Background Information on pulmonary sequelae and pulmonary function 2 years after recovery from SARS-CoV-2 infection is lacking. Purpose To longitudinally assess changes in chest CT abnormalities and pulmonary function in individuals after SARS-CoV-2 infection. Materials and Methods In this prospective study, participants discharged from the hospital after SARS-CoV-2 infection from January 20 to March 10, 2020, were considered for enrollment. Participants without chest CT scans at admission or with complete resolution of lung abnormalities at discharge were excluded. Serial chest CT scans and pulmonary function test results were obtained 6 months (June 20 to August 31, 2020), 12 months (December 20, 2020, to February 3, 2021), and 2 years (November 16, 2021, to January 10, 2022) after symptom onset. The term interstitial lung abnormality (ILA) and two subcategories, fibrotic ILAs and nonfibrotic ILAs, were used to describe residual CT abnormalities on follow-up CT scans. Differences between groups were compared with the χ² test, Fisher exact test, or independent samples t test. Results Overall, 144 participants (median age, 60 years [range, 27-80 years]; 79 men) were included. On 2-year follow-up CT scans, 39% of participants (56 of 144) had ILAs, including 23% (33 of 144) with fibrotic ILAs and 16% (23 of 144) with nonfibrotic ILAs. The remaining 88 of 144 participants (61%) showed complete radiologic resolution. Over 2 years, the incidence of ILAs gradually decreased (54%, 42%, and 39% of participants at 6 months, 12 months, and 2 years, respectively; P < .001). Respiratory symptoms (34% vs 15%, P = .007) and abnormal diffusing capacity of lung for carbon monoxide (43% vs 20%, P = .004) occurred more frequently in participants with ILAs than in those with complete radiologic resolution. Conclusion More than one-third of participants had persistent interstitial lung abnormalities 2 years after COVID-19 infection, which were associated with respiratory symptoms and decreased diffusion pulmonary function. Chinese Clinical Trial Registry no. ChiCTR2000038609 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by van Beek in this issue.

Background: The pathogenesis of neuropathic pain and the reasons for the prolonged unhealing remain unknown. Increasing evidence suggests that sex oestrogen differences play a role in pain sensitivity, but few studies have focused on the oestrogen receptor which may be an important molecular component contributing to peripheral pain transduction. We aimed to investigate the impact of oestrogen receptors on the nociceptive neuronal response in the dorsal root ganglion (DRG) and spinal dorsal horn using a spared nerve injury (SNI) rat model of chronic pain. Methods: We intrathecally (i.t.) administered a class of oestrogen receptor antagonists and agonists intrathecal (i.t.) administrated to male rats with SNI or normal rats to identify the main receptor. Moreover, we assessed genes identified through genomic metabolic analysis to determine the key metabolism point and elucidate potential mechanisms mediating continuous neuronal sensitization and neuroinflammatory responses in neuropathic pain. The excitability of DRG neurons was detected using the patch-clamp technique. Primary culture was used to extract microglia and DRG neurons, and siRNA transfection was used to silence receptor protein expression. Immunofluorescence, Western blotting, RT-PCR and behavioural testing were used to assess the expression, cellular distribution, and actions of the main receptor and its related signalling molecules. Results: Increasing the expression and function of G protein-coupled oestrogen receptor (GPER), but not oestrogen receptor-α (ERα) and oestrogen receptor-β (ERβ), in the DRG neuron and microglia, but not the dorsal spinal cord, contributed to SNI-induced neuronal sensitization. Inhibiting GPER expression in the DRG alleviated SNI-induced pain behaviours and neuroinflammation by simultaneously downregulating iNOS, IL-1β and IL-6 expression and restoring GABAα2 expression. Additionally, the positive interaction between GPER and β-alanine and subsequent β-alanine accumulation enhances pain sensation and promotes chronic pain development. Conclusion: GPER activation in the DRG induces a positive association between β-alanine with iNOS, IL-1β and IL-6 expression and represses GABAα2 involved in post-SNI neuropathic pain development. Blocking GPER and eliminating β-alanine in the DRG neurons and microglia may prevent neuropathic pain development.

