Detection of KRAS mutation using plasma samples in non-small-cell lung cancer: a systematic review and meta-analysis

BackgroundThe aim of this study was to investigate the diagnostic accuracy of KRAS mutation detection using plasma sample of patients with non-small cell lung cancer (NSCLC).MethodsDatabases of Pubmed, Embase, Cochrane Library, and Web of Science were searched for studies detecting KRAS mutation in paired tissue and plasma samples of patients with NSCLC. Data were extracted from each eligible study and analyzed using MetaDiSc and STATA.ResultsAfter database searching and screening of the studies with pre-defined criteria, 43 eligible studies were identified and relevant data were extracted. After pooling the accuracy data from 3341 patients, the pooled sensitivity, specificity and diagnostic odds ratio were 71%, 94%, and 59.28, respectively. Area under curve of summary receiver operating characteristic curve was 0.8883. Subgroup analysis revealed that next-generation sequencing outperformed PCR-based techniques in detecting KRAS mutation using plasma sample of patients with NSCLC, with sensitivity, specificity, and diagnostic odds ratio of 73%, 94%, and 82.60, respectively.ConclusionCompared to paired tumor tissue sample, plasma sample showed overall good performance in detecting KRAS mutation in patients with NSCLC, which could serve as good surrogate when tissue samples are not available

Accurately identifying hemagglutinin using sequence information and machine learning methods

IntroductionHemagglutinin (HA) is responsible for facilitating viral entry and infection by promoting the fusion between the host membrane and the virus. Given its significance in the process of influenza virus infestation, HA has garnered attention as a target for influenza drug and vaccine development. Thus, accurately identifying HA is crucial for the development of targeted vaccine drugs. However, the identification of HA using in-silico methods is still lacking. This study aims to design a computational model to identify HA.MethodsIn this study, a benchmark dataset comprising 106 HA and 106 non-HA sequences were obtained from UniProt. Various sequence-based features were used to formulate samples. By perform feature optimization and inputting them four kinds of machine learning methods, we constructed an integrated classifier model using the stacking algorithm.Results and discussionThe model achieved an accuracy of 95.85% and with an area under the receiver operating characteristic (ROC) curve of 0.9863 in the 5-fold cross-validation. In the independent test, the model exhibited an accuracy of 93.18% and with an area under the ROC curve of 0.9793. The code can be found from https://github.com/Zouxidan/HA_predict.git. The proposed model has excellent prediction performance. The model will provide convenience for biochemical scholars for the study of HA

Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings

Snake venom contains many toxic proteins that can destroy the circulatory system or nervous system of prey. Studies have found that these snake venom proteins have the potential to treat cardiovascular and nervous system diseases. Therefore, the study of snake venom protein is conducive to the development of related drugs. The research technologies based on traditional biochemistry can accurately identify these proteins, but the experimental cost is high and the time is long. Artificial intelligence technology provides a new means and strategy for large-scale screening of snake venom proteins from the perspective of computing. In this paper, we developed a sequence-based computational method to recognize snake toxin proteins. Specially, we utilized three different feature descriptors, namely g-gap, natural vector and word 2 vector, to encode snake toxin protein sequences. The analysis of variance (ANOVA), gradient-boost decision tree algorithm (GBDT) combined with incremental feature selection (IFS) were used to optimize the features, and then the optimized features were input into the deep learning model for model training. The results show that our model can achieve a prediction performance with an accuracy of 82.00% in 10-fold cross-validation. The model is further verified on independent data, and the accuracy rate reaches to 81.14%, which demonstrated that our model has excellent prediction performance and robustness

Benefits and risks of drug combination therapy for diabetes mellitus and its complications: a comprehensive review

Diabetes is a chronic metabolic disease, and its therapeutic goals focus on the effective management of blood glucose and various complications. Drug combination therapy has emerged as a comprehensive treatment approach for diabetes. An increasing number of studies have shown that, compared with monotherapy, combination therapy can bring significant clinical benefits while controlling blood glucose, weight, and blood pressure, as well as mitigating damage from certain complications and delaying their progression in diabetes, including both type 1 diabetes (T1D), type 2 diabetes (T2D) and related complications. This evidence provides strong support for the recommendation of combination therapy for diabetes and highlights the importance of combined treatment. In this review, we first provided a brief overview of the phenotype and pathogenesis of diabetes and discussed several conventional anti-diabetic medications currently used for the treatment of diabetes. We then reviewed several clinical trials and pre-clinical animal experiments on T1D, T2D, and their common complications to evaluate the efficacy and safety of different classes of drug combinations. In general, combination therapy plays a pivotal role in the management of diabetes. Integrating the effectiveness of multiple drugs enables more comprehensive and effective control of blood glucose without increasing the risk of hypoglycemia or other serious adverse events. However, specific treatment regimens should be tailored to individual patients and implemented under the guidance of healthcare professionals

Impact of lockdown on the growth of children in China aged 3-6 years during the COVID-19 pandemic

BackgroundLockdowns in COVID-19 pandemic led to less physical activity and more intake of unhealthy food in children. The aim of this study was to investigate the negative impact of major lockdowns on the growth of children aged 3-6 years during COVID-19 pandemic period.MethodsPhysical examination results in 2019 to 2022 from 5834 eligible children (2972 males and 2862 females) from Southwestern China who were 3 years old in 2019 were retrospectively collected. Height and weight data points were extracted from the results, and percentiles of height (height%), weight (weight%), and BMI (BMI%), and rates of overweight and obesity were calculated and compared between different years during the pandemic.ResultsAfter analyzing the 15404 growth data points from 5834 children, a slowly increasing trend of height% from 2019 to 2022 was observed. Weight%, BMI%, overweight rate, obesity rate, and combined overweight and obesity rate had two peaks in 2020 and 2022 when major lockdowns were adopted and a drop in between (year 2021), except for obesity rate which did not drop in 2021. Similar results were shown after stratification by gender.ConclusionThe lockdowns in COVID-19 pandemic promoted obesity of kindergarten children, but did not show any negative impact on their height growth possibly due to over-nutrition of children during lockdowns. More efforts need to be made to limit the increase of obesity rate in kindergarten children during possible future lockdowns

