Explore the vast range of titles available.

Large language models such as ChatGPT can produce increasingly realistic text, with unknown information on the accuracy and integrity of using these models in scientific writing. We gathered fifth research abstracts from five high-impact factor medical journals and asked ChatGPT to generate research abstracts based on their titles and journals. Most generated abstracts were detected using an AI output detector, ‘GPT-2 Output Detector’, with % ‘fake’ scores (higher meaning more likely to be generated) of median [interquartile range] of 99.98% ‘fake’ [12.73%, 99.98%] compared with median 0.02% [IQR 0.02%, 0.09%] for the original abstracts. The AUROC of the AI output detector was 0.94. Generated abstracts scored lower than original abstracts when run through a plagiarism detector website and iThenticate (higher scores meaning more matching text found). When given a mixture of original and general abstracts, blinded human reviewers correctly identified 68% of generated abstracts as being generated by ChatGPT, but incorrectly identified 14% of original abstracts as being generated. Reviewers indicated that it was surprisingly difficult to differentiate between the two, though abstracts they suspected were generated were vaguer and more formulaic. ChatGPT writes believable scientific abstracts, though with completely generated data. Depending on publisher-specific guidelines, AI output detectors may serve as an editorial tool to help maintain scientific standards. The boundaries of ethical and acceptable use of large language models to help scientific writing are still being discussed, and different journals and conferences are adopting varying policies.

BACKGROUNDDespite guidelines promoting the prevention and aggressive treatment of ventilator-associated pneumonia (VAP), the importance of VAP as a driver of outcomes in mechanically ventilated patients, including patients with severe COVID-19, remains unclear. We aimed to determine the contribution of unsuccessful treatment of VAP to mortality for patients with severe pneumonia.METHODSWe performed a single-center, prospective cohort study of 585 mechanically ventilated patients with severe pneumonia and respiratory failure, 190 of whom had COVID-19, who underwent at least 1 bronchoalveolar lavage. A panel of intensive care unit (ICU) physicians adjudicated the pneumonia episodes and endpoints on the basis of clinical and microbiological data. Given the relatively long ICU length of stay (LOS) among patients with COVID-19, we developed a machine-learning approach called CarpeDiem, which grouped similar ICU patient-days into clinical states based on electronic health record data.RESULTSCarpeDiem revealed that the long ICU LOS among patients with COVID-19 was attributable to long stays in clinical states characterized primarily by respiratory failure. While VAP was not associated with mortality overall, the mortality rate was higher for patients with 1 episode of unsuccessfully treated VAP compared with those with successfully treated VAP (76.4% versus 17.6%, P < 0.001). For all patients, including those with COVID-19, CarpeDiem demonstrated that unresolving VAP was associated with a transitions to clinical states associated with higher mortality.CONCLUSIONSUnsuccessful treatment of VAP is associated with higher mortality. The relatively long LOS for patients with COVID-19 was primarily due to prolonged respiratory failure, placing them at higher risk of VAP.FUNDINGNational Institute of Allergy and Infectious Diseases (NIAID), NIH grant U19AI135964; National Heart, Lung, and Blood Institute (NHLBI), NIH grants R01HL147575, R01HL149883, R01HL153122, R01HL153312, R01HL154686, R01HL158139, P01HL071643, and P01HL154998; National Heart, Lung, and Blood Institute (NHLBI), NIH training grants T32HL076139 and F32HL162377; National Institute on Aging (NIA), NIH grants K99AG068544, R21AG075423, and P01AG049665; National Library of Medicine (NLM), NIH grant R01LM013337; National Center for Advancing Translational Sciences (NCATS), NIH grant U01TR003528; Veterans Affairs grant I01CX001777; Chicago Biomedical Consortium grant; Northwestern University Dixon Translational Science Award; Simpson Querrey Lung Institute for Translational Science (SQLIFTS); Canning Thoracic Institute of Northwestern Medicine.

