Undetected Association Between Fatty Acids and Dementia with Lewy Bodies: A Bidirectional Two-Sample Mendelian Randomization Study.

Background: Although observational studies indicated connections between fatty acids (FAs) and Alzheimer's disease and dementia, uncertainty persists regarding how these relationships extend to dementia with Lewy bodies (DLB). Objective: To explore the potential causal relationships between FAs and the development of DLB, thus clarifying these associations using genetic instruments to infer causality. Methods: We applied a two-sample Mendelian randomization (MR) and multivariable Mendelian randomization (MVMR) approach. Genetic data were obtained from a DLB cohort, comprising 2,591 cases and 4,027 controls of European descent. Eight FAs, including linoleic acid, docosahexaenoic acid, monounsaturated fatty acid, omega-3 fatty acid, omega-6 fatty acid, polyunsaturated fatty acid, saturated fatty acid, and total fatty acid, were procured from a comprehensive GWAS of metabolic biomarkers of UK Biobank, conducted by Nightingale Health in 2020 (met-d), involving 114,999 individuals. Our analysis included inverse-variance weighted, MR-Egger, weighted-median, simple mode, and weighted-mode MR estimates. Cochran's Q-statistics, MR-PRESSO, and MR-Egger intercept test were used to quantify the heterogeneity and horizontal pleiotropy of instrumental variables. Results: Only linoleic acid showed a significant genetic association with the risk of developing DLB in the univariate MR. The odds ratio for linoleic acid was 1.337 with a 95% confidence interval of 1.019-1.756 (pIVW = 0.036). Results from the MVMR showed that no FAs were associated with the incidence of DLB. Conclusions: The results did not support the hypothesis that FAs could reduce the risk of developing DLB. However, elucidating the relationship between FAs and DLB risk holds potential implications for informing dietary recommendations and therapeutic approaches in DLB.

Simultaneous determination of unecritinib (TQ-B3101) and its active metabolite crizotinib in rat plasma by LC-MS/MS：An application to pharmacokinetic studies.

Unecritinib (TQ-B3101) is a selective tyrosine kinase receptor inhibitor. In the study, in vitro metabolic experiments revealed that the hydrolysis of TQ-B3101 was mainly catalyzed by carboxylesterase 2 (CES2), followed by CES1. Next, a sensitive and reliable LC-MS/MS method was established for the simultaneous determination of TQ-B3101 and its metabolite crizotinib in rat plasma. To prevent in vitro hydrolysis of TQ-B3101, sodium fluoride, the CESs inhibitor at a concentration of 2 M, was immediately added after whole blood collection. Plasma samples were extracted by acetonitrile-induced protein precipitation method, and chromatographically separated on a Gemini C18 column (50 mm × 2.0 mm i.d., 5 µm) using gradient elution with a mobile phase of 0.1% formic acid and 5 mmol/L ammonium acetate with 0.1% formic acid. The retention times for TQ-B3101 and crizotinib were 2.61 and 2.38 min, respectively. The analytes were detected with tandem mass spectrometer by positive electrospray ionization, using the ion transitions at m/z 492.3 → 302.3 for TQ-B3101, m/z 450.3 → 260.3 for crizotinib, and m/z 494.0 → 394.3 for imatinib (internal standard). Method validation was conducted in the linear range of 1.00-800 ng/mL for the two analytes. The precision, accuracy and stabilities all met the acceptance criteria. The pharmacokinetic study indicated that TQ-B3101 was rapidly hydrolyzed to crizotinib with the elimination half-life of 1.11 h after a single gavage administration of 27 mg/kg to Sprague-Dawley rats, and the plasma exposure of TQ-B3101 was only 2.98% of that of crizotinib.

Long-read sequencing unveils novel somatic variants and methylation patterns in the genetic information system of early lung cancer.