Background: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. Objective: In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. Methods: For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. Results: Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. Conclusions: Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.

Background: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. Objective: We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. Methods: In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants’ clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. Results: Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). Conclusions: Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study’s hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.

Objective: To explore whether the pretreatment dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) and radiomics signatures were associated with pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer. Method: A retrospective review of 70 patients with breast invasive carcinomas proved by biopsy between June 2017 and October 2020 (26 patients were pathological complete response, and 44 patients were non-pathological complete response). Within the pre-contrast and five post-contrast dynamic series, a total of 1037 quantitative imaging features were extracted from in each phase. Additionally, the Δfeatures (the difference between the features before and after the comparison) were used for subsequent analysis. The least absolute shrinkage and selection operator (LASSO) regression method was used to select features related to pCR, and then use these features to train multiple machine learning classifiers to predict the probability of pCR for a given patient. The area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the predictive performances of the radiomics model for each of the five phases of time points. Result: Among the five phases, each individual phase performed with AUCs ranging from 0.845 to 0.919 in predicting pCR. The best single phases performance was given by the 3rd phase (AUC = 0.919, sensitivity 0.885, specificity 0.864). 5 of the features have significant differences between pCR and non-pCR groups in each phase, most features reach their maximum or minimum in the 2nd or 3rd phase. Conclusion: The radiomic features extracted from each phase of pre-treatment DCE-MRI possess discriminatory power to predict tumor response.

Summary: Backgrounds: Since December 2019, a novel coronavirus epidemic has emerged in Wuhan city, China and then rapidly spread to other areas. As of 20 Feb 2020, a total of 2,055 medical staff confirmed with coronavirus disease 2019 (COVID-19) caused by SARS-Cov-2 in China had been reported. We sought to explore the epidemiological, clinical characteristics and prognosis of novel coronavirus-infected medical staff. Methods: In this retrospective study, 64 confirmed cases of novel coronavirus-infected medical staff admitted to Union Hospital, Wuhan between 16 Jan, 2020 to 15 Feb, 2020 were included. Two groups concerned were extracted from the subjects based on duration of symptoms: group 1 (≤10 days) and group 2 (>10 days). Epidemiological and clinical data were analyzed and compared across groups. The Kaplan-Meier plot was used to inspect the change in hospital discharge rate. The Cox regression model was utilized to identify factors associated with hospital discharge. Findings: The median age of medical staff included was 35 years old. 64% were female and 67% were nurses. None had an exposure to Huanan seafood wholesale market or wildlife. A small proportion of the cohort had contact with specimens (5%) as well as patients in fever clinics (8%) and isolation wards (5%). Fever (67%) was the most common symptom, followed by cough (47%) and fatigue (34%). The median time interval between symptoms onset and admission was 8.5 days. On admission, 80% of medical staff showed abnormal IL-6 levels and 34% had lymphocytopenia. Chest CT mainly manifested as bilateral (61%), septal/subpleural (80%) and ground-glass (52%) opacities. During the study period, no patients was transferred to intensive care unit or died, and 34 (53%) had been discharged. Higher body mass index (BMI) (≥ 24 kg/m ² ) (HR 0.14; 95% CI 0.03-0.73), fever (HR 0.24; 95% CI 0.09-0.60) and higher levels of IL-6 on admission (HR 0.31; 95% CI 0.11-0.87) were unfavorable factors for discharge. Interpretation: In this study, medical staff infected with COVID-19 have relatively milder symptoms and favorable clinical course, which may be partly due to their medical expertise, younger age and less underlying diseases. Smaller BMI, absence of fever symptoms and normal IL-6 levels on admission are favorable for discharge for medical staff. Further studies should be devoted to identifying the exact patterns of SARS-CoV-2 infection among medical staff.