Criteria to identify patients who are ready to be liberated from mechanical ventilation (MV) are imprecise, often resulting in prolonged MV or reintubation, both of which are associated with adverse outcomes. Daily protocol-driven assessment of the need for MV leads to earlier extubation but requires dedicated personnel. We sought to determine whether machine learning (ML) applied to the electronic health record could predict next-day extubation. We examined 37 clinical features aggregated from 12AM-8AM on each patient-ICU-day from a single-center prospective cohort study of patients in our quaternary care medical ICU who received MV. We also tested our models on an external test set from a community hospital ICU in our health care system. We used three data encoding/imputation strategies and built XGBoost, LightGBM, logistic regression, LSTM, and RNN models to predict next-day extubation. We compared model predictions and actual events to examine how model-driven care might have differed from actual care. Our internal cohort included 448 patients and 3,095 ICU days, and our external test cohort had 333 patients and 2,835 ICU days. The best model (LSTM) predicted next-day extubation with an AUROC of 0.870 (95% CI 0.834–0.902) on the internal test cohort and 0.870 (95% CI 0.848–0.885) on the external test cohort. Across multiple model types, measures previously demonstrated to be important in determining readiness for extubation were found to be most informative, including plateau pressure and Richmond Agitation Sedation Scale (RASS) score. Our model often predicted patients to be stable for extubation in the days preceding their actual extubation, with 63.8% of predicted extubations occurring within three days of true extubation. Our findings suggest that an ML model may serve as a useful clinical decision support tool rather than complete replacement of clinical judgement. However, any ML-based model should be compared with protocol-based practice in a prospective, randomized controlled trial to determine improvement in outcomes while maintaining safety as well as cost effectiveness.

Regulatory T (Treg) cells orchestrate resolution and repair of acute lung inflammation and injury after viral pneumonia. Compared with younger patients, older individuals experience impaired recovery and worse clinical outcomes after severe viral infections, including influenza and SARS coronavirus 2 (SARS-CoV-2). Whether age is a key determinant of Treg cell prorepair function after lung injury remains unknown. Here, we showed that aging results in a cell-autonomous impairment of reparative Treg cell function after experimental influenza pneumonia. Transcriptional and DNA methylation profiling of sorted Treg cells provided insight into the mechanisms underlying their age-related dysfunction, with Treg cells from aged mice demonstrating both loss of reparative programs and gain of maladaptive programs. Strategies to restore youthful Treg cell functional programs could be leveraged as therapies to improve outcomes among older individuals with severe viral pneumonia.

Pulmonary fibrosis is a relentlessly progressive and often fatal disease with a paucity of available therapies. Genetic evidence implicates disordered epithelial repair, which is normally achieved by the differentiation of small cuboidal alveolar type 2 (AT2) cells into large, flattened alveolar type 1 (AT1) cells as an initiating event in pulmonary fibrosis pathogenesis. Using models of pulmonary fibrosis in young adult and old mice and a model of adult alveologenesis after pneumonectomy,we show that administration of ISRIB, a small molecule that restores protein translation by EIF2B during activation of the integrated stress response (ISR), accelerated the differentiation of AT2 into AT1 cells. Accelerated epithelial repair reduced the recruitment of profibrotic monocyte-derived alveolar macrophages and ameliorated lung fibrosis. These findings suggest a dysfunctional role for the ISR in regeneration of the alveolar epithelium after injury with implications for therapy.