Lung cancer poses a global health challenge, necessitating advanced diagnostics for improved outcomes. Intensive efforts are ongoing to pinpoint early detection biomarkers, such as genomic variations and DNA methylation, to elevate diagnostic precision. We conducted long-read sequencing on cancerous and adjacent non-cancerous tissues from a patient with lung adenocarcinoma. We identified somatic structural variations (SVs) specific to lung cancer by integrating data from various SV calling methods and differentially methylated regions (DMRs) that were distinct between these two tissue samples, revealing a unique methylation pattern associated with lung cancer. This study discovered over 40,000 somatic SVs and over 180,000 DMRs linked to lung cancer. We identified approximately 700 genes of significant relevance through comprehensive analysis, including genes intricately associated with many lung cancers, such as NOTCH1, SMOC2, CSMD2, and others. Furthermore, we observed that somatic SVs and DMRs were substantially enriched in several pathways, such as axon guidance signaling pathways, which suggests a comprehensive multi-omics impact on lung cancer progression across various biological investigation levels. These datasets can potentially serve as biomarkers for early lung cancer detection and may hold significant value in clinical diagnosis and treatment applications.

Prediction of SARS-CoV-2 Infection Phosphorylation Sites and Associations of these Modifications with Lung Cancer Development.

INTRODUCTION: Since the emergence of SARS-CoV-2 viruses, multiple mutant strains have been identified. Infection with SARS-CoV-2 virus leads to alterations in host cell phosphorylation signal, which systematically modulates the immune response. METHOD: Identification and analysis of SARS-CoV-2 virus infection phosphorylation sites enable insight into the mechanisms of viral infection and effects on host cells, providing important fundamental data for the study and development of potent drugs for the treatment of immune inflammatory diseases. In this paper, we have analyzed the SARS-CoV-2 virus-infected phosphorylation region and developed a transformer-based deep learning-assisted identification method for the specific identification of phosphorylation sites in SARS-CoV-2 virus-infected host cells. RESULT: Furthermore, through association analysis with lung cancer, we found that SARS-CoV-2 infection may affect the regulatory role of the immune system, leading to an abnormal increase or decrease in the immune inflammatory response, which may be associated with the development and progression of cancer. CONCLUSION: We anticipate that this study will provide an important reference for SARS-CoV-2 virus evolution as well as immune-related studies and provide a reliable complementary screening tool for anti-SARS-CoV-2 virus drug and vaccine design.

In vivo and in vitro evaluation of pulmonary administration of itraconazole nanostructured lipid carriers for pulmonary aspergillosis.

OBJECTIVE: Pulmonary aspergillosis, which is a secondary complication of fungal pneumonia, is widely considered to have an increasing incidence and high mortality. Itraconazole (Itz) can inhibit ergosterol biosynthesis to treat pulmonary aspergillosis. Nevereless, Itz's clinical application is limited because of its poor water solubility, low oral bioavailability, and systemic hepatotoxicity. In this study, Itz-loaded nanostructured lipid carriers (Itz-NLCs) were developed to improve the in vitro permeability and bioavailability of Itz via pulmonary administration. METHODS: Itz-NLCs were prepared by the emulsification-evaporation method using oleic acid and glycerol monostearate as liquid and solid lipids, respectively. RESULTS: The Itz-NLCs were optimized with tiny particle size, uniform distribution, and excellent entrapment efficiency (EE, 97.57% ± 0.45%). A Xenopus alveolar membrane was used in the permeation study, and the cumulative permeation percentage of Itz was 10% for Itz-NLCs at 8 h, which was 2.50-fold higher than that for Itz suspensions (4%, p < .001). A rabbit pharmacokinetic investigation revealed that Itz-NLCs have an 83.05% absolute bioavailability after intratracheal instillation. CONCLUSIONS: The purpose of Itz-NLCs is to enhance the bioavailability and permeability of Itz in vitro for administration via the lungs.

Metastasis-related gene identification by compound constrained NMF and a semisupervised cluster approach using pancancer multiomics features.