In Silico Screening Identifies a Novel Potential PARP1 Inhibitor Targeting Synthetic Lethality in Cancer Treatment

Synthetic lethality describes situations in which defects in two different genes or pathways together result in cell death. This concept has been applied to drug development for cancer treatment, as represented by Poly (ADP-ribose) polymerase (PARPs) inhibitors. In the current study, we performed a computational screening to discover new PARP inhibitors. Among the 11,247 compounds analyzed, one natural product, ZINC67913374, stood out by its superior performance in the simulation analyses. Compared with the FDA approved PARP1 inhibitor, olaparib, our results demonstrated that the ZINC67913374 compound achieved a better grid score (−86.8) and amber score (−51.42). Molecular dynamics simulations suggested that the PARP1-ZINC67913374 complex was more stable than olaparib. The binding free energy for ZINC67913374 was −177.28 kJ/mol while that of olaparib was −159.16 kJ/mol. These results indicated ZINC67913374 bound to PARP1 with a higher affinity, which suggest ZINC67913374 has promising potential for cancer drug development

The diagnostic accuracy of digital PCR, ARMS and NGS for detecting KRAS mutation in cell-free DNA of patients with colorectal cancer: A systematic review and meta-analysis.

IntroductionBefore anti-EGFR therapy is given to patients with colorectal cancer, it is required to determine KRAS mutation status in tumor. When tumor tissue is not available, cell-free DNA (liquid biopsy) is commonly used as an alternative. Due to the low abundance of tumor-derived DNA in cell-free DNA samples, methods with high sensitivity were preferred, including digital polymerase chain reaction, amplification refractory mutation system and next-generation sequencing. The aim of this systemic review and meta-analysis was to investigate the accuracy of those methods in detecting KRAS mutation in cell-free DNA sample from patients with colorectal cancer.MethodsLiterature search was performed in Pubmed, Embase, and Cochrane Library. After removing duplicates from the 170 publications found by literature search, eligible studies were identified using pre-defined criteria. Quality of the publications and relevant data were assessed and extracted thereafter. Meta-DiSc and STATA softwares were used to pool the accuracy parameters from the extracted data.ResultsA total of 33 eligible studies were identified for this systemic review and meta-analysis. After pooling, the overall sensitivity, specificity, and diagnostic odds ratio were 0.77 (95%CI: 0.74-0.79), 0.87 (95%CI: 0.85-0.89), and 23.96 (95%CI: 13.72-41.84), respectively. The overall positive and negative likelihood ratios were 5.55 (95%CI: 3.76-8.19) and 0.29 (95%CI: 0.21-0.38), respectively. Area under curve of the summarized ROC curve was 0.8992.ConclusionDigital polymerase chain reaction, amplification refractory mutation system, and next-generation sequencing had overall high accuracy in detecting KRAS mutation in cell-free DNA sample. Large prospective randomized clinical trials are needed to further convince the accuracy and usefulness of KRAS mutation detection using cfDNA/liquid biopsy samples in clinical practice.Trial registrationPROSPERO CRD42020176682; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=176682

Friction and Wear Mechanism of MoS2/C Composite Coatings Under Atmospheric Environment

Tribological properties of MoS2/C coatings with different carbon contents (44.7-84.3 at.%) deposited by magnetron sputtering were systematically investigated under atmospheric environment. During tribological tests, the coating with the least MoS2 content exhibited the lowest friction coefficient and wear rate, while coating with the most MoS2 showed the worst performance. To understand friction and wear mechanism, multiple analytical tools such as SEM, EDS, Raman, XPS and TEM were applied to investigate the composition and structure. TEM and SEM characteristics proved that the tribofilm with multilayered structure was formed on the tribopair. The C-rich layer adhered to the tribopair and the top layer was well-ordered MoS2 tribofilm, and the dominated amorphous MoS2 was found between the two layers. It suggested that the shear plane was mainly made of wellordered MoS2 transfer film, while carbon improved the mechanical properties of the coatings, served as a lubricant and also inhibited the oxidation of MoS2

The impact of COVID-19 lockdown on nursing higher education at Chengdu University.

BackgroundTo combat/control the COVID-19 pandemic, a complete lockdown was implemented in China for almost 6 months during 2020.PurposeTo determine the impact of a long-term lockdown on the academic performance of first-year nursing students via mandatory online learning, and to determine the benefits of online teaching.MethodsThe recruitment and academic performance of 1st-year nursing students were assessed between 2019 [prior to COVID-19, n = 195, (146 women)] and 2020 [during COVID-19, n = 180 (142 women)]. The independent sample t test or Mann-Whitney test was applied for a comparison between these two groups.ResultsThere was no significant difference in student recruitment between 2019 and 2020. The overall performance of the first-year students improved in the Biochemistry, Immunopathology, Traditional Chinese Medicine Nursing and Combined Nursing courses via mandatory online teaching in 2020 compared with traditional teaching in 2019.ConclusionSuspension of in-class learning but continuing education virtually online has occurred without negatively impacting academic performance, thus academic goals are more than achievable in a complete lockdown situation. This study offers firm evidence to forge a path for developments in teaching methods to better incorporate virtual learning and technology in order to adapt to fast-changing environments. However, the psychological/psychiatric and physical impact of the COVID-19 lockdown and the lack of face-to-face interaction on these students remains to be explored