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Long-term therapy for unresectable colorectal liver metastases remains challenging. Intraarterial treatments aim to avoid systemic adverse effects of chemotherapy. Nanoliposomal cytotoxic drugs manage to increase the drug concentration within the tumor while reducing toxicity in healthy tissue. In this study we analyzed the effect of hepatic arterial infusion (HAI) with nanoliposomal irinotecan with or without the combination of embolization particles in a rat model for colorectal liver metastases. For the study 32 WAG/Rij rats received subcapsular tumor implantation with CC531 rat colonic adenocarcinoma cells. After ten days tumor size was assessed via ultrasound and animals underwent HAI. One group served as control receiving NaCl 0.9 % (Sham), the three treatment groups received either nanoliposomal irinotecan (HAI nal iri), Embocept® S (HAI Embo) or Embocept® S and nanoliposomal irinotecan (HAI Embo+nal iri). Three days after treatment animals were sacrificed after assessment of tumor size. As a result all treatment groups showed a significant reduction in tumor growth compared to Sham (p<0.05). Expression of the apoptosis marker caspase-3 was enhanced in HAI nal iri and HAI Embo+nal iri compared to Sham and HAI Embo and even significantly enhanced after HAI Embo+nal iri in comparison to Sham (p<0.05). We were able to show that HAI with Embocept® S led to significantly reduced tumor growth while HAI with nanoliposomal irinotecan alone or in combination with Embocept® S even led to a reduction of tumor size. Thus, we demonstrate that intraarterial treatment with nanoliposomal irinotecan effectively inhibits tumor growth in a rat model of colorectal liver metastases and demands further investigation.

Intraarterial chemotherapy for colorectal liver metastases (CRLM) can be applied alone or together with embolization particles. It remains unclear whether different types of embolization particles lead to higher intratumoral drug concentration. Herein, we quantified the concentrations of CPT-11 and its active metabolite SN-38 in plasma, liver and tumor tissue after hepatic arterial infusion (HAI) of irinotecan, with or without further application of embolization particles, in a rat model of CRLM. Animals underwent either systemic application of irinotecan, or HAI with or without the embolization particles Embocept® S and Tandem™. Four hours after treatment concentrations of CPT-11 and SN-38 were analyzed in plasma, tumor and liver samples by high-performance liquid chromatography. Additionally, DNA-damage and apoptosis were analyzed immunohistochemically. Tumor tissue concentrations of SN-38 were significantly increased after HAI with irinotecan and EmboCept® S compared to the other groups. The number of apoptotic cells was significantly higher after both HAI with irinotecan and EmboCept® S or Tandem™ loaded with irinotecan compared to the control group. HAI with irinotecan and EmboCept® S resulted in an increased SN-38 tumor concentration. Both HAI with irinotecan and EmboCept® S or Tandem™ loaded with irinotecan were highly effective with regard to apoptosis.

Low-cost air pollution sensors often fail to attain sufficient performance compared with state-of-the-art measurement stations, and they typically require expensive laboratory-based calibration procedures. A repeatedly proposed strategy to overcome these limitations is calibration through co-location with public measurement stations. Here we test the idea of using machine learning algorithms for such calibration tasks using hourly-averaged co-location data for nitrogen dioxide (NO2) and particulate matter of particle sizes smaller than 10 µm (PM10) at three different locations in the urban area of London, UK. We compare the performance of ridge regression, a linear statistical learning algorithm, to two non-linear algorithms in the form of random forest regression (RFR) and Gaussian process regression (GPR). We further benchmark the performance of all three machine learning methods relative to the more common multiple linear regression (MLR). We obtain very good out-of-sample R2 scores (coefficient of determination) >0.7, frequently exceeding 0.8, for the machine learning calibrated low-cost sensors. In contrast, the performance of MLR is more dependent on random variations in the sensor hardware and co-located signals, and it is also more sensitive to the length of the co-location period. We find that, subject to certain conditions, GPR is typically the best-performing method in our calibration setting, followed by ridge regression and RFR. We also highlight several key limitations of the machine learning methods, which will be crucial to consider in any co-location calibration. In particular, all methods are fundamentally limited in how well they can reproduce pollution levels that lie outside those encountered at training stage. We find, however, that the linear ridge regression outperforms the non-linear methods in extrapolation settings. GPR can allow for a small degree of extrapolation, whereas RFR can only predict values within the training range. This algorithm-dependent ability to extrapolate is one of the key limiting factors when the calibrated sensors are deployed away from the co-location site itself. Consequently, we find that ridge regression is often performing as good as or even better than GPR after sensor relocation. Our results highlight the potential of co-location approaches paired with machine learning calibration techniques to reduce costs of air pollution measurements, subject to careful consideration of the co-location training conditions, the choice of calibration variables and the features of the calibration algorithm.

Cognitive deficits are central attendant symptoms of major depressive disorder (MDD) with a crucial impact in patients’ everyday life. Thus, it is of particular clinical importance to understand their pathophysiology. The aim of this study was to investigate a possible relationship between brain structure and cognitive performance in MDD patients in a well-characterized sample. N = 1007 participants (NMDD = 482, healthy controls (HC): NHC = 525) were selected from the FOR2107 cohort for this diffusion-tensor imaging study employing tract-based spatial statistics. We conducted a principal component analysis (PCA) to reduce neuropsychological test results, and to discover underlying factors of cognitive performance in MDD patients. We tested the association between fractional anisotropy (FA) and diagnosis (MDD vs. HC) and cognitive performance factors. The PCA yielded a single general cognitive performance factor that differed significantly between MDD patients and HC (P < 0.001). We found a significant main effect of the general cognitive performance factor in FA (Ptfce-FWE = 0.002) in a large bilateral cluster consisting of widespread frontotemporal-association fibers. In MDD patients this effect was independent of medication intake, the presence of comorbid diagnoses, the number of previous hospitalizations, and depressive symptomatology. This study provides robust evidence that white matter disturbances and cognitive performance seem to be associated. This association was independent of diagnosis, though MDD patients show more pronounced deficits and lower FA values in the global white matter fiber structure. This suggests a more general, rather than the depression-specific neurological basis for cognitive deficits.

