Seasonal cruise Q4 2019 : Cruise Report

This cruise was the second of in total four seasonal cruises with RV Kronprins Haakon in 2019/20 focusing on biology in the project Arven etter Nansen (AeN). This seasonal cruise was named Q4 (Q4= 4th quarter of the year) investigating in total 17 stations of the established AeN transect along 34 E in the Northern Barents Sea and adjacent Arctic Basin from 76 to 82°N (see Fig. 1 below). The cruise addressed objectives of the research foci in RF1 on Physical drivers, RF2 on Human drivers, RF3 on the living Barents Sea and RA-C Technology and method development, and collected a multitude of data along the Nansen Legacy transect which was ice covered except the southernmost station P1. In addition to in situ sampling, on board experiments were conducted to quantify biological processes, rates and interactions that will also be important feeds into modeling work and projections in RF4 The future Barents Sea. The cruise took a variety of continuous ship measurements (Weather station, EK80, EM203, ADCP, thermosalinograph, pCO2 underway) as well as station measurements such as CTD with water samples, biological sampling of the benthos (box corer, benthic trawl), water column (multinet, MIK net, macrozooplankton trawl and many other smaller nets) and sea ice (snow, ice cores, water just underneath sea ice). In addition, experimental work (respiration, grazing and egg production) was conducted in the ship’s laboratories. The chemistry team onboard measured oxygen, nutrients and pH from standard depths on most CTD stations and sea ice samples. The cruise started in Longyearbyen and ended in Tromsø (28.11.-17.12.2019). The sampling began at the deep (>3000 m) northernmost station of the transect, Stn. P7, and continued along the southward transect until station P1, in open water and Atlantic dominated water masses. During the expedition the Barents Sea was characterized by a relatively large sea ice cover with consolidated sea ice all the way from P7 to P2. The Polar Front was located just north of P1. All process stations were sampled (P7-P1) as well as two ice stations: one close to P7 ad one close to P5. At the southernmost station P1, stormy weather challenged sampling, but most tasks were in the end accomplished except of deploying the box corer, sediment trap and the AUV. These operations were considered too challenging due to strong drift and ship movement, and it was not safe to conduct small boat operations. Challenges with the box corer was also experienced at the deep station P7 due to technical issues. In the end, most work was accomplished despite challenging weather, sea ice conditions and some technical issues making this cruise successful in gaining new important knowledge about the Northern Barents Sea in the polar night season which is extremely poorly studied. The overall high biological activity and biomass at this time of the year, November-December, was surprising for most of us

Generalizability of clinical prediction models in mental health

Concerns about the generalizability of machine learning models in mental health arise, partly due to sampling effects and data disparities between research cohorts and real-world populations. We aimed to investigate whether a machine learning model trained solely on easily accessible and low-cost clinical data can predict depressive symptom severity in unseen, independent datasets from various research and real-world clinical contexts. This observational multi-cohort study included 3021 participants (62.03% females, MAge = 36.27 years, range 15–81) from ten European research and clinical settings, all diagnosed with an affective disorder. We firstly compared research and real-world inpatients from the same treatment center using 76 clinical and sociodemographic variables. An elastic net algorithm with ten-fold cross-validation was then applied to develop a sparse machine learning model for predicting depression severity based on the top five features (global functioning, extraversion, neuroticism, emotional abuse in childhood, and somatization). Model generalizability was tested across nine external samples. The model reliably predicted depression severity across all samples (r = 0.60, SD = 0.089, p < 0.0001) and in each individual external sample, ranging in performance from r = 0.48 in a real-world general population sample to r = 0.73 in real-world inpatients. These results suggest that machine learning models trained on sparse clinical data have the potential to predict illness severity across diverse settings, offering insights that could inform the development of more generalizable tools for use in routine psychiatric data analysis

Stem Cell Therapy with Overexpressed VEGF and PDGF Genes Improves Cardiac Function in a Rat Infarct Model

