Comparative efficiency and safety of insulin degludec/aspart with insulin glargine in type 2 diabetes: a meta-analysis of randomized controlled trials.

Recent studies have found compared with insulin glargine (IGlar), insulin degludec/aspart (IDeg/Asp) may provide adequate glycemic control and prevent hypoglycemia events in type 2 diabetes mellitus (T2DM). Consequently, we performed a meta-analysis to appraise and compare the efficiency and safety of IDeg/Asp and IGlar in the treatment of T2DM. We sought the databases including PubMed, Embase, Scopus, Cochrane library to confirm related articles which inspected the effect of IDeg/Asp versus IGlar for the treatment of T2DM until May 2021. Finally, six randomized controlled trials (RCTs) of 1,346 patients were included. The results showed that IDeg/Asp significantly decreased the mean hemoglobin A1c (HbA1c) level but was prone to serious adverse events, and IGlar increased the nocturnal confirmed hypoglycemia events. Besides, there were no significant changes in other indicators, including mean fasting plasma glucose (FPG) level, nine-point self-measured plasma glucose (SMPG) level, and adverse events. What's more, we found that there was no significant difference in the occurrence of hypoglycemia overall, but our subgroup analysis of confirmed hypoglycemia revealed the population in this subgroup (duration of diabetes ≤11 years) might has its particularity effecting the hypoglycemia outcome. Concerning efficiency, IDeg/Asp may have advantages in controlling the mean HbA1c level. Regarding safety, IGlar might increase the risk of nocturnal confirmed hypoglycemia. Further evidence is needed to compare better the efficiency and safety of IDeg/Asp versus IGlar therapy.

LY294002 Is a Promising Inhibitor to Overcome Sorafenib Resistance in FLT3-ITD Mutant AML Cells by Interfering With PI3K/Akt Signaling Pathway.

Internal tandem duplications (ITD) mutation within FMS-like tyrosine kinase 3 (FLT3), the most frequent mutation happens in almost 20% acute myeloid leukemia (AML) patients, always predicts a poor prognosis. As a small molecule tyrosine kinase inhibitor, sorafenib is clinically used for the treatment of advanced renal cell carcinoma (RCC), hepatocellular carcinoma (HCC), and differentiated thyroid cancer (DTC), with its preclinical and clinical activity demonstrated in the treatment of Fms-like tyrosine kinase 3-internal tandem duplication (FLT3-ITD) mutant AML. Even though it shows a rosy future in the AML treatment, the short response duration remains a vital problem that leads to treatment failure. Rapid onset of drug resistance is still a thorny problem that we cannot overlook. Although the mechanisms of drug resistance have been studied extensively in the past years, there is still no consensus on the exact reason for resistance and without effective therapeutic regimens established clinically. My previous work reported that sorafenib-resistant FLT3-ITD mutant AML cells displayed mitochondria dysfunction, which rendered cells depending on glycolysis for energy supply. In my present one, we further illustrated that losing the target protein FLT3 and the continuously activated PI3K/Akt signaling pathway may be the reason for drug resistance, with sustained activation of PI3K/AKT signaling responsible for the highly glycolytic activity and adenosine triphosphate (ATP) generation. PI3K inhibitor, LY294002, can block PI3K/AKT signaling, further inhibit glycolysis to disturb ATP production, and finally induce cell apoptosis. This finding would pave the way to remedy the FLT3-ITD mutant AML patients who failed with FLT3 targeted therapy.

Precise resection of multiple pulmonary nodules using a three-dimensional reconstruction model: A case report.

A 48-year-old woman presented to our department and chest computed tomography (CT) revealed five pulmonary nodules, two of which were in the left upper lobe of the lung and three in the superior segment of the left lower lobe., All the lesions were resected for comprehensive histological assessment in order to distinguish synchronous multiple primary lung cancers (SMPLCs) from intrapulmonary metastases. The nodules were all successfully removed by minimally invasive surgery under the guidance of three dimensional (3D) reconstruction, in order to preserve as much lung function for the patient as possible. Postoperative histopathological examination demonstrated the presence of SMPLC. The patient was discharged from hospital on postoperative day 4 without any complications.

2019 Novel Coronavirus-Infected Pneumonia on CT: A Feasibility Study of Few-Shot Learning for Computerized Diagnosis of Emergency Diseases.

COVID-19 is an emerging disease with transmissibility and severity. So far, there are no effective therapeutic drugs or vaccines for COVID-19. The most serious complication of COVID-19 is a type of pneumonia called 2019 novel coronavirus-infected pneumonia (NCIP) with about 4.3% mortality rate. Comparing to chest Digital Radiography (DR), it is recently reported that chest Computed Tomography (CT) is more useful to serve as the early screening and diagnosis tool for NCIP. In this study, aimed to help physicians make the diagnostic decision, we develop a machine learning (ML) approach for automated diagnosis of NCIP on chest CT. Different from most ML approaches which often require training on thousands or millions of samples, we design a few-shot learning approach, in which we combine few-shot learning with weakly supervised model training, for computerized NCIP diagnosis. A total of 824 patients are retrospectively collected from two Hospitals with IRB approval. We first use 9 patients with clinically confirmed NCIP and 20 patients without known lung diseases for training a location detector which is a multitask deep convolutional neural network (DCNN) designed to output a probability of NCIP and the segmentation of targeted lesion area. An experienced radiologist manually localizes the potential locations of NCIPs on chest CTs of 9 COVID-19 patients and interactively segments the area of the NCIP lesions as the reference standard. Then, the multitask DCNN is furtherly fine-tuned by a weakly supervised learning scheme with 291 case-level labeled samples without lesion labels. A test set of 293 patients is independently collected for evaluation. With our NCIP-Net, the test AUC is 0.91. Our system has potential to serve as the NCIP screening and diagnosis tools for the fight of COVID-19's endemic and pandemic.