Monocyte-derived alveolar macrophages drive lung injury and fibrosis in murine models and are associated with pulmonary fibrosis in humans. Monocyte-derived alveolar macrophages have been suggested to develop a phenotype that promotes lung repair as injury resolves. We compared single-cell and cytokine profiling of the alveolar space in a cohort of 35 patients with post-acute sequelae of COVID-19 who had persistent respiratory symptoms and abnormalities on a computed tomography scan of the chest that subsequently improved or progressed. The abundance of monocyte-derived alveolar macrophages, their gene expression programs, and the level of the monocyte chemokine CCL2 in bronchoalveolar lavage fluid positively associated with the severity of radiographic fibrosis. Monocyte-derived alveolar macrophages from patients with resolving or progressive fibrosis expressed the same set of profibrotic genes. Our findings argue against a distinct reparative phenotype in monocyte-derived alveolar macrophages, highlighting their utility as a biomarker of failed lung repair and a potential target for therapy. Misharin, Sala and colleagues show that in patients with lung fibrosis after COVID-19, monocyte-derived alveolar macrophages activate an inflammatory and fibrotic program that was similar in patients with either resolving or progressing fibrosis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 180 million people since the onset of the pandemic. Despite similar viral load and infectivity rates between children and adults, children rarely develop severe illness. Differences in the host response to the virus at the primary infection site are among the mechanisms proposed to account for this disparity. Our objective was to investigate the host response to SARS-CoV-2 in the nasal mucosa in children and adults and compare it with the host response to respiratory syncytial virus (RSV) and influenza virus. We analyzed clinical outcomes and gene expression in the nasal mucosa of 36 children with SARS-CoV-2, 24 children with RSV, 9 children with influenza virus, 16 adults with SARS-CoV-2, and 7 healthy pediatric and 13 healthy adult controls. In both children and adults, infection with SARS-CoV-2 led to an IFN response in the nasal mucosa. The magnitude of the IFN response correlated with the abundance of viral reads, not the severity of illness, and was comparable between children and adults infected with SARS-CoV-2 and children with severe RSV infection. Expression of and did not correlate with age or presence of viral infection. SARS-CoV-2-infected adults had increased expression of genes involved in neutrophil activation and T-cell receptor signaling pathways compared with SARS-CoV-2-infected children, despite similar severity of illness and viral reads. Age-related differences in the immune response to SARS-CoV-2 may place adults at increased risk of developing severe illness.

BackgroundIncreases in IL-6 by cancer-associated fibroblasts (CAFs) contribute to colon cancer progression, but the mechanisms involved in the increase of this tumor-promoting cytokine are unknown. The aim of this study was to identify novel targets involved in the dysregulation of IL-6 expression by CAFs in colon cancer.MethodsColonic normal (N), hyperplastic, tubular adenoma, adenocarcinoma tissues, and tissue-derived myo-/fibroblasts (MFs) were used in these studies.ResultsTranscriptomic analysis demonstrated a striking decrease in alcohol dehydrogenase 1B (ADH1B) expression, a gene potentially involved in IL-6 dysregulation in CAFs. ADH1B expression was downregulated in approximately 50% of studied tubular adenomas and all T1-4 colon tumors, but not in hyperplastic polyps. ADH1B metabolizes alcohols, including retinol (RO), and is involved in the generation of all-trans retinoic acid (atRA). LPS-induced IL-6 production was inhibited by either RO or its byproduct atRA in N-MFs, but only atRA was effective in CAFs. Silencing ADH1B in N-MFs significantly upregulated LPS-induced IL-6 similar to those observed in CAFs and lead to the loss of RO inhibitory effect on inducible IL-6 expression.ConclusionOur data identify ADH1B as a novel potential mesenchymal tumor suppressor, which plays a critical role in ADH1B/retinoid-mediated regulation of tumor-promoting IL-6.

Skeletal muscle dysfunction in survivors of pneumonia disproportionately affects older individuals in whom it causes substantial morbidity. We found that skeletal muscle recovery was impaired in old compared with young mice after influenza A virus‐induced pneumonia. In young mice, recovery of muscle loss was associated with expansion of tissue‐resident skeletal muscle macrophages and downregulation of MHC II expression, followed by a proliferation of muscle satellite cells. These findings were absent in old mice and in mice deficient in Cx3cr1. Transcriptomic profiling of tissue‐resident skeletal muscle macrophages from old compared with young mice showed downregulation of pathways associated with phagocytosis and proteostasis, and persistent upregulation of inflammatory pathways. Consistently, skeletal muscle macrophages from old mice failed to downregulate MHCII expression during recovery from influenza A virus‐induced pneumonia and showed impaired phagocytic function in vitro. Like old animals, mice deficient in the phagocytic receptor Mertk showed no macrophage expansion, MHCII downregulation, or satellite cell proliferation and failed to recover skeletal muscle function after influenza A pneumonia. Our data suggest that a loss of phagocytic function in a CX3CR1+ tissue‐resident skeletal muscle macrophage population in old mice precludes satellite cell proliferation and recovery of skeletal muscle function after influenza A pneumonia. A schematic illustrating how impaired phagocytic function in CX3CR1+ tissue‐resident skeletal muscle macrophages prevents muscle recovery after influenza A virus‐induced pneumonia in old mice