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The vast majority of chronic myeloid leukemia patients express a BCR-ABL1 fusion gene mRNA encoding a 210 kDa tyrosine kinase which promotes leukemic transformation. A possible differential impact of the corresponding BCR-ABL1 transcript variants e13a2 ("b2a2") and e14a2 ("b3a2") on disease phenotype and outcome is still a subject of debate. A total of 1105 newly diagnosed imatinib-treated patients were analyzed according to transcript type at diagnosis (e13a2, n=451; e14a2, n=496; e13a2+e14a2, n=158). No differences regarding age, sex, or Euro risk score were observed. A significant difference was found between e13a2 and e14a2 when comparing white blood cells (88 vs. 65 x 10(9)/L, respectively; P<0.001) and platelets (296 vs. 430 x 109/L, respectively; P<0.001) at diagnosis, indicating a distinct disease phenotype. No significant difference was observed regarding other hematologic features, including spleen size and hematologic adverse events, during imatinib-based therapies. Cumulative molecular response was inferior in e13a2 patients (P=0.002 for major molecular response; P<0.001 for MR4). No difference was observed with regard to cytogenetic response and overall survival. In conclusion, e13a2 and e14a2 chronic myeloid leukemia seem to represent distinct biological entities. However, clinical outcome under imatinib treatment was comparable and no risk prediction can be made according to e13a2 versus e14a2 BCR-ABL1 transcript type at diagnosis. (clinicaltrials.gov identifier: 00055874)
Major molecular remission (MMR) is an important therapy goal in chronic myeloid leukemia (CML). So far, MMR is not a failure criterion according to ELN management recommendation leading to uncertainties when to change therapy in CML patients not reaching MMR after 12 months. At monthly landmarks, for different molecular remission status Hazard ratios (HR) were estimated for patients registered to CML study IV who were divided in a learning and a validation sample. The minimum HR for MMR was found at 2.5 years with 0.28 (compared to patients without remission). In the validation sample, a significant advantage for progression-free survival (PFS) for patients in MMR could be detected (p-value 0.007). The optimal time to predict PFS in patients with MMR could be validated in an independent sample at 2.5 years. With our model we provide a suggestion when to define lack of MMR as therapy failure and thus treatment change should be considered. The optimal response time for 1% BCR-ABL at about 12-15 months was confirmed and for deep molecular remission no specific time point was detected. Nevertheless, it was demonstrated that the earlier the MMR is achieved the higher is the chance to attain deep molecular response later.
West African summer monsoon precipitation is characterized by distinct decadal variability. Due to its welldocumented link to oceanic boundary conditions in various ocean basins it represents a paradigm for decadal predictability. In this study, we reappraise this hypothesis for several sub-regions of sub-Saharan West Africa using the new German contribution to the coupled model intercomparison project phase 5 (CMIP5) near-term prediction system.
In addition, we assume that dynamical downscaling of the global decadal predictions leads to an enhanced predictive skill because enhanced resolution improves the atmospheric response to oceanic forcing and landsurface feedbacks. Based on three regional climate models, a heterogeneous picture is drawn: none of the regional climate models outperforms the global decadal predictions or all other regional climate models in every region nor decade. However, for every test case at least one regional climate model was identified which outperforms the global predictions. The highest predictive skill is found in the western and central Sahel Zone with correlation coefficients and mean-square skill scores exceeding 0.9 and 0.8, respectively.
