Combined Reversed Phase HPLC, Mass Spectrometry, and NMR Spectroscopy for a Fast Separation and Efficient Identification of Phosphatidylcholines

In respect of the manifold involvement of lipids in biochemical processes, the analysis of intact and underivatised lipids of body fluids as well as cell and tissue extracts is still a challenging task, if detailed molecular information is required. Therefore, the advantage of combined use of high-pressure liquid chromatography (HPLC), mass spectrometry (MS), and nuclear magnetic resonance (NMR) spectroscopy will be shown analyzing three different types of extracts of the ubiquitous membrane component phosphatidylcholine. At first, different reversed phase modifications were tested on phosphatidylcholines (PC) with the same effective carbon number (ECN) for their applicability in lipid analysis. The results were taken to improve the separation of three natural PC extract types and a new reversed phase (RP)-HPLC method was developed. The individual species were characterized by one- and two-dimensional NMR and positive or negative ion mode quadrupole time of flight (q-TOF)-MS as well as MS/MS techniques. Furthermore, ion suppression effects during electrospray ionisation (ESI), difficulties, limits, and advantages of the individual analytical techniques are addressed

sQUlearn \unicode{x2013} A Python Library for Quantum Machine Learning

sQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn. The library's dual-layer architecture serves both QML researchers and practitioners, enabling efficient prototyping, experimentation, and pipelining. sQUlearn provides a comprehensive toolset that includes both quantum kernel methods and quantum neural networks, along with features like customizable data encoding strategies, automated execution handling, and specialized kernel regularization techniques. By focusing on NISQ-compatibility and end-to-end automation, sQUlearn aims to bridge the gap between current quantum computing capabilities and practical machine learning applications.Comment: 10+5 pages, 5+3 figure

First dose in children: physiological insights into pharmacokinetic scaling approaches and their implications in paediatric drug development

Dose selection for “first in children” trials often relies on scaling of the pharmacokinetics from adults to children. Commonly used approaches are physiologically-based pharmacokinetic modeling (PBPK) and allometric scaling (AS) in combination with maturation of clearance for early life. In this investigation, a comparison of the two approaches was performed to provide insight into the physiological meaning of AS maturation functions and their interchangeability. The analysis focused on the AS maturation functions established using paracetamol and morphine paediatric data after intravenous administration. First, the estimated AS maturation functions were compared with the maturation functions of the liver enzymes as used in the PBPK models. Second, absolute clearance predictions using AS in combination with maturation functions were compared to PBPK predictions for hypothetical drugs with different pharmacokinetic properties. The results of this investigation showed that AS maturation functions do not solely represent ontogeny of enzyme activity, but aggregate multiple pharmacokinetic properties, as for example extraction ratio and lipophilicity (log P). Especially in children younger than 1 year, predictions using AS in combination with maturation functions and PBPK were not interchangeable. This highlights the necessity of investigating methodological uncertainty to allow a proper estimation of the “first dose in children” and assessment of its risk and benefits

JulianA: An automatic treatment planning platform for intensity-modulated proton therapy and its application to intra- and extracerebral neoplasms

Creating high quality treatment plans is crucial for a successful radiotherapy treatment. However, it demands substantial effort and special training for dosimetrists. Existing automated treatment planning systems typically require either an explicit prioritization of planning objectives, human-assigned objective weights, large amounts of historic plans to train an artificial intelligence or long planning times. Many of the existing auto-planning tools are difficult to extend to new planning goals. A new spot weight optimisation algorithm, called JulianA, was developed. The algorithm minimises a scalar loss function that is built only based on the prescribed dose to the tumour and organs at risk (OARs), but does not rely on historic plans. The objective weights in the loss function have default values that do not need to be changed for the patients in our dataset. The system is a versatile tool for researchers and clinicians without specialised programming skills. Extending it is as easy as adding an additional term to the loss function. JulianA was validated on a dataset of 19 patients with intra- and extracerebral neoplasms within the cranial region that had been treated at our institute. For each patient, a reference plan which was delivered to the cancer patient, was exported from our treatment database. Then JulianA created the auto plan using the same beam arrangement. The reference and auto plans were given to a blinded independent reviewer who assessed the acceptability of each plan, ranked the plans and assigned the human-/machine-made labels. The auto plans were considered acceptable in 16 out of 19 patients and at least as good as the reference plan for 11 patients. Whether a plan was crafted by a dosimetrist or JulianA was only recognised for 9 cases. The median time for the spot weight optimisation is approx. 2 min (range: 0.5 min - 7 min)

