GSK3: A potential target and pending issues for treatment of Alzheimer's disease.

Glycogen synthase kinase-3 (GSK3), consisting of GSK3α and GSK3ß subtypes, is a complex protein kinase that regulates numerous substrates. Research has observed increased GSK3 expression in the brains of Alzheimer's disease (AD) patients and models. AD is a neurodegenerative disorder with diverse pathogenesis and notable cognitive impairments, characterized by Aß aggregation and excessive tau phosphorylation. This article provides an overview of GSK3's structure and regulation, extensively analyzing its relationship with AD factors. GSK3 overactivation disrupts neural growth, development, and function. It directly promotes tau phosphorylation, regulates amyloid precursor protein (APP) cleavage, leading to Aß formation, and directly or indirectly triggers neuroinflammation and oxidative damage. We also summarize preclinical research highlighting the inhibition of GSK3 activity as a primary therapeutic approach for AD. Finally, pending issues like the lack of highly specific and affinity-driven GSK3 inhibitors, are raised and expected to be addressed in future research. In conclusion, GSK3 represents a target in AD treatment, filled with hope, challenges, opportunities, and obstacles.

HTR1B regulates mitochondrial homeostasis and mitophagy by activating the ERK/ MAPK signalling pathway during human embryonic arrest.

Background: Previous studies have shown that serotonin and its receptors are widely distributed in mammalian reproductive tisssues and play an important role in embryonic development. However, the specific effects of the serotonergic system on embryonic arrest (EA) and the underlying mechanism require further investigation. Methods: Chorionic villi were collected from patients with EA and healthy pregnant women. Western blotting (WB) and immunohistochemistry (IHC) were used to detect serotonin receptor 1B (HTR1B) levels and evaluate mitochondrial function. Additionally, HTR-8/SVneo cells were transfected with an HTR1B overexpression plasmid. Quantitative real-time polymerase chain reaction(qRT-PCR), Cell Counting Kit-8 (CCK-8), and wound healing assays were utilized to evaluate mitophagy level, cell proliferation and cell migration, respectively. Results: We discovered elevated HTR1B levels in the chorionic villi of the patients with EA compared to controls. Concurrently, we observed enhanced levels of nucleus-encoded proteins including mitofilin, succinate dehydrogenase complex subunit A (SDHA), and cytochrome c oxidase subunit 4 (COXIV), along with the mitochondrial fusion protein optic atrophy 1(OPA1), fission proteins mitochondrial fission protein 1(FIS1) and mitochondrial fission factor (MFF) in the EA group. Additionally, there was an excessive mitophagy levels in EA group. Furthermore, a notable activation of mitogen-activated protein kinase (MAPK) signaling pathway proteins including extracellular regulating kinase (ERK), c-Jun N-terminal kinase (JNK), and P38 was observed in the EA group. By overexpressing HTR1B in HTR-8/SVneo cells, we observed a significant reduction in cell proliferation and migration. HTR1B overexpression also caused an increase in levels of SDHA and FIS1, as well as an upregulation of mitophagy. Notably, the ERK inhibitor U0126 effectively mitigated these effects. Conclusion: These findings show that HTR1B influences mitochondrial homeostasis, promoting excessive mitophagy and impairing cell proliferation and migration by activating the MAPK signalling pathway during post-implantation EA. Therefore, HTR1B may serve as a potential therapeutic target for patients with EA.

Dendritic Cell-Hitchhiking In Vivo for Vaccine Delivery to Lymph Nodes.