Background: Melanoma is an aggressive tumor with increasing incidence. To develop accurate prognostic markers and targeted therapies, changes leading to malignant transformation of melanocytes need to be understood. In the Xiphophorus melanoma model system, a mutated version of the EGF receptor Xmrk (Xiphophorus melanoma receptor kinase) triggers melanomagenesis. Cellular events downstream of Xmrk, such as the activation of Akt, Ras, B-Raf or Stat5, were also shown to play a role in human melanomagenesis. This makes the elucidation of Xmrk downstream targets a useful method for identifying processes involved in melanoma formation. Methods: Here, we analyzed Xmrk-induced gene expression using a microarray approach. Several highly expressed genes were confirmed by realtime PCR, and pathways responsible for their induction were revealed using small molecule inhibitors. The expression of these genes was also monitored in human melanoma cell lines, and the target gene FOSL1 was knocked down by siRNA. Proliferation and migration of siRNA-treated melanoma cell lines were then investigated. Results: Genes with the strongest upregulation after receptor activation were FOS-like antigen 1 (Fosl1), early growth response 1 (Egr1), osteopontin (Opn), insulin-like growth factor binding protein 3 (Igfbp3), dual-specificity phosphatase 4 (Dusp4), and tumor-associated antigen L6 (Taal6). Interestingly, most genes were blocked in presence of a SRC kinase inhibitor. Importantly, we found that FOSL1, OPN, IGFBP3, DUSP4, and TAAL6 also exhibited increased expression levels in human melanoma cell lines compared to human melanocytes. Knockdown of FOSL1 in human melanoma cell lines reduced their proliferation and migration. Conclusion: Altogether, the data show that the receptor tyrosine kinase Xmrk is a useful tool in the identification of target genes that are commonly expressed in Xmrk-transgenic melanocytes and melanoma cell lines. The identified molecules constitute new possible molecular players in melanoma development. Specifically, a role of FOSL1 in melanomagenic processes is demonstrated. These data are the basis for future detailed analyses of the investigated target genes.
The impact of imatinib dose on response rates and survival in older patients with chronic myeloid leukemia in chronic phase has not been studied well. We analyzed data from the German CML-Study IV, a randomized five-arm treatment optimization study in newly diagnosed BCR-ABL-positive chronic myeloid leukemia in chronic phase. Patients randomized to imatinib 400 mg/day (IM400) or imatinib 800 mg/day (IM800) and stratified according to age (≥65 years vs. <65 years) were compared regarding dose, response, adverse events, rates of progression, and survival. The full 800 mg dose was given after a 6-week run-in period with imatinib 400 mg/day. The dose could then be reduced according to tolerability. A total of 828 patients were randomized to IM400 or IM800. Seven hundred eighty-four patients were evaluable (IM400, 382; IM800, 402). One hundred ten patients (29 %) on IM400 and 83 (21 %) on IM800 were ≥65 years. The median dose per day was lower for patients ≥65 years on IM800, with the highest median dose in the first year (466 mg/day for patients ≥65 years vs. 630 mg/day for patients <65 years). Older patients on IM800 achieved major molecular remission and deep molecular remission as fast as younger patients, in contrast to standard dose imatinib with which older patients achieved remissions much later than younger patients. Grades 3 and 4 adverse events were similar in both age groups. Five-year relative survival for older patients was comparable to that of younger patients. We suggest that the optimal dose for older patients is higher than 400 mg/day. ClinicalTrials.gov identifier: NCT00055874
The DREAM complex plays an important role in regulation of gene expression during the cell cycle. We have previously shown that the DREAM subunit LIN9 is required for early embryonic development and for the maintenance of the inner cell mass in vitro. In this study we examined the effect of knocking down LIN9 on ESCs. We demonstrate that depletion of LIN9 alters the cell cycle distribution of ESCs and results in an accumulation of cells in G2 and M and in an increase of polyploid cells. Genome-wide expression studies showed that the depletion of LIN9 results in downregulation of mitotic genes and in upregulation of differentiation-specific genes. ChIP-on chip experiments showed that mitotic genes are direct targets of LIN9 while lineage specific markers are regulated indirectly. Importantly, depletion of LIN9 does not alter the expression of pluripotency markers SOX2, OCT4 and Nanog and LIN9 depleted ESCs retain alkaline phosphatase activity. We conclude that LIN9 is essential for proliferation and genome stability of ESCs by activating genes with important functions in mitosis and cytokinesis.
Climate models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts.
Hintergrund
Die Versorgung von Patellafrakturen ist technisch anspruchsvoll. Auch wenn die radiologischen Ergebnisse zumeist zufriedenstellend sind, deckt sich dies häufig nicht mit der subjektiven Einschätzung der Patienten. Die klassische Versorgung mittels Drahtzuggurtung weist einige Komplikationen auf. Die winkelstabile Plattenosteosynthese hat sich in den letzten Jahren biomechanisch als vorteilhaft erwiesen.
Fragestellung
Von wem werden Patellafrakturen in Deutschland versorgt? Wie sieht der aktuelle Versorgungsstandard aus? Haben sich „moderne“ Osteosyntheseformen durchgesetzt? Was sind die häufigsten Komplikationen?