Lsd1 ablation triggers metabolic reprogramming of brown adipose tissue

Previous work indicated that lysine-specific demethylase 1 (Lsd1) can positively regulate the oxidative and thermogenic capacities of white and beige adipocytes. Here we investigate the role of Lsd1 in brown adipose tissue (BAT) and find that BAT- selective Lsd1 ablation induces a shift from oxidative to glycolytic metabolism. This shift is associated with downregulation of BAT-specific and upregulation of white adipose tissue (WAT)-selective gene expression. This results in the accumulation of di- and triacylglycerides and culminates in a profound whitening of BAT in aged Lsd1- deficient mice. Further studies show that Lsd1 maintains BAT properties via a dual role. It activates BAT-selective gene expression in concert with the transcription factor Nrf1 and represses WAT-selective genes through recruitment of the CoREST complex. In conclusion, our data uncover Lsd1 as a key regulator of gene expression and metabolic function in BAT

The European union’s 2010 target: Putting rare species in focus

P. 167-185The European Union has adopted the ambitious target of halting the loss of biodiversity by 2010. Several indicators have been proposed to assess progress towards the 2010 target, two of them addressing directly the issue of species decline. In Europe, the Fauna Europaea database gives an insight into the patterns of distribution of a total dataset of 130,000 terrestrial and freshwater species without taxonomic bias, and provide a unique opportunity to assess the feasibility of the 2010 target. It shows that the vast majority of European species are rare, in the sense that they have a restricted range. Considering this, the paper discusses whether the 2010 target indicators really cover the species most at risk of extinction. The analysis of a list of 62 globally extinct European taxa shows that most contemporary extinctions have affected narrow-range taxa or taxa with strict ecological requirements. Indeed, most European species listed as threatened in the IUCN Red List are narrow-range species. Conversely, there are as many wide-range species as narrow-range endemics in the list of protected species in Europe (Bird and Habitat Directives). The subset of biodiversity captured by the 2010 target indicators should be representative of the whole biodiversity in terms of patterns of distribution and abundance. Indicators should not overlook a core characteristic of biodiversity, i.e. the large number of narrow-range species and their intrinsic vulnerability. With ill-selected indicator species, the extinction of narrowrange endemics would go unnoticedS

Loss of Caveolin-1 Accelerates Neurodegeneration and Aging

The aged brain exhibits a loss in gray matter and a decrease in spines and synaptic densities that may represent a sequela for neurodegenerative diseases such as Alzheimer's. Membrane/lipid rafts (MLR), discrete regions of the plasmalemma enriched in cholesterol, glycosphingolipids, and sphingomyelin, are essential for the development and stabilization of synapses. Caveolin-1 (Cav-1), a cholesterol binding protein organizes synaptic signaling components within MLR. It is unknown whether loss of synapses is dependent on an age-related loss of Cav-1 expression and whether this has implications for neurodegenerative diseases such as Alzheimer's disease.We analyzed brains from young (Yg, 3-6 months), middle age (Md, 12 months), aged (Ag, >18 months), and young Cav-1 KO mice and show that localization of PSD-95, NR2A, NR2B, TrkBR, AMPAR, and Cav-1 to MLR is decreased in aged hippocampi. Young Cav-1 KO mice showed signs of premature neuronal aging and degeneration. Hippocampi synaptosomes from Cav-1 KO mice showed reduced PSD-95, NR2A, NR2B, and Cav-1, an inability to be protected against cerebral ischemia-reperfusion injury compared to young WT mice, increased Aβ, P-Tau, and astrogliosis, decreased cerebrovascular volume compared to young WT mice. As with aged hippocampi, Cav-1 KO brains showed significantly reduced synapses. Neuron-targeted re-expression of Cav-1 in Cav-1 KO neurons in vitro decreased Aβ expression.Therefore, Cav-1 represents a novel control point for healthy neuronal aging and loss of Cav-1 represents a non-mutational model for Alzheimer's disease