In recent years, with the gradual increase in pancancer-related research, more attention has been given to the field of pancancer metastasis. However, the molecular mechanism of pancancer metastasis is very unclear, and identification methods for pancancer metastasis-related genes are still lacking. In view of this research status, we developed a novel pipeline to identify pancancer metastasis-related genes based on compound constrained nonnegative matrix factorization (CCNMF). To solve the above problems, the following modules were designed. A correntropy operator and feature similarity fusion (FSF) were first adopted to process the multiomics features of genes; thus, the influences caused by irrelevant biomolecular patterns, manifested as non-Gaussian noise, were minimized. CCNMF was then adopted to handle the above features with compound constraints consisting of a gene relation network and a "metastasis-related" gene set, which maximizes the biological interpretability of the metafeatures generated by NMF. Since a negative set of pancancer "metastasis-related" genes could hardly be obtained, semisupervised analyses were performed on gene features acquired by each step in our pipeline to examine our method's effect. 83% of the 236 candidates identified by the above method were associated with the metastasis of one or more cancers, 71.9% candidates were identified immune-related in pancancer in addition to the hallmark genes. Our study provides an effective and interpretable method for identifying metastasis-related as well as immune-related genes, and the method is successfully applied to TCGA pancancer data.

Applicability analysis of immunotherapy for lung cancer patients based on deep learning.

According to global and Chinese cancer statistics, lung cancer is the second most common cancer globally with the highest mortality rate and a severe threat to human life and health. In recent years, immunotherapy has made significant breakthroughs in the treatment of cancer patients. However, only 30% of patients are applicable and may have immune-related adverse events. The traditional immunological inspection methods have limitations and often can not obtain the expected benefits. Deep learning is a typical representation learning method that can spontaneously mine the hidden feature of effective classification from seas of data. In order to alleviate medical resources and reduce costs, this paper proposes a deep learning-based method to predict patients best suited for immune checkpoint blocking therapy from patients CT images. The deep immunotherapy analysis method proposed in this paper is divided into three steps:(1) Using LUNA16 public dataset to develop a deep learning model for nodule detection. (2) Nodule detection was performed on the Anti-PD-1_Lung dataset, and the effectiveness of immunotherapy was determined by comparing the detection results of nodules before and after immunotherapy. (3) After the data set was processed, the deep learning method trained and analyzed the Lung images. According to the experimental results and comparative analysis, the proposed deep immunotherapy analysis method has a good performance in the detection of nodules. It works for the predictions for the applicability of immunotherapy for lung cancer.1.

Exploration of Lung Cancer-Related Genetic Factors via Mendelian Randomization Method Based on Genomic and Transcriptomic Summarized Data.

Lung carcinoma is one of the most deadly malignant tumors in mankind. With the rising incidence of lung cancer, searching for the high effective cures become more and more imperative. There has been sufficient research evidence that living habits and situations such as smoking and air pollution are associated with an increased risk of lung cancer. Simultaneously, the influence of individual genetic susceptibility on lung carcinoma morbidity has been confirmed, and a growing body of evidence has been accumulated on the relationship between various risk factors and the risk of different pathological types of lung cancer. Additionally, the analyses from many large-scale cancer registries have shown a degree of familial aggregation of lung cancer. To explore lung cancer-related genetic factors, Genome-Wide Association Studies (GWAS) have been used to identify several lung cancer susceptibility sites and have been widely validated. However, the biological mechanism behind the impact of these site mutations on lung cancer remains unclear. Therefore, this study applied the Summary data-based Mendelian Randomization (SMR) model through the integration of two GWAS datasets and four expression Quantitative Trait Loci (eQTL) datasets to identify susceptibility genes. Using this strategy, we found ten of Single Nucleotide Polymorphisms (SNPs) sites that affect the occurrence and development of lung tumors by regulating the expression of seven genes. Further analysis of the signaling pathway about these genes not only provides important clues to explain the pathogenesis of lung cancer but also has critical significance for the diagnosis and treatment of lung cancer.