Therapeutic cancer vaccines are among the first FDA-approved cancer immunotherapies. Among them, it remains a major challenge to achieve robust lymph-node (LN) accumulation. However, delivering cargo into LN is difficult owing to the unique structure of the lymphatics, and clinical responses have been largely disappointing. Herein, inspired by the Migrated-DCs homing from the periphery to the LNs, an injectable hydrogel-based polypeptide vaccine system is described for enhancing immunostimulatory efficacy, which could form a local niche of vaccine "hitchhiking" on DCs. The OVA peptide modified by lipophilic DSPE domains in the hydrogel is spontaneously inserted into the cell membrane to achieve "antigen anchoring" on DCs in vivo. Overall, OVA peptide achieves active access LNs through recruiting and "hitchhiking" subcutaneous Migrated-DCs. Remarkably, it is demonstrated that the composite hydrogel enhances LNs targeting efficacy by approximately six-fold compared to free OVA peptide. Then, OVA peptide can be removed from the cell surface under a typical acidic microenvironment within the LNs, further share them with LN-resident APCs via the "One-to-Many" strategy (One Migrated-DC corresponding to Many LN-resident APCs), thereby activating powerful immune stimulation. Moreover, the hydrogel vaccine exhibits significant tumor growth inhibition in melanoma and inhibits pulmonary metastatic nodule formation.

Genome-wide chromatin accessibility reveals transcriptional regulation of heterosis in inter-subspecific hybrid rice.

The utilization of rice heterosis is essential for ensuring global food security; however, its molecular mechanism remains unclear. In this study, comprehensive analyses of accessible chromatin regions (ACRs), DNA methylation, and gene expression in inter-subspecific hybrid and its parents were performed to determine the potential role of chromatin accessibility in rice heterosis. The hybrid exhibited abundant ACRs, in which the gene ACRs and proximal ACRs were directly related to transcriptional activation rather than the distal ACRs. Regarding the dynamic accessibility contribution of the parents, paternal ZHF1015 transmitted a greater number of ACRs to the hybrid. Accessible genotype-specific target genes were enriched with overrepresented transcription factors, indicating a unique regulatory network of genes in the hybrid. Compared with its parents, the differentially accessible chromatin regions with upregulated chromatin accessibility were much greater than those with downregulated chromatin accessibility, reflecting a stronger regulation in the hybrid. Furthermore, DNA methylation levels were negatively correlated with ACR intensity, and genes were strongly affected by CHH methylation in the hybrid. Chromatin accessibility positively regulated the overall expression level of each genotype. ACR-related genes with maternal Z04A-bias allele-specific expression tended to be enriched during carotenoid biosynthesis, whereas paternal ZHF1015-bias genes were more active in carbohydrate metabolism. Our findings provide a new perspective on the mechanism of heterosis based on chromatin accessibility in inter-subspecific hybrid rice.

Plasma proteometabolome in lung cancer: exploring biomarkers through bidirectional Mendelian randomization and colocalization analysis.

Unlike other cancers with widespread screening (breast, colorectal, cervical, prostate, and skin), lung nodule biopsies for positive screenings have higher morbidity with clinical complications. Development of non-invasive diagnostic biomarkers could thereby significantly enhance lung cancer management for at-risk patients. Here, we leverage Mendelian Randomization (MR) to investigate the plasma proteome and metabolome for potential biomarkers relevant to lung cancer. Utilizing bidirectional MR and co-localization analyses, we identify novel associations, highlighting inverse relationships between plasma proteins SFTPB and KDELC2 in lung adenocarcinoma (LUAD) and positive associations of TCL1A with lung squamous cell carcinoma (LUSC) and CNTN1 with small cell lung cancer (SCLC). Additionally, our work reveals significant negative correlations between metabolites such as theobromine and paraxanthine, along with paraxanthine-related ratios, in both LUAD and LUSC. Conversely, positive correlations are found in caffeine/paraxanthine and arachidonate (20:4n6)/paraxanthine ratios with these cancer types. Through single-cell sequencing data of normal lung tissue, we further explore the role of lung tissue-specific protein SFTPB in carcinogenesis. These findings offer new insights into lung cancer etiology, potentially guiding the development of diagnostic biomarkers and therapeutic approaches.

Effect of Different Fertilizer Types on Quality of Foxtail Millet under Low Nitrogen Conditions.

