Long non-coding RNA UCA1 is a predictive biomarker of cancer

Objective: The long-term impact of COVID-19 on patient health has been a recent focus. This study aims to determine the persistent symptoms and psychological conditions of patients hospitalized with COVID-19 15 months after onset, that patients first developed symptoms. The potential risk factors were also explored. Methods: A cohort of COVID-19 patients discharged from February 20, 2020 to March 31, 2020 was recruited. Follow-ups were conducted using validated questionnaires and psychological screening scales at 15 months after onset to evaluate the patients' health status. The risk factors for long-term health impacts and their associations with disease severity was analyzed. Findings: 534 COVID-19 patients were enrolled. The median age of the patients was 62.0 years old (IQR 52.0–70.0) and 295 were female (55.2%). The median time from onset to follow-up was 460.0 (451.0–467.0) days. Sleep disturbance (18.5%, 99/534) and fatigue (17.2%, 92/534) were the most common persistent symptoms. 6.4% (34/534) of the patients had depression, 9.2% (49/534) were anxious, 13.0% (70/534) had insomnia and 4.7% (25/534) suffered from post-traumatic stress disorder (PTSD). Multivariate adjusted logistic regression analysis showed that glucocorticoid use during hospitalization (OR 3.58, 95% CI 1.12–11.44) was significantly associated with an increased risk of fatigue. The OR values for anxiety and sleep disorders were 2.36 (95% CI 1.07–5.20) and 2.16 (95% CI 1.13–4.14) in females to males. The OR value of PTSD was 25.6 (95% CI 3.3–198.4) in patients with persistent symptoms to those without persistent symptoms. No significant associations were observed between fatigue syndrome or adverse mental outcomes and disease severity. Conclusions: 15-month follow-up in this study demonstrated the need of extended rehabilitation intervention for complete recovery in COVID-19 patients.

Osimertinib, an orally administered third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor, is widely approved for the first-line and second-line treatment of advanced non-small-cell lung cancer (NSCLC) with EGFR mutations. However, the rapid development of osimertinib resistance renders the unsustainable treatment benefit. Patients with EGFR-mutated NSCLC who develop osimertinib resistance, especially those acquiring relatively rare and ‘off-target’ resistance mutations, still lack effective therapeutic options for postosimertinib therapy. Herein, we reported a 73-year-old woman diagnosed with T1N3M1 lung adenocarcinoma harboring EGFR L858R mutation, who acquired two GNAS mutations (R201C and R201H) and lost the EGFR L858R mutation after progression on icotinib and osimertinib. The patient was subsequently treated with trametinib and there was no obvious tumor increase. Our study revealed that GNAS R201 can confer the osimertinib resistance in EGFR-positive NSCLC, and present the first report of the prevalence of GNAS R201C and R201H mutants in NSCLC which response to trametinib treatment. Our case suggests that trametinib could be a treatment option in NSCLC patients harboring GNAS-activating mutations.

BACKGROUND: Treatment of severe Corona Virus Disease 2019 (COVID-19) is challenging. We performed a phase 2 trial to assess the efficacy and safety of human umbilical cord-mesenchymal stem cells (UC-MSCs) to treat severe COVID-19 patients with lung damage, based on our phase 1 data. METHODS: In this randomized, double-blind, and placebo-controlled trial, we recruited 101 severe COVID-19 patients with lung damage. They were randomly assigned to receive either UC-MSCs (4 × 10 ⁷ cells per infusion) or placebo on day 0, 3, and 6. The primary endpoint was an altered proportion of whole lung lesion volumes from baseline to day 28. Other imaging outcomes, 6-minute walk test, maximum vital capacity, diffusing capacity, and adverse events were recorded and analysed. RESULTS: 100 COVID-19 patients were finally recruited to receive either UC-MSCs (n = 65) or placebo (n = 35). UC-MSCs administration exerted numerical improvement in whole lung lesion volume from baseline to day 28 compared with the placebo (the median difference was -13.31%, 95%CI -29.14%, 2.13%, P=0.080). UC-MSCs significantly reduced the proportions of solid component lesion volume compared with the placebo (median difference: -15.45%; 95% CI -30.82%, -0.39%; P=0.043). The 6-minute walk test showed an increased distance in patients treated with UC-MSCs (difference: 27.00 m; 95% CI 0.00, 57.00; P=0.057). The incidence of adverse events was similar in the two groups. CONCLUSIONS: UC-MSCs treatment is a safe and potentially effective therapeutic approach for COVID-19 patients with lung damage. (Funded by The National Key R&D Program of China and others. ClinicalTrials.gov number, NCT04288102 .)

It was to study the recognition performance of the fusion of neural network and genetic algorithm for pulmonary images, and to realize the diagnosis of pulmonary diseases by recognizing the respiratory sound signals. Pulmonary computerized tomography (CT) images were selected as the data base, and the genetic algorithm was applied to achieve fast global optimal search. On the combination of neural network and genetic algorithm, an improved genetic intelligent algorithm model was put forward. The simulation experiments were performed to compare the performances such as the algorithmic rate, accuracy, and sensitivity, so as to verify the superiority of the model. Then, the proposed algorithm was used to verify its effectiveness by collecting the respiratory sound signals of related diseases. The genetic algorithm could not only obtain the global optimal solution, but also greatly shorten the calculation time. With the pulmonary CT images, the complete segmentation of the pulmonary airways and the recognition of pulmonary images could be achieved. The algorithm could effectively recognize respiratory sound signals of health people and patients with chronic obstructive pulmonary diseases (COPD) and pneumonia. Its accuracy reached 0.943, with a precision of 0.921 and a recall rate of 0.931. It allowed to achieve the goal of diagnosing pulmonary diseases by respiratory sound signals. The fusion of neural network and genetic algorithm could realize pulmonary image recognition, and the diagnosis of pulmonary diseases could also be diagnosed through the feature analysis of respiratory sound signals.