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Category: chemistry – Page 168

Initial staging of prostate cancer (PCa) is usually performed with conventional imaging (CI), involving computed tomography (CT) and bone scanning (BS). The aim of this study was to analyze the role of [18F]F-choline positron emission tomography (PET)/CT in the initial management and outcome prediction of PCa patients by analyzing data from a multidisciplinary approach. We retrospectively analyzed 82 patients who were discussed by the uro-oncology board of the University Hospital of Ferrara for primary staging newly diagnosed PCa (median age 72 (56–86) years; median baseline prostate specific antigen (PSA) equal to 8.73 ng/mL). Patients were divided into three groups based on the imaging performed: group A = only CI; group B = CI + [18F]F-choline PET/CT; group C = only [18F]F-choline PET/CT. All data on imaging findings, therapy decisions and patient outcomes were retrieved from hospital information systems. Moreover, we performed a sub-analysis of semiquantitative parameters extracted from [18F]F-choline PET/CT to search any correlation with patient outcomes. The number of patients included in each group was 35, 35 and 12, respectively. Patients with higher values of initial PSA were subjected to CI + PET/CT (p = 0.005). Moreover, the use of [18F]F-choline PET/CT was more frequent in patients with higher Gleason score (GS) or ISUP grade (p = 0.013). The type of treatment performed (surgery n = 33; radiation therapy n = 22; surveillance n = 6; multimodality therapy n = 6; systemic therapy n = 13; not available n = 2) did not show any relationship with the modality adopted to stage the disease. [18F]F-choline PET/CT induced a change of planned therapy in 5/35 patients in group B (14.3%). Moreover, patients investigated with [18F]F-choline PET/CT alone demonstrated longer biochemical recurrence (BCR)-free survival (30.8 months) in comparison to patients of groups A and B (15.5 and 23.5 months, respectively, p = 0.006), probably due to a more accurate selection of primary treatment. Finally, total lesion choline kinase activity (TLCKA) of the primary lesion, calculated by multiplying metabolic tumor volume and mean standardized uptake value (SUVmean), was able to more effectively discriminate patients who had recurrence after therapy compared to those without (p = 0.03). In our real-world experience [18F]F-choline PET/CT as a tool for the initial management of PCa had a relevant impact in terms of therapy selection and was associated with longer BCR-free survival. Moreover, TLCKA of the primary lesion looks a promising parameter for predicting recurrence after curative therapy.

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Genomic analyses, such as next-generation sequencing (NGS) and quantitative polymerase chain reaction (qPCR), require pure nucleic acids and accurate analyte concentrations to perform successful reactions. The purification process to access this genetic material uses methods that rely on detergents, mechanical disruption, and heat to disrupt the cellular structures of nuclei, ribosomes, bacteria, and viruses. Nucleic acid is then purified by performing a solvent extraction, alcohol precipitation, and salting-out.

Contaminants can copurify with nucleic acids

Isolation of nucleic acids (including various forms of DNA and RNA) may be needed from cell harvest, PCR, restriction enzyme digest, agarose gel, and other sources. Several avenues in nucleic acid extraction protocols inadvertently allow the co-precipitation of contaminants owing to the type of starting material or the chosen extraction method (Table 1). In some cases, changing the method or adding another purification step can mitigate or eliminate the copurification issue. However, when contamination remains an issue, it is important to learn as much as possible about the impurities that can denature enzymes, block templates, or otherwise lead to failed chemical reactions necessary for downstream applications.

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Recently, a research team led by Professor Hongzhe SUN from the Department of Chemistry, Faculty of Science, The University of Hong Kong, has published a paper in Nature Communications.

The researchers found that, chromium(III) (Cr(III)), a nutritional supplement, can enhance cells’ ability to metabolise glucose by regulating ATP synthase activity. This process improves mitochondrial deformation caused by high glucose levels and significantly boosts glucose metabolism in type 2 diabetic mice. To uncover the protein targets of Cr(III) and elucidate the molecular mechanism, the team has developed a fluorescent probe for detecting transient metal-protein interactions, achieving a high spatiotemporal resolution tracking of the Cr(III) proteome in HepG2 cells. This led to the identification of Cr(III)-binding proteins within cells. The team then revealed that Cr(III) replaces magnesium ions (Mg2+) in ATP synthase, reduces ATP synthase activity, and activates the downstream AMPK pathway, resulting in improved glucose metabolism. This study provides a novel concept for hypoglycaemic research.

“Although Cr(III) compounds have long been used as a nutritional supplement for diabetes treatment, weight loss and muscle development, its protein target and mechanism of action remain concealed for over half a century. We used a novel fluorescent probe, along with other chemical biology approaches, to uncover the long-standing scientific problem of the biological chemistry of Cr(III) and discovered that Cr(III) targets ATP synthase to regulate glucose,” commented Professor Sun.

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A new technique produces perovskite nanocrystals right where they’re needed, so the exceedingly delicate materials can be integrated into nanoscale.

The nanoscale refers to a length scale that is extremely small, typically on the order of nanometers (nm), which is one billionth of a meter. At this scale, materials and systems exhibit unique properties and behaviors that are different from those observed at larger length scales. The prefix “nano-” is derived from the Greek word “nanos,” which means “dwarf” or “very small.” Nanoscale phenomena are relevant to many fields, including materials science, chemistry, biology, and physics.