In order to clarify the effect of different fertilizers on foxtail millet quality under low nitrogen conditions, we used JGNo.21 and LZGNo.2 as experimental materials and set up five treatments, including non-fertilization, nitrogen, phosphorus, compound, and organic fertilizers, to study the regulation of different fertilizer types on agronomic traits, nutrient fractions, and pasting characteristics of foxtail millet under low nitrogen conditions. Compared with the control, all of the fertilizers improved the agronomic traits of JGNo.21 to a certain extent. Nitrogen and compound fertilizer treatments reduced the starch content of JGNo.21; the starch content was reduced by 0.55% and 0.07% under nitrogen and compound fertilizers treatments. Phosphorus and organic fertilizers increased starch content, and starch content increased by 0.50% and 0.56% under phosphorus and organic fertilizer treatments. The effect of each fertilizer treatment on protein content was completely opposite to that of starch; different fertilizer treatments reduced the fat content of JGNo.21 and increased the fiber content. Among them, nitrogen and phosphorus fertilizers increased the yellow pigment content; the yellow pigment content increased by 1.21% and 2.64% under nitrogen and phosphorus fertilizer treatments. Organic and compound fertilizers reduced the content of yellow pigment; the yellow pigment content was reduced by 3.36% and 2.79% under organic and compound fertilizer treatments. Nitrogen and organic fertilizers increased the fat content of LZGNo.2; the fat content increased by 2.62% and 1.98% under nitrogen, organic fertilizer treatment. Compound and phosphorus fertilizer decreased the fat content; the fat content decreased by 2.16% and 2.90% under compound and phosphorus fertilizer treatment. Different fertilizer treatments reduced the cellulose and yellow pigment content of LZGNo.2. The content of essential, non-essential, and total amino acids of JGNo.21 was increased under compound and nitrogen fertilizer treatments and decreased under organic and phosphorus fertilizer treatments. The content of essential, non-essential, and total amino acids of LZGNo.2 was significantly higher under compound, nitrogen, and organic fertilizer treatments compared with control and significantly decreased under phosphorus fertilizer treatments. Nitrogen and compound fertilizer treatments significantly reduced the values of peak viscosity, trough viscosity, breakdown viscosity, final viscosity, setback viscosity, and pasting time of each index of JGNo.21; phosphorus and organic fertilizer treatments improved the values of each index. In contrast, the pasting viscosity of LZGNo.2 increased under phosphorus fertilizer treatment and decreased under nitrogen fertilizer treatment. Reasonable fertilization can improve the quality of foxtail millet, which provides a scientific theoretical basis for improving the quality of foxtail millet.

Flexible wearable sensors for crop monitoring: a review.

Crops were the main source of human food, which have met the increasingly diversified demand of consumers. Sensors were used to monitor crop phenotypes and environmental information in real time, which will provide a theoretical reference for optimizing crop growth environment, resisting biotic and abiotic stresses, and improve crop yield. Compared with non-contact monitoring methods such as optical imaging and remote sensing, wearable sensing technology had higher time and spatial resolution. However, the existing crop sensors were mainly rigid mechanical structures, which were easy to cause damage to crop organs, and there were still challenges in terms of accuracy and biosafety. Emerging flexible sensors had attracted wide attention in the field of crop phenotype monitoring due to their excellent mechanical properties and biocompatibility. The article introduced the key technologies involved in the preparation of flexible wearable sensors from the aspects of flexible preparation materials and advanced preparation processes. The monitoring function of flexible sensors in crop growth was highlighted, including the monitoring of crop nutrient, physiological, ecological and growth environment information. The monitoring principle, performance together with pros and cons of each sensor were analyzed. Furthermore, the future opportunities and challenges of flexible wearable devices in crop monitoring were discussed in detail from the aspects of new sensing theory, sensing materials, sensing structures, wireless power supply technology and agricultural sensor network, which will provide reference for smart agricultural management system based on crop flexible sensors, and realize efficient management of agricultural production and resources.

