Redox activation of metal-based prodrugs as a strategy for drug delivery

This review provides an overview of metal-based anticancer drugs and drug candidates. In particular, we focus on metal complexes that can be activated in the reducing environment of cancer cells, thus serving as prodrugs. There are many reports of Pt and Ru complexes as redox-activatable drug candidates, but other d-block elements with variable oxidation states have a similar potential to serve as prodrugs in this manner. In this context are compounds based on Fe, Co, or Cu chemistry, which are also covered. A trend in the field of medicinal inorganic chemistry has been toward molecularly targeted, metal-based drugs obtained by functionalizing complexes with biologically active ligands. Another recent activity is the use of nanomaterials for drug delivery, exploiting passive targeting of tumors with nano-sized constructs made from Au, Fe, carbon, or organic polymers. Although complexes of all of the above mentioned metals will be described, this review focuses primarily on Pt compounds, including constructs containing nanomaterials.German Academic Exchange Service (DAAD fellowship)German Academic Exchange Service (DAAD reintegration grant)National Cancer Institute (U.S.) (grant CA034992

Synthese modifizierter Oligonucleotid-Sonden für den DNA-Nachweis durch katalytische Signalamplifikation

Die Arbeit behandelt die Synthese Metallkomplex-funktionalisierter Oligonucleotide und Peptidnucleinsäuren (PNAs) und deren Anwendung als Sonden für den sequenzspezifischen DNA-Nachweis. Zum einen wurde dazu Metallkatalyse am DNA-Templat genutzt, zum anderen Signalverstärkung durch chemische oder enzymatische Katalyse. Eine Nucleinsäure kann als Templat funktionalisierte Oligonucleotide in räumliche Nähe zueinander bringen und deren intermolekulare Reaktion beschleunigen. Die im Rahmen dieser Arbeit untersuchte Redoxreaktion zwischen einer Metallkomplex- und einer Thiol-funktionalisierten Peptidnucleinsäure am DNA-Templat wurde fluorimetrisch verfolgt. Für die Nachweisreaktionen, die auf Signalverstärkung beruhen, wurden Konjugate von DNA-Oligonucleotiden mit Metallkomplexen synthetisiert, die bei Hybridisierung mit komplementärer DNA das Metallion (CuII, ZnII) freisetzen. Das CuII-Ion (bzw. ZnII-Ion) aktiviert als Cofaktor einen chemischen Präkatalysator (bzw. ein Apoenzym) und die anschließend durch den Katalysator (bzw. das Holoenzym) initiierten Reaktionen machen das DNA-Target durch Umsetzung fluoro- oder chromogener Substrate fluorimetrisch, photometrisch oder sogar für das Auge direkt sichtbar. Das DNA-Target konnte mithilfe dieser Signalwandlung (DNA - Metallion) mit hoher Empfindlichkeit und Sequenzspezifität nachgewiesen werden. Die verwendeten Metallkomplex-funktionalisierten Oligonucleotide wurden zum besseren Verständnis der Signalwandlung hinsichtlich ihrer thermodynamischen und kinetischen Stabilität charakterisiert. In einer Variante dieser Oligonucleotid-Konjugate wirkte das Metallion CuII als intramolekularer Fluoreszenzlöscher, sodass die Bindung einer komplementären Nucleinsäure in Form eines Fluoreszenzsignals verfolgt werden kann

Platinum(IV)-chlorotoxin (CTX) conjugates for targeting cancer cells

Cisplatin is one of the most widely used anticancer drugs. Its side effects, however, have motivated researchers to search for equally effective analogs that are better tolerated. Selectively targeting cancer tissue is one promising strategy. For this purpose, a platinum(IV) complex was conjugated to the cancer-targeting peptide chlorotoxin (CTX, TM601) in order to deliver cisplatin selectively to cancer cells. The 1:1 Pt-CTX conjugate was characterized by mass spectrometry and gel electrophoresis. Like most platinum(IV) derivatives, the cytotoxicity of the conjugate was lower in cell culture than that of cisplatin, but greater than those of its Pt(IV) precursor and CTX in several cancer cell lines.National Cancer Institute (U.S.) (Grant CA034992)German Academic Exchange Service (Fellowship

Serum Pancreatic Stone Protein Reference Values in Healthy Pregnant Women: A Prospective Cohort Study

