and predictive values in the validation and testing groups. DCA curves were performed to check the clinical impact of the best model. Finally, a nomogram was obtained to present the selected model. RESULTS: One hundred and nineteen patients with lung adenocarcinoma were included in our study. The whole group was divided into three datasets: a training set (n = 96), a validation set (n = 11), and a testing set (n = 12). In total, 1781 radiomic features were extracted from PET images. One hundred sixty-three predictive models were established according to each original feature group and their combinations. After model comparison and selection, one model, including wHLH_fo_IR, wHLH_glrlm_SRHGLE, wHLH_glszm_SAHGLE, and smoking habits, was validated with the highest predictive value. The model obtained AUCs of 0.731 (95% CI: 0.619~0.843), 0.750 (95% CI: 0.248~1.000), and 0.750 (95% CI: 0.448~1.000) in the training set, the validation set and the testing set, respectively. Results from calibration plots in validation and testing groups indicated that there was no departure between observed and predictive values in the two datasets (p = 0.377 and 0.861, respectively). CONCLUSIONS: Our model combining 18F-FDG-PET radiomics and machine learning indicated a good predictive ability of KRAS status in lung adenocarcinoma. It may be a helpful non-invasive method to screen the KRAS mutation status of heterogenous lung adenocarcinoma before selected biopsy sampling.

Adjustment for the Age- and Gender-Related Metabolic Changes Improves the Differential Diagnosis of Parkinsonism.

Age and gender are the important factors for brain metabolic declines in both normal aging and neurodegeneration, and the confounding effects may influence early and differential diagnosis of neurodegenerative diseases based on the [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET). We aimed to explore the potential of the adjustment of age- and gender-related confounding factors on [18F]FDG PET images in differentiation of Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supra-nuclear palsy (PSP). Eight hundred and seventy-seven clinically definitely diagnosed Parkinsonian patients from a benchmark Huashan Parkinsonian PET imaging database were included. An age- and gender-adjusted Z (AGAZ) score was established based on the gender-specific longitudinal metabolic changes on healthy subjects. AGAZ scores and standardized uptake value ratio (SUVR) values were quantified at regional-level and support vector machine-based error-correcting output codes method was applied for classification. Additional references of the classifications based on metabolic pattern scores were included. The feature-based AGAZ score showed the best performance in classification (accuracy for PD, MSA, PSP: 93.1%, 96.3%, 94.8%). In both genders, the AGAZ score consistently achieved the best efficiency, and the improvements compared to the conventional SUVR value for PD, MSA, and PSP mainly laid in specificity (Male: 5.7%; Female: 11.1%), sensitivity (Male: 7.2%; Female: 7.3%), and sensitivity (Male: 7.3%; Female: 17.2%). Female patients benefited more from the adjustment on [18F]FDG PET in MSA and PSP groups (absolute net reclassification index, p < 0.001). Collectively, the adjustment of age- and gender-related confounding factors may improve the differential diagnosis of Parkinsonism. Particularly, the diagnosis of female Parkinsonian population has the best improvement from this correction. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-022-00079-6.

Unraveling an Alternative Mechanism in Polymer Self-Assemblies: An Order-Order Transition with Unusual Molecular Interactions between Hydrophilic and Hydrophobic Polymer Blocks.