Therapeutic potential was evaluated in a rat model of myocardial infarction using nanofiber-expanded human cord blood derived hematopoietic stem cells (CD133+/CD34+) genetically modified with VEGF plus PDGF genes (VIP).Myocardial function was monitored every two weeks up to six weeks after therapy. Echocardiography revealed time dependent improvement of left ventricular function evaluated by M-mode, fractional shortening, anterior wall tissue velocity, wall motion score index, strain and strain rate in animals treated with VEGF plus PDGF overexpressed stem cells (VIP) compared to nanofiber expanded cells (Exp), freshly isolated cells (FCB) or media control (Media). Improvement observed was as follows: VIP>Exp> FCB>media. Similar trend was noticed in the exercise capacity of rats on a treadmill. These findings correlated with significantly increased neovascularization in ischemic tissue and markedly reduced infarct area in animals in the VIP group. Stem cells in addition to their usual homing sites such as lung, spleen, bone marrow and liver, also migrated to sites of myocardial ischemia. The improvement of cardiac function correlated with expression of heart tissue connexin 43, a gap junctional protein, and heart tissue angiogenesis related protein molecules like VEGF, pNOS3, NOS2 and GSK3. There was no evidence of upregulation in the molecules of oncogenic potential in genetically modified or other stem cell therapy groups.Regenerative therapy using nanofiber-expanded hematopoietic stem cells with overexpression of VEGF and PDGF has a favorable impact on the improvement of rat myocardial function accompanied by upregulation of tissue connexin 43 and pro-angiogenic molecules after infarction

Intracranial, intratumoral implantation of drug-releasing microdevices in patients with high grade gliomas is feasible, safe, and may predict tumor response to systemic chemotherapy

Abstract The lack of reliable predictive biomarkers to guide effective therapy is a major obstacle for the advancement of therapy for high grade gliomas (HGG), and particularly glioblastoma (GBM), one of the few cancers whose prognosis has not improved over the past several decades. With this pilot clinical trial we provide first in human evidence that drug-releasing intratumoral microdevices (IMD) can be safely and effectively used to obtain patient-specific, high throughput molecular and histopathological data to inform selection of drugs based on their observed antitumor effect in situ. The use of IMD is seamlessly integrated in standard surgical practice during tumor resection. None of the six enrolled patients experienced adverse events related to the IMD, and the retrieved tissue was usable for downstream analysis for 11 out of 12 retrieved specimens. Molecular analysis of the specimens provided, for the first time in humans, preliminary evidence of the robustness of the readout, with strong correlation between IMD analysis and clinic-radiological responses to temozolomide. From an investigational aspect, the amount of information obtained with IMD allows unprecedented characterization of tissue effects of any drugs of interest, within the physiological context of the intact tumor.</jats:p

Intracranial, intratumoral implantation of drug-releasing microdevices in patients with high grade gliomas is feasible, safe, and may predict tumor response to systemic chemotherapy

Abstract The lack of reliable predictive biomarkers to guide effective therapy is a major obstacle for the advancement of therapy for high grade gliomas (HGG), and particularly glioblastoma (GBM), one of the few cancers whose prognosis has not improved over the past several decades. With this pilot clinical trial we provide first in human evidence that drug-releasing intratumoral microdevices (IMD) can be safely and effectively used to obtain patient-specific, high throughput molecular and histopathological data to inform selection of drugs based on their observed antitumor effect in situ. The use of IMD is seamlessly integrated in standard surgical practice during tumor resection. None of the six enrolled patients experienced adverse events related to the IMD, and the retrieved tissue was usable for downstream analysis for 11 out of 12 retrieved specimens. Molecular analysis of the specimens provided, for the first time in humans, preliminary evidence of the robustness of the readout, with strong correlation between IMD analysis and clinic-radiological responses to temozolomide. From an investigational aspect, the amount of information obtained with IMD allows unprecedented characterization of tissue effects of any drugs of interest, within the physiological context of the intact tumor.</jats:p