Exploring potential neuroimaging biomarkers for the response to non-steroidal anti-inflammatory drugs in episodic migraine.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are considered first-line medications for acute migraine attacks. However, the response exhibits considerable variability among individuals. Thus, this study aimed to explore a machine learning model based on the percentage of amplitude oscillations (PerAF) and gray matter volume (GMV) to predict the response to NSAIDs in migraine treatment. METHODS: Propensity score matching was adopted to match patients having migraine with response and nonresponse to NSAIDs, ensuring consistency in clinical characteristics and migraine-related features. Multimodal magnetic resonance imaging was employed to extract PerAF and GMV, followed by feature selection using the least absolute shrinkage and selection operator regression and recursive feature elimination algorithms. Multiple predictive models were constructed and the final model with the smallest predictive residuals was chosen. The model performance was evaluated using the area under the receiver operating characteristic (ROCAUC) curve, area under the precision-recall curve (PRAUC), balance accuracy (BACC), sensitivity, F1 score, positive predictive value (PPV), and negative predictive value (NPV). External validation was performed using a public database. Then, correlation analysis was performed between the neuroimaging predictors and clinical features in migraine. RESULTS: One hundred eighteen patients with migraine (59 responders and 59 non-responders) were enrolled. Six features (PerAF of left insula and left transverse temporal gyrus; and GMV of right superior frontal gyrus, left postcentral gyrus, right postcentral gyrus, and left precuneus) were observed. The random forest model with the lowest predictive residuals was selected and model metrics (ROCAUC, PRAUC, BACC, sensitivity, F1 score, PPV, and NPV) in the training and testing groups were 0.982, 0.983, 0.927, 0.976, 0.930, 0.889, and 0.973; and 0.711, 0.648, 0.639, 0.667,0.649, 0.632, and 0.647, respectively. The model metrics of external validation were 0.631, 0.651, 0.611, 0.808, 0.656, 0.553, and 0.706. Additionally, a significant positive correlation was found between the GMV of the left precuneus and attack time in non-responders. CONCLUSIONS: Our findings suggest the potential of multimodal neuroimaging features in predicting the efficacy of NSAIDs in migraine treatment and provide novel insights into the neural mechanisms underlying migraine and its optimized treatment strategy.

BiU-net: A dual-branch structure based on two-stage fusion strategy for biomedical image segmentation.

BACKGROUND AND OBJECTIVE: Computer-based biomedical image segmentation plays a crucial role in planning of assisted diagnostics and therapy. However, due to the variable size and irregular shape of the segmentation target, it is still a challenge to construct an effective medical image segmentation structure. Recently, hybrid architectures based on convolutional neural networks (CNNs) and transformers were proposed. However, most current backbones directly replace one or all convolutional layers with transformer blocks, regardless of the semantic gap between features. Thus, how to sufficiently and effectively eliminate the semantic gap as well as combine the global and local information is a critical challenge. METHODS: To address the challenge, we propose a novel structure, called BiU-Net, which integrates CNNs and transformers with a two-stage fusion strategy. In the first fusion stage, called Single-Scale Fusion (SSF) stage, the encoding layers of the CNNs and transformers are coupled, with both having the same feature map size. The SSF stage aims to reconstruct local features based on CNNs and long-range information based on transformers in each encoding block. In the second stage, Multi-Scale Fusion (MSF), BiU-Net interacts with multi-scale features from various encoding layers to eliminate the semantic gap between deep and shallow layers. Furthermore, a Context-Aware Block (CAB) is embedded in the bottleneck to reinforce multi-scale features in the decoder. RESULTS: Experiments on four public datasets were conducted. On the BUSI dataset, our BiU-Net achieved 85.50 % on Dice coefficient (Dice), 76.73 % on intersection over union (IoU), and 97.23 % on accuracy (ACC). Compared to the state-of-the-art method, BiU-Net improves Dice by 1.17 %. For the Monuseg dataset, the proposed method attained the highest scores, reaching 80.27 % and 67.22 % for Dice and IoU. The BiU-Net achieves 95.33 % and 81.22 % Dice on the PH2 and DRIVE datasets. CONCLUSIONS: The results of our experiments showed that BiU-Net transcends existing state-of-the-art methods on four publicly available biomedical datasets. Due to the powerful multi-scale feature extraction ability, our proposed BiU-Net is a versatile medical image segmentation framework for various types of medical images. The source code is released on (https://github.com/ZYLandy/BiU-Net).