Identification of Parkinson's Disease-Causing Genes via Omics Data.

Parkinson's disease (PD) is the second most frequent neurogenic disease after Alzheimer's disease. The clinical manifestations include mostly motor disorders, such as bradykinesia, myotonia, and static tremors. Since the cause of this pathological features remain unclear, there is currently no radical treatment for PD. Environmental and genetic factors are thought to contribute to the pathology of PD. To identify the genetic factors, some studies employed the Genome-Wide Association Studies (GWAS) method and detected certain genes closely related to PD. However, the functions of these gene mutants in the development of PD are unknown. Combining GWAS and expression Quantitative Trait Loci (eQTL) analysis, the biological meaning of mutation could be explained to some extent. Therefore, the present investigation used Summary data-based Mendelian Randomization (SMR) analysis to integrate of two PD GWAS datasets and four eQTL datasets with the objective of identifying casual genes. Using this strategy, we found six Single Nucleotide Polymorphism (SNP) loci which could cause the development of PD through altering the susceptibility gene expression, and three risk genes: Synuclein Alpha (SNCA), Mitochondrial Poly(A) Polymerase (MTPAP), and RP11-305E6.4. We proved the accuracy of results through case studies and inferred the functions of these genes in PD. Overall, this study provides insights into the genetic mechanism behind PD, which is crucial for the study of the development of this disease and its diagnosis and treatment.

Pan-Cancer Metastasis Prediction Based on Graph Deep Learning Method.

Tumor metastasis is the major cause of mortality from cancer. From this perspective, detecting cancer gene expression and transcriptome changes is important for exploring tumor metastasis molecular mechanisms and cellular events. Precisely estimating a patient's cancer state and prognosis is the key challenge to develop a patient's therapeutic schedule. In the recent years, a variety of machine learning techniques widely contributed to analyzing real-world gene expression data and predicting tumor outcomes. In this area, data mining and machine learning techniques have widely contributed to gene expression data analysis by supplying computational models to support decision-making on real-world data. Nevertheless, limitation of real-world data extremely restricted model predictive performance, and the complexity of data makes it difficult to extract vital features. Besides these, the efficacy of standard machine learning pipelines is far from being satisfactory despite the fact that diverse feature selection strategy had been applied. To address these problems, we developed directed relation-graph convolutional network to provide an advanced feature extraction strategy. We first constructed gene regulation network and extracted gene expression features based on relational graph convolutional network method. The high-dimensional features of each sample were regarded as an image pixel, and convolutional neural network was implemented to predict the risk of metastasis for each patient. Ten cross-validations on 1,779 cases from The Cancer Genome Atlas show that our model's performance (area under the curve, AUC = 0.837; area under precision recall curve, AUPRC = 0.717) outstands that of an existing network-based method (AUC = 0.707, AUPRC = 0.555).

Using digital images of the zebra finch song system as a tool to teach organizational effects of steroid hormones: a free downloadable module.

Zebra finch song behavior is sexually dimorphic: males sing and females do not. The neural system underlying this behavior is sexually dimorphic, and this sex difference is easy to quantify. During development, the zebra finch song system can be altered by steroid hormones, specifically estradiol, which actually masculinizes it. Because of the ease of quantification and experimental manipulation, the zebra finch song system has great potential for use in undergraduate labs. Unfortunately, the underlying costs prohibit use of this system in undergraduate labs. Further, the time required to perform a developmental study renders such undertakings unrealistic within a single academic term. We have overcome these barriers by creating digital tools, including an image library of song nuclei from zebra finch brains. Students using this library replicate and extend a published experiment examining the dose of estradiol required to masculinize the female zebra finch brain. We have used this library for several terms, and students not only obtain significant experimental results but also make gains in understanding content, experimental controls, and inferential statistics (analysis of variance and post hoc tests). We have provided free access to these digital tools at the following website: http://mdcune.psych.ucla.edu/modules/birdsong.