Low-cost air pollution sensors often fail to attain sufficient performance compared with state-of-the-art measurement stations, and they typically require expensive laboratory-based calibration procedures. A repeatedly proposed strategy to overcome these limitations is calibration through co-location with public measurement stations. Here we test the idea of using machine learning algorithms for such calibration tasks using hourly-averaged co-location data for nitrogen dioxide (NO2) and particulate matter of particle sizes smaller than 10 µm (PM10) at three different locations in the urban area of London, UK. We compare the performance of ridge regression, a linear statistical learning algorithm, to two non-linear algorithms in the form of random forest regression (RFR) and Gaussian process regression (GPR). We further benchmark the performance of all three machine learning methods relative to the more common multiple linear regression (MLR). We obtain very good out-of-sample R2 scores (coefficient of determination) >0.7, frequently exceeding 0.8, for the machine learning calibrated low-cost sensors. In contrast, the performance of MLR is more dependent on random variations in the sensor hardware and co-located signals, and it is also more sensitive to the length of the co-location period. We find that, subject to certain conditions, GPR is typically the best-performing method in our calibration setting, followed by ridge regression and RFR. We also highlight several key limitations of the machine learning methods, which will be crucial to consider in any co-location calibration. In particular, all methods are fundamentally limited in how well they can reproduce pollution levels that lie outside those encountered at training stage. We find, however, that the linear ridge regression outperforms the non-linear methods in extrapolation settings. GPR can allow for a small degree of extrapolation, whereas RFR can only predict values within the training range. This algorithm-dependent ability to extrapolate is one of the key limiting factors when the calibrated sensors are deployed away from the co-location site itself. Consequently, we find that ridge regression is often performing as good as or even better than GPR after sensor relocation. Our results highlight the potential of co-location approaches paired with machine learning calibration techniques to reduce costs of air pollution measurements, subject to careful consideration of the co-location training conditions, the choice of calibration variables and the features of the calibration algorithm.

Low-cost air pollution sensors often fail to attain sufficient performance compared with state-of-the-art measurement stations, and they typically require expensive laboratory-based calibration procedures. A repeatedly proposed strategy to overcome these limitations is calibration through co-location with public measurement stations. Here we test the idea of using machine learning algorithms for such calibration tasks using hourly-averaged co-location data for nitrogen dioxide (NO.sub.2) and particulate matter of particle sizes smaller than 10 µm (PM.sub.10) at three different locations in the urban area of London, UK. We compare the performance of ridge regression, a linear statistical learning algorithm, to two non-linear algorithms in the form of random forest regression (RFR) and Gaussian process regression (GPR). We further benchmark the performance of all three machine learning methods relative to the more common multiple linear regression (MLR). We obtain very good out-of-sample R.sup.2 scores (coefficient of determination) 0.7, frequently exceeding 0.8, for the machine learning calibrated low-cost sensors. In contrast, the performance of MLR is more dependent on random variations in the sensor hardware and co-located signals, and it is also more sensitive to the length of the co-location period. We find that, subject to certain conditions, GPR is typically the best-performing method in our calibration setting, followed by ridge regression and RFR. We also highlight several key limitations of the machine learning methods, which will be crucial to consider in any co-location calibration. In particular, all methods are fundamentally limited in how well they can reproduce pollution levels that lie outside those encountered at training stage. We find, however, that the linear ridge regression outperforms the non-linear methods in extrapolation settings. GPR can allow for a small degree of extrapolation, whereas RFR can only predict values within the training range. This algorithm-dependent ability to extrapolate is one of the key limiting factors when the calibrated sensors are deployed away from the co-location site itself. Consequently, we find that ridge regression is often performing as good as or even better than GPR after sensor relocation. Our results highlight the potential of co-location approaches paired with machine learning calibration techniques to reduce costs of air pollution measurements, subject to careful consideration of the co-location training conditions, the choice of calibration variables and the features of the calibration algorithm.

Programmed cell death (PCD) is a fundamental cellular process crucial to development, homeostasis, and immunity in multicellular eukaryotes. In contrast to our knowledge on the regulation of diverse animal cell death subroutines, information on execution of PCD in plants remains fragmentary. Here we make use of the accessibility of the Arabidopsis thaliana root cap to visualize the execution process of developmentally controlled PCD. We identify a succession of selective decompartmentalization events and ion fluxes that are controlled by a gene regulatory network downstream of the NAC transcription factor SOMBRERO (SMB). Surprisingly, breakdown of the large central vacuole is a relatively late and variable event, preceded by an increase of intracellular calcium levels and acidification, release of mitochondrial matrix proteins, leakage of nuclear and endoplasmic reticulum lumina, and release of fluorescent membrane reporters into the cytosol. Elevated intracellular calcium levels and acidification are sufficient to trigger cell death execution specifically in cells that are rendered competent to undergo PCD by SMB activity, suggesting that these ion fluxes act as PCD-triggering signals.