Association analysis between an epigenetic risk score and blood pressure.

Background: Epigenome-wide association studies have revealed multiple DNA methylation sites (CpGs) associated with alcohol consumption, an important lifestyle risk factor for cardiovascular diseases. Results: We generated an alcohol consumption epigenetic risk score (ERS) based on previously reported 144 alcohol-associated CpGs and examined the association of the ERS with systolic blood pressure (SBP), diastolic blood pressure (DBP), and hypertension (HTN) in 3,898 Framingham Heart Study (FHS) participants. We found an association of alcohol intake with the ERS in the meta-analysis with 0.09 units higher ERS per drink consumed per day (p < 0.0001). Cross-sectional analyses in FHS revealed that a one-unit increment of the ERS was associated with 1.93 mm Hg higher SBP (p = 4.64E-07), 0.68 mm Hg higher DBP (p = 0.006), and an odds ratio of 1.78 for HTN (p < 2E-16). Meta-analysis of the cross-sectional association of the ERS with BP traits in eight independent external cohorts (n = 11,544) showed similar relationships with blood pressure levels, i.e., a one-unit increase in ERS was associated with 0.74 (p = 0.002) and 0.50 (p = 0.0006) mm Hg higher SBP and DBP, but could not confirm the association with hypertension. Longitudinal analyses in FHS (n = 3,260) and five independent external cohorts (n = 4,021) showed that the baseline ERS was not associated with a change in blood pressure over time or with incident HTN. Conclusions: Our findings provide proof-of-concept that utilizing an ERS is a useful approach to capture the recent health consequences of lifestyle behaviors such as alcohol consumption.

Metabolic profile of follicular fluid in patients with ovarian endometriosis undergoing IVF: a pilot study.

RESEARCH QUESTION: What are the metabolic characteristics of follicular fluid in patients with ovarian endometriosis undergoing IVF? DESIGN: This was an exploratory cohort study on endometriosis. In total, 19 infertile patients with ovarian endometriosis diagnosed by laparoscopy, and 23 controls matched in terms of age and body mass index (women with infertility due to male or tubal factors) were enrolled in this study. All patients underwent IVF treatment with a gonadotrophin-releasing hormone antagonist protocol, and follicular fluid was collected at oocyte retrieval. The metabolomics of follicular fluid samples was analysed using an ultra-high-performance liquid chromatography Orbitrap Exploris mass spectrometer (UHPLC-OE-MS). The best combination of biomarkers was selected by performing stepwise logistic regression analysis with backward elimination. RESULTS: Fifteen metabolites were identified as biomarkers associated with endometriosis. A final model containing 8-hydroxy-2-deoxyguanosine, biotin, n-acetyl-L-methionine and n-methylnicotinamide was constructed. Receiver operating characteristic analysis confirmed the value of these parameters in diagnosing endometriosis, with sensitivity of 94.7% and specificity of 95.7%. Enrichment analysis via the Kyoto Encyclopedia of Genes and Genome showed that 15 metabolites were enriched in eight metabolic pathways. CONCLUSION: Metabolomics based on UHPLC-OE-MS effectively characterized the metabolomics analysis of follicular fluid in patients with ovarian endometriosis. These findings may provide a new basis for better understanding of how diseases progress, and for the discovery of new biomarkers.