Material und Methoden
Die Mitglieder der Deutschen Gesellschaft für Orthopädie und Unfallchirurgie sowie der Deutschen Kniegesellschaft wurden aufgefordert, an einer Onlinebefragung teilzunehmen.
Ergebnisse
Insgesamt wurden 511 komplett ausgefüllte Fragebogen ausgewertet. Die Befragten sind zum größten Teil auf Unfallchirurgie spezialisiert (51,5 %) und verfügen über langjährige Berufserfahrung in Traumazentren. Die Hälfte der Operateure versorgt ≤5 Patellafrakturen jährlich. In knapp 40 % der Fälle wird die präoperative Bildgebung um eine Computertomographie ergänzt. Die klassische Zuggurtung ist noch die bevorzugte Osteosyntheseform bei allen Frakturtypen (Querfraktur 52 %, Mehrfragmentfrakturen 40 %). Bei Mehrfragmentfrakturen entscheiden sich 30 % der Operateure für eine winkelstabile Plattenosteosynthese. Bei Beteiligung des kaudalen Pols dient als zusätzliche Sicherung die McLaughlin-Schlinge (60 %).
Diskussion
Der Versorgungsstandard von Patellafrakturen in Deutschland entspricht weitgehend der aktualisierten S2e-Leitlinie. Nach wie vor wird die klassische Zuggurtungsosteosynthese als Verfahren der Wahl genutzt. Weitere klinische (Langzeit‑)Studien werden benötigt, um die Vorteile der winkelstabilen Plattenosteosynthese zu verifizieren.
Invasive aspergillosis (IA) is a severe complication in immunocompromised patients. Early diagnosis is crucial to decrease its high mortality, yet the diagnostic gold standard (histopathology and culture) is time‐consuming and cannot offer early confirmation of IA. Detection of IA by polymerase chain reaction (PCR) shows promising potential. Various studies have analysed its diagnostic performance in different clinical settings, especially addressing optimal specimen selection. However, direct comparison of different types of specimens in individual patients though essential, is rarely reported. We systematically assessed the diagnostic performance of an Aspergillus‐specific nested PCR by investigating specimens from the site of infection and comparing it with concurrent blood samples in individual patients (pts) with IA. In a retrospective multicenter analysis PCR was performed on clinical specimens (n = 138) of immunocompromised high‐risk pts (n = 133) from the site of infection together with concurrent blood samples. 38 pts were classified as proven/probable, 67 as possible and 28 as no IA according to 2008 European Organization for Research and Treatment of Cancer/Mycoses Study Group consensus definitions. A considerably superior performance of PCR from the site of infection was observed particularly in pts during antifungal prophylaxis (AFP)/antifungal therapy (AFT). Besides a specificity of 85%, sensitivity varied markedly in BAL (64%), CSF (100%), tissue samples (67%) as opposed to concurrent blood samples (8%). Our results further emphasise the need for investigating clinical samples from the site of infection in case of suspected IA to further establish or rule out the diagnosis.
In many real world settings, imbalanced data impedes model performance of learning algorithms, like neural networks, mostly for rare cases. This is especially problematic for tasks focusing on these rare occurrences. For example, when estimating precipitation, extreme rainfall events are scarce but important considering their potential consequences. While there are numerous well studied solutions for classification settings, most of them cannot be applied to regression easily. Of the few solutions for regression tasks, barely any have explored cost-sensitive learning which is known to have advantages compared to sampling-based methods in classification tasks. In this work, we propose a sample weighting approach for imbalanced regression datasets called DenseWeight and a cost-sensitive learning approach for neural network regression with imbalanced data called DenseLoss based on our weighting scheme. DenseWeight weights data points according to their target value rarities through kernel density estimation (KDE). DenseLoss adjusts each data point’s influence on the loss according to DenseWeight, giving rare data points more influence on model training compared to common data points. We show on multiple differently distributed datasets that DenseLoss significantly improves model performance for rare data points through its density-based weighting scheme. Additionally, we compare DenseLoss to the state-of-the-art method SMOGN, finding that our method mostly yields better performance. Our approach provides more control over model training as it enables us to actively decide on the trade-off between focusing on common or rare cases through a single hyperparameter, allowing the training of better models for rare data points.