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Pulmonary arterial hypertension (PAH) is characterized by endothelial cell (EC) dysfunction. There are no data from living patients to inform whether differential gene expression of pulmonary artery ECs (PAECs) can discern disease subtypes, progression and pathogenesis. We aimed to further validate our previously described method to propagate ECs from right heart catheter (RHC) balloon tips and to perform additional PAEC phenotyping. We performed bulk RNA sequencing of PAECs from RHC balloons. Using unsupervised dimensionality reduction and clustering we compared transcriptional signatures from PAH to controls and other forms of pulmonary hypertension. Select PAEC samples underwent single cell and population growth characterization and anoikis quantification. Fifty-four specimens were analyzed from 49 subjects. The transcriptome appeared stable over limited passages. Six genes involved in sex steroid signaling, metabolism, and oncogenesis were significantly upregulated in PAH subjects as compared to controls. Genes regulating BMP and Wnt signaling, oxidative stress and cellular metabolism were differentially expressed in PAH subjects. Changes in gene expression tracked with clinical events in PAH subjects with serial samples over time. Functional assays demonstrated enhanced replication competency and anoikis resistance. Our findings recapitulate fundamental biological processes of PAH and provide new evidence of a cancer-like phenotype in ECs from the central vasculature of PAH patients. This "cell biopsy" method may provide insight into patient and lung EC heterogeneity to advance precision medicine approaches in PAH., (© 2023. The Author(s).)

Miniaturized electrical stimulation (ES) implants show great promise in practice, but their real-time control by means of biophysical mechanistic algorithms is not feasible due to computational complexity. Here, we study the feasibility of more computationally efficient machine learning methods to control ES implants. For this, we estimate the normalized twitch force of the stimulated extensor digitorum longus muscle on n = 11 Wistar rats with intra- and cross-subject calibration. After 2000 training stimulations, we reach a mean absolute error of 0.03 in an intra-subject setting and 0.2 in a cross-subject setting with a random forest regressor. To the best of our knowledge, this work is the first experiment showing the feasibility of AI to simulate complex ES mechanistic models. However, the results of cross-subject training motivate more research on error reduction methods for this setting., (© 2023. The Author(s).)

While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital's image repository. The authors collected over 6,500 non-public chest X-rays comprising diverse COVID-19 severities, along with radiology reports and RT-PCR data. The authors provisioned one internally held-out and two external test sets to assess model generalizability and compare performance to traditional radiologist interpretation. The pipeline was evaluated on a prospective cohort of 80 radiographs, reporting a 95% diagnostic accuracy. The study mitigates bias in AI model development and demonstrates the value of an end-to-end COVID-19 triage platform., (© 2022. The Author(s).)

Acute kidney injury (AKI) is a major complication after cardiothoracic surgery. Early prediction of AKI could prompt preventive measures, but is challenging in the clinical routine. One important reason is that the amount of postoperative data is too massive and too high-dimensional to be effectively processed by the human operator. We therefore sought to develop a deep-learning-based algorithm that is able to predict postoperative AKI prior to the onset of symptoms and complications. Based on 96 routinely collected parameters we built a recurrent neural network (RNN) for real-time prediction of AKI after cardiothoracic surgery. From the data of 15,564 admissions we constructed a balanced training set (2224 admissions) for the development of the RNN. The model was then evaluated on an independent test set (350 admissions) and yielded an area under curve (AUC) (95% confidence interval) of 0.893 (0.862-0.924). We compared the performance of our model against that of experienced clinicians. The RNN significantly outperformed clinicians (AUC = 0.901 vs. 0.745, p  < 0.001) and was overall well calibrated. This was not the case for the physicians, who systematically underestimated the risk ( p  < 0.001). In conclusion, the RNN was superior to physicians in the prediction of AKI after cardiothoracic surgery. It could potentially be integrated into hospitals' electronic health records for real-time patient monitoring and may help to detect early AKI and hence modify the treatment in perioperative care., Competing Interests: Competing interestsA.M. declares the receipt of consulting and lecturing fees from Medtronic GmbH and Edwards Lifesciences Services GmbH, and consulting fees from Pfizer. C.E. declares ownership of shares in codiag AG. F.S. declares the receipt of honoraria, consultancy fees or travel support from Medtronic GmbH, Biotronik SE & Co., Abbott GmbH & Co. KG, Sanofi S.A., Cardiorentis AG, Novartis Pharma GmbH. J.K. declares the receipt of lecturing fees from, Boston Scientific, LSI Solutions, Edwards, Medtronic, Abbott, Ascyrus Medical GmbH. V.F. declares (institutional) financial activities with Medtronic, Biotronik, Abbott, Boston, Edwards, Berlin Heart, Novartis, Jotec, Zurich Heart in relation to Educational Grants, honoraria, consultancy, research & study funds, fees for travel support. All other authors declare no competing interests., (© The Author(s) 2020.)

Children with neuroblastoma receiving high-dose carboplatin as part of their conditioning regimen for autologous marrow transplantation have a high incidence of speech frequency hearing loss. We evaluated hearing loss in 11 children with advanced stage neuroblastoma who underwent autologous marrow transplantation, following a conditioning regimen containing high-dose carboplatin (2g/m2, total dose). Audiometric evaluations were obtained at diagnosis, prior to and following transplant. Exposure to other known ototoxins also was assessed. All patients sustained worsening of hearing following high-dose carboplatin. Nine of the 11 children (82%) had evidence of speech frequency hearing loss post transplant for which hearing aids were recommended (grades 3-4). Three of the nine children had speech frequency loss prior to transplant which progressed following transplant. The entire group was heavily pre-treated with platinum-containing chemotherapy pre-BMT and had extensive exposure to other ototoxins, including aminoglycoside antibiotics, diuretics, and noise exposure - all of which could have exacerbated the effects of carboplatin. High-dose carboplatin is ototoxic, particularly in patients who have been primed with previous platinum therapy or other ototoxic agents. We conclude that further efforts are needed to monitor and minimize this complication. In cases where hearing loss is inevitable due to cumulative ototoxic exposures, families need to be adequately prepared for the tradeoffs of potentially curable therapy.

The therapy of choice for relapsed childhood acute lymphoblastic leukemia is controversial. We retrospectively compared the outcome of 57 patients who received autologous bone marrow transplantation (BMT) with 17 patients who underwent allogeneic BMT for B cell lineage acute lymphoblastic leukemia after at least one marrow relapse. The allogeneic BMT cohort included only those who would also have been eligible for autologous BMT had they not had a matched sibling donor. Specifically, patients who were not in complete remission, those with T cell positive leukemia, t(9;22) or those with only an extramedullary relapse were excluded from both groups. Conditioning regimens included total body irradiation and chemotherapy. Age, white blood count at diagnosis, and duration of first and longest complete remissions were comparable for the two groups. The median follow-up of the event-free survivors was 4.8 years for those who received an autologous BMT (n = 26) and 4.6 years for those who received an allogeneic BMT (n = 8). The relapse rate was higher in the autologous BMT group and the incidence of non-leukemic deaths higher in the allogeneic BMT group. Event-free survival at 3 years was comparable for the two groups (47% +/- 7 vs 53% +/- 12, autologous vs allogeneic, respectively; P = 0.77). Based upon these findings, we concluded that the outcome for autologous BMT was equivalent to allogeneic BMT for relapsed childhood B cell lineage acute lymphoblastic leukemia in selected clinical situations.