Identification of immunogenic cell death-related damage-related molecular patterns (DAMPs) to predict outcomes in patients with head and neck squamous cell carcinoma.

PURPOSE: Head and neck cancer is the sixth most common type of cancer worldwide, wherein the immune responses are closely associated with disease occurrence, development, and prognosis. Investigation of the role of immunogenic cell death-related genes (ICDGs) in adaptive immune response activation may provide cues into the mechanism underlying the outcome of HNSCC immunotherapy. METHODS: ICDGs expression patterns in HNSCC were analyzed, after which consensus clustering in HNSCC cohort conducted. A 4-gene prognostic model was constructed through LASSO and Cox regression analyses to analyze the prognostic index using the TCGA dataset, followed by validation with two GEO datasets. The distribution of immune cells and the response to immunotherapy were compared between different risk subtypes through multiple algorithms. Moreover, immunohistochemical (IHC) analyses were conducted to validate the prognostic value of HSP90AA1 as a predictor of HNSCC patient prognosis. In vitro assays were performed to further detect the effect of HSP90AA1 in the development of HNSCC. RESULTS: A novel prognostic index based on four ICDGs was constructed and proved to be useful as an independent factor of HNSCC prognosis. The risk score derived from this model grouped patients into high- and low-risk subtypes, wherein the high-risk subtype had worse survival outcomes and poorer immunotherapy response. IHC analysis validated the applicability of HSP90AA1 as a predictor of prognosis of HNSCC patients. HSP90AA1 expression in tumor cells promotes the progression of HNSCC. CONCLUSIONS: Together, these results highlight a novel four-gene prognostic signature as a valuable tool to assess survival status and prognosis of HNSCC patients.

Hyperspectral imaging combined with GA-SVM for maize variety identification.

The demand for identification of maize varieties has increased dramatically due to the phenomenon of mixed seeds and inferior varieties pretending to be high-quality varieties continuing to occur. It is urgent to solve the problem of efficient and accurate identification of maize varieties. A hyperspectral image acquisition system was used to acquire images of maize seeds. Regions of interest (ROI) with an embryo size of 10 × 10 pixel were extracted, and the average spectral information in the range of 949.43-1709.49 nm was intercepted for the subsequent study in order to eliminate random noise at both ends. Savitzky-Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the full-band spectrum. The feature wavelengths were screened by successive projection algorithms (SPA), competitive adaptive reweighted sampling (CARS) single screening, and two combinations of CARS-SPA and CARS + SPA, respectively. Support vector machines (SVMs) and models optimized based on genetic algorithm (GA), particle swarm optimization (PSO) were established by using full bands (FB) and feature bands as the model input. The results showed that the MSC-(CARS-SPA)-GA-SVM model had the best performance with 93.00% of the test set accuracy, 8 feature variables, and a running time of 24.45 s. MSC pretreatment can effectively eliminate the scattering effect of spectral data, and the feature wavelengths extracted by CARS-SPA can represent all wavelength information. The study proved that hyperspectral imaging combined with GA-SVM can realize the identification of maize varieties, which provided a theoretical basis for maize variety classification and authenticity identification.

Neutrophil extracellular traps promote invasion and metastasis via NLRP3-mediated oral squamous cell carcinoma pyroptosis inhibition.

Neutrophil extracellular traps (NETs) are reticular structures composed of neutrophil elastase (NE), cathepsin G (CG) and DNA-histone enzyme complexes. Accumulating evidence has revealed that NETs play important roles in tumor progression, metastasis, and thrombosis. However, our understanding of its clinical value and mechanism of action in oral squamous cell carcinoma (OSCC) is limited and has not yet been systematically described. Here, we aimed to investigate the clinical significance of NETs in OSCC and the mechanisms by which they affect its invasive and metastatic capacity. Our results demonstrated that high enrichment of NETs is associated with poor prognosis in OSCC, and mechanistic studies have shown that NE in NETs promotes invasion and metastasis via NLRP3-mediated inhibition of pyroptosis in OSCC. These findings may provide a new therapeutic approach for OSCC.

pH-Responsive Co-Assembled Peptide Hydrogel to Inhibit Drug-Resistant Bacterial Infection and Promote Wound Healing.

