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      The Efficacy and Safety of Anlotinib Plus Etoposide with Cisplatin/Carboplatin in the First-Line Treatment of Lung Cancer: A Phase II Clinical Study

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          Abstract

          Background: The primary aim of this phase II clinical study was to assess the safety and efficacy of combining anlotinib, etoposide, and platinum-based drugs as a first-line treatment for ES-SCLC.

          Methods: Patients underwent the standard chemotherapeutic regimen, consisting of four courses of etoposide plus cisplatin/carboplatin. Additionally, each patient received a 2-week intervention with anlotinib (12 mg/day, once daily). Anlotinib was continued until disease progression, occurrence of unbearable adverse events (AEs), or withdrawal from the research. Progression-free survival (PFS) served as the primary prognostic measure. Secondary measures included the disease control rate (DCR), objective response rate (ORR), overall survival time (OS), and the incidence of AEs.

          Results: The DCR and ORR were 97.6% and 91.0%, respectively. Estimated PFS and OS were 5.0 months (95% CI: 1.0-10.8 months) and 13.0 months (95% CI: 8.4-18.6 months), respectively. No unexpected adverse effects were reported during the trial. The most common adverse reactions included anemia (42.22%), hypertension (53.33%), alopecia (40.00%), elevated transaminase (24.40%), and elevated alkaline phosphatase (24.44%). Sixteen cases (35.56%) were classified as AEs of grades 3-5. No deaths attributed to treatment-related causes occurred in any patient during the trial.

          Conclusion: Combination chemotherapy is currently the first-line therapy for extensive small-cell lung cancer (ES-SCLC). Combining anlotinib with conventional platinum-based chemotherapy demonstrated promising therapeutic outcomes and prognosis in the management of ES-SCLC.

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          Most cited references39

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          Treatment of extensive-stage small cell lung carcinoma: current status and future prospects.

          Small cell lung cancer (SCLC) is an aggressive lung tumour strongly associated with cigarette smoking, with patients often presenting with metastatic disease at the time of diagnosis. Although SCLC is very chemoradiosensitive and high response rates are obtained with treatment, relapse rates are high and the prognosis remains very poor. In limited-stage SCLC, the overall survival rate has been significantly improved by adding dose-hyperfractionated thoracic radiotherapy and prophylactic cranial irradiation to systemic chemotherapy. In contrast, little progress has been made in the treatment of extensive-stage SCLC (ES-SCLC), apart from the recently documented survival gain by the addition of prophylactic cranial irradiation. First-line therapy in ES-SCLC currently consists of chemotherapy, combining a platinum drug with either etoposide or irinotecan as a possible alternative. New treatments are needed in order to improve the prognosis of ES-SCLC, as median survival with current standard treatment is still only 9-10 months from diagnosis. The present review focuses on the management of ES-SCLC, with special attention to the development of new treatment options.
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            Deep learning-based predictive identification of neural stem cell differentiation

            The differentiation of neural stem cells (NSCs) into neurons is proposed to be critical in devising potential cell-based therapeutic strategies for central nervous system (CNS) diseases, however, the determination and prediction of differentiation is complex and not yet clearly established, especially at the early stage. We hypothesize that deep learning could extract minutiae from large-scale datasets, and present a deep neural network model for predictable reliable identification of NSCs fate. Remarkably, using only bright field images without artificial labelling, our model is surprisingly effective at identifying the differentiated cell types, even as early as 1 day of culture. Moreover, our approach showcases superior precision and robustness in designed independent test scenarios involving various inducers, including neurotrophins, hormones, small molecule compounds and even nanoparticles, suggesting excellent generalizability and applicability. We anticipate that our accurate and robust deep learning-based platform for NSCs differentiation identification will accelerate the progress of NSCs applications. The differentiation of neural stem cells (NSCs) into neurons is a critical part in devising potential cell-based therapeutic strategies for central nervous system diseases but NSCs fate determination and prediction is problematic. Here, the authors present a deep neural network model for predictable reliable identification of NSCs fate.
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              Iterative reconstruction of low-dose CT based on differential sparse

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                Author and article information

                Journal
                J Cancer
                J Cancer
                jca
                Journal of Cancer
                Ivyspring International Publisher (Sydney )
                1837-9664
                2024
                5 May 2024
                : 15
                : 11
                : 3539-3546
                Affiliations
                [1 ]Department of Thoracic Surgery, Linfen Central Hospital, Linfen, Shanxi, 041000, China.
                [2 ]Department of Cardiovascular Surgery, Xijing Hospital, Air Force Military Medical, University, Xincheng, Xi'an, 710032, China.
                [3 ]Department of medical, Xi'an Fourth Hospital, Xincheng, Xi'an, 710004, China.
                [4 ]Department of Cardiovascular Surgery, Xi'an Fourth Hospital, Xincheng, Xi'an, 710004, China.
                Author notes
                ✉ Corresponding author: Weiwei Zhi, Email: weiwei.zhi@ 123456aol.com .

                Competing Interests: The authors have declared that no competing interest exists.

                Article
                jcav15p3539
                10.7150/jca.91701
                11134448
                38817880
                4a6e89d0-aa00-4e8d-90f5-7e54acf02dfe
                © The author(s)

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.

                History
                : 30 October 2023
                : 13 April 2024
                Categories
                Research Paper

                Oncology & Radiotherapy
                anlotinib,etoposide,clinical trials,lung cancer
                Oncology & Radiotherapy
                anlotinib, etoposide, clinical trials, lung cancer

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