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Investigating orphan cytochromes P450 from Mycobacterium tuberculosis : the search for potential drug targets

Tuberculosis (TB) is a disease that the World Health Organisation (WHO) regards as a global pandemic. There is a great need for new drugs to combat this threat. Drug resistant strains of the causative agent, Mycobacterium tuberculosis (Mtb), have increased the urgency of this quest for novel anti-mycobacterial medicines. Publication of the Mtb genome sequence revealed a large number of cytochrome P450 (CYP) enzymes [Cole, S. T. et al. 1998]. These mono-oxygenase enzymes have been studied for many years and are responsible for metabolic functions in every kingdom of life. Research on the Mtb P450s to date has highlighted several of them as having critcal roles within the organism. CYP121 and CYP128 have been implicated as essential through gene knockout studies. It has been demonstrated that CYP125 is not essential for viability. However, it is part of a gene cluster highly important for Mtb infectivity and virulence. Due to the prospective importance of P450s to Mtb, this group of enzymes is under investigation as a source of novel drug targets. CYP142 was discovered as a potential drug target after it was located to a gene cluster involved in cholesterol catabolism during Mtb dormancy. As part of this PhD project, it was demonstrated that CYP142 performs an almost identical role to that reported for CYP125. These enzymes both perform C27 hydroxylation and carboxylation of the cholesterol side chain. However, variations in the level of oxidation have been identified, dependent upon the redox system with which these P450s are associated. A crystal structure of CYP142 showing high similarity in active site architecture to CYP125 supports the physiological role of CYP142 in cholesterol catabolism. Combining this with in vitro data which demonstrates that CYP142 possesses high affinity for a range of azole anti-fungal agents [Ahmad, Z. et al. 2005, 2006] supports the suggestion that it is a candidate target for the next generation of anti-mycobacterial drugs. CYP144 was highlighted as being important during the latent phase of Mtb growth, a phase that is not targeted by any of the current antimycobacterials. Work performed as part of this PhD has shown that many characteristics of CYP144 are highly comparable to those reported for other MtbP450s. CYP144 shows high affinity and specificity towards many azole molecules. Econazole, clotrimazole and miconazole have repeatedly been shown to bind to MtbP450s, including CYP144 and CYP142, with high affinity and are excellent potential candidates as novel anti-mycobacterial agents. An N-terminally truncated form of CYP144, CYP144-T, has been investigated in the pursuit of a CYP144 crystal structure. It is hoped that this will enable the elucidation of a physiological role for CYP144. Both CYP142 and CYP144 have demonstrated biochemical and biophysical characteristics that contribute to our knowledge of P450 enzymes. This PhD has established that CYP142 exhibits an equilibrium between P450 and P420 species in its CO-bound, ferrous form. A conversion from P420, and stabilisation of P450, upon substrate binding was also demonstrated. CYP144 displays unusual azole coordination characteristics when examined by EPR and removal of the CYP144 gene from Mtb increased sensitivity of the strain to clotrimazole. Studies of these enzymes has advanced knowledge of P450 and Mtb redox chemistry, established roles for the MtbP450 cohort and identified the potential of anti-mycobacterial drugs and associated targets.EThOS - Electronic Theses Online ServiceGBUnited Kingdo