Drug-resistant bacterial infection and biofilm formation are the key inhibitors of wound healing, and new strategies are urgently needed to address these issues. In this study, we designed a pH-responsive co-assembled peptide hydrogel to inhibit Methicillin-resistant Staphylococcus aureus (MRSA) infection and promote wound healing. We synthesized a cationic short peptide (Nap-FFKKK) and a co-assembled hydrogel with curcumin at pH ∼ 7.8. The loaded curcumin was continuously released in a weak acid environment (pH ∼ 5.5). The lysine-rich cationic peptide inhibited biofilm formation in MRSA via electrostatic interaction with the negatively charged bacterial cell surface and, thus, provided a reinforcing antibacterial effect with curcumin. In vitro antibacterial experiments showed that the co-assembled system considerably reduced the minimum inhibitory concentration of curcumin against MRSA by 10-fold and promoted wound healing in a mouse model of MRSA-infected wounds. This study provides a simple and promising strategy to treat drug-resistant bacterial infections in wounds.

An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs.

Detecting genes that affect specific traits (such as human diseases and crop yields) is important for treating complex diseases and improving crop quality. A genome-wide association study (GWAS) provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms. Many GWAS summary statistics data related to various complex traits have been gathered recently. Studies have shown that GWAS risk loci and expression quantitative trait loci (eQTLs) often have a lot of overlaps, which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS. In this review, we review three types of gene-trait association detection methods of integrating GWAS summary statistics and eQTLs data, namely colocalization methods, transcriptome-wide association study-oriented approaches, and Mendelian randomization-related methods. At the theoretical level, we discussed the differences, relationships, advantages, and disadvantages of various algorithms in the three kinds of gene-trait association detection methods. To further discuss the performance of various methods, we summarize the significant gene sets that influence high-density lipoprotein, low-density lipoprotein, total cholesterol, and triglyceride reported in 16 studies. We discuss the performance of various algorithms using the datasets of the four lipid traits. The advantages and limitations of various algorithms are analyzed based on experimental results, and we suggest directions for follow-up studies on detecting gene-trait associations.

Comparing visible and near infrared 'point' spectroscopy and hyperspectral imaging techniques to visualize the variability of apple firmness.

In this work, visible and near-infrared 'point' (Vis-NIR) spectroscopy and hyperspectral imaging (Vis-NIR-HSI) techniques were applied on three different apple cultivars to compare their firmness prediction performances based on a large intra-variability of individual fruit, and develop rapid and simple models to visualize the variability of apple firmness on three apple cultivars. Apples with high degree of intra-variability can strongly affect the prediction model performances. The apple firmness prediction accuracy can be improved based on the large intra-variability samples with the coefficient variation (CV) values over 10%. The least squares-support vector machine (LS-SVM) models based on Vis-NIR-HSI spectra had better performances for firmness prediction than that of Vis-NIR spectroscopy, with the with the Rc2 over 0.84. Finally, The Vis-NIR-HSI technique combined with least squares-support vector machine (LS-SVM) models were successfully applied to visualize the spatial the variability of apple firmness.

Enhancing selection of alcohol consumption-associated genes by random forest.