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Scientists at Leipzig University, in collaboration with colleagues at Vilnius University in Lithuania, have developed a new method to measure the smallest twists and torques of molecules within milliseconds. The method makes it possible to track the gene recognition of CRISPR-Cas protein complexes, also known as “genetic scissors”, in real time and with the highest resolution. With the data obtained, the recognition process can be accurately characterised and modelled to improve the precision of the genetic scissors. The results obtained by the team led by Professor Ralf Seidel and Dominik Kauert from the Faculty of Physics and Earth Sciences have now been published in the prestigious journal Nature Structural and Molecular Biology.

When bacteria are attacked by a virus, they can defend themselves with a mechanism that fends off the genetic material introduced by the intruder. The key is CRISPR-Cas protein complexes. It is only in the last decade that their function for adaptive immunity in microorganisms has been discovered and elucidated. With the help of an embedded RNA, the CRISPR complexes recognize a short sequence in the attacker’s DNA. The mechanism of sequence recognition by RNA has since been used to selectively switch off and modify genes in any organism. This discovery revolutionized genetic engineering and was already honored in 2020 with the Nobel Prize in Chemistry awarded to Emmanuelle Charpentier and Jennifer A. Doudna.

Occasionally, however, CRISPR complexes also react to gene segments that differ slightly from the sequence specified by the RNA. This leads to undesirable side effects in medical applications. “The causes of this are not yet well understood, as the process could not be observed directly until now,” says Dominik Kauert, who worked on the project as a PhD student.

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A newly discovered pathway for formaldehyde oxidation could be an important general mechanism in tropospheric chemistry. In the new route, absorption of sunlight allows organic molecules to react with atmospheric oxygen in a reaction that had not previously been observed. According to the researchers behind the findings, many compounds in the atmosphere are likely to undergo this process, particularly at low altitudes.

‘We discovered a new way molecules in the atmosphere can react,’ says Scott Kable at the University of New South Wales in Australia. He explains that in this process – called photophysical oxidation (PPO) – a molecule absorbs sunlight and before it breaks into fragments, it reacts with atmospheric oxygen to produce free radicals. In the common photochemical oxidation (PCO) reaction, which has been known for several decades, the molecules are first split by sunlight and then the fragments react with oxygen. ‘Importantly, the free radical fragments formed in the first step of PCO can be measured separately in the atmosphere or a lab,’ points out Kable.

The team demonstrated the PPO mechanism using formaldehyde as a model system. Meredith Jordan from the University of Sydney mentions that many organic compounds released to the environment turn into formaldehyde on their way to being oxidised to carbon dioxide. ‘But most importantly for our research, the spectroscopy and photochemistry of this compound are very well understood,’ she says. ‘Without this detailed pre-existing knowledge, we wouldn’t have been able to find the evidence of PPO.’

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Developing Novel DNA-Based Mechano-Technologies For Human Health — Dr. Khalid Salaita, Ph.D. — Emory University

Dr. Khalid Salaita, Ph.D. (https://www.salaitalab.com/salaita) is a Professor of Chemistry at Emory University in Atlanta, Georgia (USA), program faculty in the Department of Biomedical Engineering at Georgia Tech and Emory, program member of Cancer Cell Biology at Winship Cancer Institute, and most recently is the recent winner Future Insight Prize given by Merck KGaA, Darmstadt, Germany (https://www.emdgroup.com/en/research/open-innovation/futurei…aming.html) for his cutting edge work in the area of mechanobiology.

Dr. Salaita earned his B.S. in Chemistry, from Old Dominion University, his Ph.D. in Chemistry from Northwestern University, completed a postdoctoral fellowship in the Department of Chemistry at the University of California, Berkeley, and then started his own lab at Emory University, investigating the interface between living systems and engineered nanoscale materials. To achieve this goal, his group has pioneered the development of tools like molecular force sensors, DNA mechano-technology, smart therapeutics, and nanoscale mechanical actuators to help manipulate living cells.

In recognition of his work, Dr. Salaita has received a number of awards, most notably: the Alfred P. Sloan Research Fellowship, the Camille-Dreyfus Teacher Scholar award, the National Science Foundation Early CAREER award, and the Kavli Fellowship.

Dr. Salaita is currently a member of the Enabling Bioanalytical and Imaging Technologies (EBIT) study Section and an Associate Editor of Smart Materials. His program has been supported by NSF, NIH, and DARPA.

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Objective: This study describes the expression profiles and roles of cardiac pigment epithelium-derived factor (PEDF) during cardiac development.

Methods: Gene datasets from the Gene Expression Omnibus (GEO) database were used to analyze the correlation between cardiac PEDF expression and heart disease. Western blotting.

Immunohistochemistry, histological staining and echocardiography were used to assess the expression patterns and functions of PEDF during cardiac development.

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When asked how this model can cover such a broad scale, Xie says, that it “is rooted in the integration of mechanistic modeling and ML statistical methods, which allows the model to provide a more comprehensive and nuanced understanding of various aspects of RNA and related processes, while quantifying uncertainties due to limited knowledge.”

For example, she explains that, “The mechanistic aspect of the model captures intricate physical and chemical properties at the atomic level, which supports a deep understanding of the underlying biological processes, and the machine-learning element can effectively capture patterns in complex datasets—such as molecular simulations and single-molecule fluorescence microscopy time-course data—and learn relationships that might not be explicitly described in existing mechanistic models.”

In addition to helping scientists better understand the fundamental biology of RNA, the Northeastern team’s hybrid model promises many commercial benefits in the production of monoclonal antibodies, cell and gene therapies, and mRNA vaccines. As Xie says, “It can advance the knowledge of RNA manufacturing mechanisms and guide simultaneous design/control strategies at different levels, such as RNA sequence selection and specifications of critical quality attributes, with less experiments.”

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