Background: In non-pregnant populations, pancreatic stone protein (PSP) has been reported to have a higher diagnostic performance for identifying severe inflammatory and infectious disease than other established biomarkers. Objective: To generate reference values for serum PSP in pregnancy and compare them to the values of the general healthy population. Design: A prospective cohort study. Setting: A single center. Population: Healthy women with singleton and multiple pregnancies. Methods: This is a prospective single-center cohort study. Between 2013 and 2021, samples of 5 mL peripheral blood were drawn from 440 healthy pregnant women. Therein, 393 cases were singletons and 47 were multiple pregnancies. Serum PSP levels were measured by specific enzyme-linked immunosorbent assay. The main outcome measures were serum PSP level (ng/mL) reference values in healthy pregnant women. Results: The mean PSP reference values in women with singleton pregnancies were 7.9 ± 2.6 ng/mL (95% CI; 2.69–13.03 ng/mL). The PSP values in women with multiple pregnancies (9.17 ± 3.06 ng/mL (95% CI; 3.05–15.28 ng/mL)) were significantly higher (p = 0.001). The PSP values in the first trimester (6.94 ± 2.53 ng/mL) were lower compared to the second (7.42 ± 2.21 ng/mL) and third trimesters (8.33 ± 2.68 ng/mL, p = 0.0001). Subgroup analyses in singletons revealed no correlations between PSP values, maternal characteristics, and pre-existing medical conditions. Conclusion: The PSP values in healthy pregnant women (4–12 ng/mL) were in the range of the reference values of the general healthy population (8–16 ng/mL). This insight blazes a trail for further clinical studies on the use of PSP as a potential novel biomarker for the early detection of pregnancy-related diseases such as chorioamnionitis

The Role of Pancreatic Stone Protein (PSP) as a Biomarker of Pregnancy-Related Diseases

Background: Pancreatic stone protein (PSP) is a biochemical serum marker that contains levels that are elevated in various inflammatory and infectious diseases. The role of PSP in the diagnosis of these diseases seems to be more important compared to clinically established biochemical serum markers in discriminating the severity of the same diseases. Standard values for PSP in pregnant women in relation to gestational age have been reported recently. Additionally, increased PSP levels have been observed to be associated with renal dysfunction in pregnant women. The aim of this study is to evaluate the diagnostic role of PSP in pregnancy-related diseases, such as pre-eclampsia (PE), hemolysis-elevated liver enzymes, and low platelet (HELLP) syndrome. In addition, the study aims to assess its diagnostic role in inflammation-triggered diseases as preterm premature rupture of membranes (PPROM) or COVID-19-positive pregnant women. Materials and Methods: In this single-centred prospective study performed at a tertiary university hospital between 2013 and 2021, we included 152 pregnant women who were diagnosed with either PE, HELLP syndrome, or PPROM. In December 2020, in the context of the COVID-19 pandemic, the Independent Ethics Committee (IEC) approved an amendment to the study protocol. Depending on the underlying disease, single or serial-serum PSP measurements were assessed. These PSP values were compared to PSP levels of women with normal pregnancies. Results: Pregnant women diagnosed with pre-eclampsia or HELLP syndrome had significantly increased PSP values (mean 9.8 ng/mL, SD 2.6) compared to healthy singleton pregnant women (mean 7.9 ng/mL, SD 2.6, p ≤ 0.001). There was no difference in serum PSP in pregnant women with PPROM compared to women with uncomplicated singleton pregnancies (mean in PPROM: 7.9 ng/mL; SD 2.9 versus mean in healthy pregnancies: 7.9 ng/mL; SD 2.6, p = 0.98). Furthermore, no difference in the PSP values in women with or without intra-amniotic infection was observed (infection: mean 7.9 ng/mL; SD 2.8 versus no infection: mean 7.8 ng/mL; SD 3, p = 0.85). The mean value of PSP in COVID-19-infected women during pregnancy (8.5 ng/mL, SD 2.3) was comparable to healthy singleton pregnancies (mean 7.9 ng/mL, SD 2.6), p = 0.24. Conclusions: The novel serum biomarker PSP is significantly upregulated in pregnant women with pre-eclampsia and HELLP syndrome. Our observations call for the further evaluation of PSP in randomized controlled clinical trials to demonstrate the actual role of PSP in pregnancy-related diseases and whether it may provide new approaches for the management and discrimination of the severity of these gestational conditions

Prognostic Significance of the Myelodysplastic Syndrome-Specific Comorbidity Index (MDS-CI) in Patients with Myelofibrosis: A Retrospective Study.