Machine learning methods have been used in identifying omics markers for a variety of phenotypes. We aimed to examine whether a supervised machine learning algorithm can improve identification of alcohol-associated transcriptomic markers. In this study, we analysed array-based, whole-blood derived expression data for 17 873 gene transcripts in 5508 Framingham Heart Study participants. By using the Boruta algorithm, a supervised random forest (RF)-based feature selection method, we selected twenty-five alcohol-associated transcripts. In a testing set (30 % of entire study participants), AUC (area under the receiver operating characteristics curve) of these twenty-five transcripts were 0·73, 0·69 and 0·66 for non-drinkers v. moderate drinkers, non-drinkers v. heavy drinkers and moderate drinkers v. heavy drinkers, respectively. The AUC of the selected transcripts by the Boruta method were comparable to those identified using conventional linear regression models, for example, AUC of 1958 transcripts identified by conventional linear regression models (false discovery rate < 0·2) were 0·74, 0·66 and 0·65, respectively. With Bonferroni correction for the twenty-five Boruta method-selected transcripts and three CVD risk factors (i.e. at P < 6·7e-4), we observed thirteen transcripts were associated with obesity, three transcripts with type 2 diabetes and one transcript with hypertension. For example, we observed that alcohol consumption was inversely associated with the expression of DOCK4, IL4R, and SORT1, and DOCK4 and SORT1 were positively associated with obesity, and IL4R was inversely associated with hypertension. In conclusion, using a supervised machine learning method, the RF-based Boruta algorithm, we identified novel alcohol-associated gene transcripts.

Development of a cell adhesion-based prognostic model for multiple myeloma: Insights into chemotherapy response and potential reversal of adhesion effects.

Multiple myeloma (MM) is a hematologic malignancy notorious for its high relapse rate and development of drug resistance, in which cell adhesion-mediated drug resistance plays a critical role. This study integrated four RNA sequencing datasets (CoMMpass, GSE136337, GSE9782, and GSE2658) and focused on analyzing 1706 adhesion-related genes. Rigorous univariate Cox regression analysis identified 18 key prognosis-related genes, including KIF14, TROAP, FLNA, MSN, LGALS1, PECAM1, and ALCAM, which demonstrated the strongest associations with poor overall survival (OS) in MM patients. To comprehensively evaluate the impact of cell adhesion on MM prognosis, an adhesion-related risk score (ARRS) model was constructed using Lasso Cox regression analysis. The ARRS model emerged as an independent prognostic factor for predicting OS. Furthermore, our findings revealed that a heightened cell adhesion effect correlated with tumor resistance to DNA-damaging drugs, protein kinase inhibitors, and drugs targeting the PI3K/Akt/mTOR signaling pathway. Nevertheless, we identified promising drug candidates, such as tirofiban, pirenzepine, erlotinib, and bosutinib, which exhibit potential in reversing this resistance. In vitro, experiments employing NCIH929, RPMI8226, and AMO1 cell lines confirmed that MM cell lines with high ARRS exhibited poor sensitivity to the aforementioned candidate drugs. By employing siRNA-mediated knockdown of the key ARRS model gene KIF14, we observed suppressed proliferation of NCIH929 cells, along with decreased adhesion to BMSCs and fibronectin. This study presents compelling evidence establishing cell adhesion as a significant prognostic factor in MM. Additionally, potential molecular mechanisms underlying adhesion-related resistance are proposed, along with viable strategies to overcome such resistance. These findings provide a solid scientific foundation for facilitating clinically stratified treatment of MM.

SALL4 in gastrointestinal tract cancers: upstream and downstream regulatory mechanisms.

Effective therapeutic targets and early diagnosis are major challenges in the treatment of gastrointestinal tract (GIT) cancers. SALL4 is a well-known transcription factor that is involved in organogenesis during embryonic development. Previous studies have revealed that SALL4 regulates cell proliferation, survival, and migration and maintains stem cell function in mature cells. Additionally, SALL4 overexpression is associated with tumorigenesis. Despite its characterization as a biomarker in various cancers, the role of SALL4 in GIT cancers and the underlying mechanisms are unclear. We describe the functions of SALL4 in GIT cancers and discuss its upstream/downstream genes and pathways associated with each cancer. We also consider the possibility of targeting these genes or pathways as potential therapeutic options for GIT cancers.