Polymer self-assembly leading to cooling-induced hydrogel formation is relatively rare for synthetic polymers and typically relies on H-bonding between repeat units. Here, we describe a non-H-bonding mechanism for a cooling-induced reversible order-order (sphere-to-worm) transition and related thermogelation of solutions of polymer self-assemblies. A multitude of complementary analytical tools allowed us to reveal that a significant fraction of the hydrophobic and hydrophilic repeat units of the underlying block copolymer is in close proximity in the gel state. This unusual interaction between hydrophilic and hydrophobic blocks reduces the mobility of the hydrophilic block significantly by condensing the hydrophilic block onto the hydrophobic micelle core, thereby affecting the micelle packing parameter. This triggers the order-order transition from well-defined spherical micelles to long worm-like micelles, which ultimately results in the inverse thermogelation. Molecular dynamics modeling indicates that this unexpected condensation of the hydrophilic corona onto the hydrophobic core is due to particular interactions between amide groups in the hydrophilic repeat units and phenyl rings in the hydrophobic ones. Consequently, changes in the structure of the hydrophilic blocks affecting the strength of the interaction could be used to control macromolecular self-assembly, thus allowing for the tuning of gel characteristics such as strength, persistence, and gelation kinetics. We believe that this mechanism might be a relevant interaction pattern for other polymeric materials as well as their interaction in and with biological environments. For example, controlling the gel characteristics could be considered important for applications in drug delivery or biofabrication.

MEK1 drives oncogenic signaling and interacts with PARP1 for genomic and metabolic homeostasis in malignant pleural mesothelioma.

Malignant pleural mesothelioma (MPM) is a lethal malignancy etiologically caused by asbestos exposure, for which there are few effective treatment options. Although asbestos carcinogenesis is associated with reactive oxygen species (ROS), the bona fide oncogenic signaling pathways that regulate ROS homeostasis and bypass ROS-evoked apoptosis in MPM are poorly understood. In this study, we demonstrate that the mitogen-activated protein kinase (MAPK) pathway RAS-RAF-MEK-ERK is hyperactive and a molecular driver of MPM, independent of histological subtypes and genetic heterogeneity. Suppression of MAPK signaling by clinically approved MEK inhibitors (MEKi) elicits PARP1 to protect MPM cells from the cytotoxic effects of MAPK pathway blockage. Mechanistically, MEKi induces impairment of homologous recombination (HR) repair proficiency and mitochondrial metabolic activity, which is counterbalanced by pleiotropic PARP1. Consequently, the combination of MEK with PARP inhibitors enhances apoptotic cell death in vitro and in vivo that occurs through coordinated upregulation of cytotoxic ROS in MPM cells, suggesting a mechanism-based, readily translatable strategy to treat this daunting disease. Collectively, our studies uncover a previously unrecognized scenario that hyperactivation of the MAPK pathway is an essential feature of MPM and provide unprecedented evidence that MAPK signaling cooperates with PARP1 to homeostatically maintain ROS levels and escape ROS-mediated apoptosis.

Comprehensive analysis of SARS-CoV-2 receptor proteins in human respiratory tissues identifies alveolar macrophages as potential virus entry site.

AIMS: COVID-19 has had enormous consequences on global health-care and has resulted in millions of fatalities. The exact mechanism and site of SARS-CoV-2 entry into the body remains insufficiently understood. Recently, novel virus receptors were identified, and alveolar macrophages were suggested as a potential viral entry cell type and vector for intra-alveolar virus transmission. Here, we investigated the protein expression of 10 well-known and novel virus entry molecules along potential entry sites in humans using immunohistochemistry. METHODS AND RESULTS: Samples of different anatomical sites from up to 93 patients were incorporated into tissue microarrays. Protein expression of ACE2, TMPRSS2, furin, CD147, C-type lectin receptors (CD169, CD209, CD299), neuropilin-1, ASGR1 and KREMEN1 were analysed. In lung tissues, at least one of the three receptors ACE2, ASGR1 or KREMEN1 was expressed in the majority of cases. Moreover, all the investigated molecules were found to be expressed in alveolar macrophages, and co-localisation with SARS-CoV-2 N-protein was demonstrated using dual immunohistochemistry in lung tissue from a COVID-19 autopsy. While CD169 and CD209 showed consistent protein expression in sinonasal, conjunctival and bronchiolar tissues, neuropilin-1 and ASGR1 were mostly absent, suggesting a minor relevance of these two molecules at these specific sites. CONCLUSION: Our results extend recent discoveries indicating a role for these molecules in virus entry at different anatomical sites. Moreover, they support the notion of alveolar macrophages being a potential entry cell for SARS-CoV-2.