In myelofibrosis, comorbidities (CMs) add prognostic information independently from the Dynamic International Prognostic Scoring System (DIPSS). The Myelodysplastic Syndrome-Specific Comorbidity Index (MDS-CI) offers a simple tool for CM assessment as it is calculable after having performed a careful history and physical examination, a small routine chemistry panel (including creatinine and liver enzymes) and a limited set of functional diagnostics. To assess the prognostic impact of the MDS-CI in addition to the DIPSS and the Mutation-Enhanced International Prognostic Scoring System (MIPSS)-70, we performed a retrospective chart review of 70 MF patients who had not received allogeneic stem cell transplantation (primary MF, n = 51; secondary MF, n = 19; median follow-up, 40 months) diagnosed at our institution between 2000 and 2020. Cardiac diseases (23/70) and solid tumors (12/70) were the most common CMs observed at MF diagnosis. Overall survival (OS) was significantly influenced by the MDS-CI (median OS MDS-CI low (n = 38): 101 months; MDS-CI intermediate (n = 25): 50 months; and high (n = 7): 8 months; p < 0.001). The MDS-CI added prognostic information after inclusion as a categorical variable in a multivariate model together with the dichotomized DIPSS or the dichotomized MIPSS70: MDS-CI high HR 14.64 (95% CI 4.42; 48.48), p = 0.0002, and MDS-CI intermediate HR 1.97 (95% CI 0.96; 4.03), p = 0.065, and MDS-CI high HR 19.65 (95% CI 4.71; 81.95), p < 0.001, and MDS-CI intermediate HR 1.063 (95% CI 0.65; 4.06), p = 0.2961, respectively. The analysis of our small and retrospective MF cohort suggests that the MDS-CI represents a useful tool to identify MF patients with an increased vulnerability due to comorbidities. However, analyses of larger cohorts are necessary to define the value of the MDS-CI as a prognostic tool in comparison with other comorbidity indices

Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org

CRP/Albumin Ratio and Glasgow Prognostic Score Provide Prognostic Information in Myelofibrosis Independently of MIPSS70-A Retrospective Study.

In myelofibrosis, the C-reactive protein (CRP)/albumin ratio (CAR) and the Glasgow Prognostic Score (GPS) add prognostic information independently of the Dynamic International Prognostic Scoring System (DIPSS). Their prognostic impact, if molecular aberrations are considered, is currently unknown. We performed a retrospective chart review of 108 MF patients (prefibrotic MF n = 30; primary MF n = 56; secondary MF n = 22; median follow-up 42 months). In MF, both a CAR > 0.347 and a GPS > 0 were associated with a shorter median overall survival (21 [95% CI 0-62] vs. 80 months [95% CI 57-103], p 0.374 HR 3.53 [95% CI 1.36-9.17], p = 0.0095 and GPS > 0 HR 4.63 [95% CI 1.76-12.1], p = 0.0019. An analysis of serum samples from an independent cohort revealed a correlation of CRP with levels of interleukin-1β and albumin with TNF-α, and demonstrated that CRP was correlated to the variant allele frequency of the driver mutation, but not albumin. Albumin and CRP as parameters readily available in clinical routine at low costs deserve further evaluation as prognostic markers in MF, ideally by analyzing data from prospective and multi-institutional registries. Since both albumin and CRP levels reflect different aspects of MF-associated inflammation and metabolic changes, our study further highlights that combining both parameters seems potentially useful to improve prognostication in MF

Single-shot velocity-map imaging of attosecond light-field control at kilohertz rate

High-speed, single-shot velocity-map imaging (VMI) is combined with carrier- envelope phase (CEP) tagging by a single-shot stereographic above-threshold ionization (ATI) phase-meter. The experimental setup provides a versatile tool for angle-resolved studies of the attosecond control of electrons in atoms, molecules, and nanostructures. Single-shot VMI at kHz repetition rate is realized with a highly sensitive megapixel complementary metal-oxide semiconductor camera omitting the need for additional image intensifiers. The developed camerasoftware allows for efficient background suppression and the storage of up to 1024 events for each image in real time. The approach is demonstrated by measuring the CEP-dependence of the electron emission from ATI of Xe in strong (≈1013 W/cm2) near single-cycle (4 fs) laser fields. Efficient background signal suppression with the system is illustrated for the electron emission from SiO2nanospheres

Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org