A nonsense mutation in VHL causing Von Hippel-Lindau syndrome in a large Chinese family-a genetic study of familial neoplastic disease.

Von Hippel-Lindau (VHL) syndrome is a multi-organ neoplastic disease characterized by highly vascular and cystic tumors in the central nervous system (CNS), retina, and visceral lesions, which are mainly caused by germline mutations in VHL. We aimed to detect novel mutations in VHL gene in families with VHL. Here, a large consanguineous four-generation family with variant phenotypes of VHL syndrome was recruited, and its molecular genetics were tested via Sanger sequencing. And various tools and databases were used to predict the variant pathogenicity, frequency, and protein function. Genetic investigation detected a c.351G > A nonsense mutation in VHL that altered the downstream reading frame and created a premature TGA stop signal, resulting in severely truncated pVHL (p.Trp117Ter). This mutation is absent from most public databases, and functional prediction bioinformatic tools demonstrated that this residue is conserved and that this variant is highly likely to be deleterious. The c.315G > A nonsense mutation in VHL is the causal mutation of this kindred that may lead to clear familial aggregation of VHL syndrome because of the dysfunction of the truncated pVHL.

Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images.

Advances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described. We used both H&E and multiplex IHC (PD-L1, CD8+, and CD163+) images, investigated whether artificial intelligence (AI)-based algorithms using automatic feature extraction methods can predict neoadjuvant chemotherapy (NAC) outcomes in HER2-positive (HER2+) and triple-negative breast cancer (TNBC) patients. Features are derived from tumor immune micro-environment and clinical data and used to train machine learning models to accurately predict the response to NAC in breast cancer patients (HER2+ AUC = 0.8975; TNBC AUC = 0.7674). The results demonstrate that this method outperforms the results trained from features that were manually generated by pathologists. The developed image features and algorithms were further externally validated by independent cohorts, yielding encouraging results, especially for the HER2+ subtype.

Embelin alleviates weaned piglets intestinal inflammation and barrier dysfunction via PCAF/NF-κB signaling pathway in intestinal epithelial cells.

BACKGROUND: Intestinal barrier plays key roles in maintaining intestinal homeostasis. Inflammation damage can severely destroy the intestinal integrity of mammals. This study was conducted to investigate the protective effects of embelin and its molecular mechanisms on intestinal inflammation in a porcine model. One hundred sixty 21-day-old castrated weaned pigs (Duroc × Landrace × Yorkshire, average initial body weight was 7.05 ± 0.28 kg, equal numbers of castrated males and females) were allotted to four groups and fed with a basal diet or a basal diet containing 200, 400, or 600 mg embelin/kg for 28 d. The growth performance, intestinal inflammatory cytokines, morphology of jejunum and ileum, tight junctions in the intestinal mucosa of piglets were tested. IPEC-1 cells with overexpression of P300/CBP associating factor (PCAF) were treated with embelin, the activity of PCAF and acetylation of nuclear factor-κB (NF-κB) were analyzed to determine the effect of embelin on PCAF/NF-κB pathway in vitro. RESULTS: The results showed that embelin decreased (P < 0.05) serum D-lactate and diamine oxidase (DAO) levels, and enhanced the expression of ZO-1, occludin and claudin-1 protein in jejunum and ileum. Moreover, the expression levels of critical inflammation molecules (interleukin-1ß, interleukin-6, tumor necrosis factor-α, and NF-κB) were down-regulated (P < 0.05) by embelin in jejunal and ileal mucosa. Meanwhile, the activity of PCAF were down-regulated (P < 0.05) by embelin. Importantly, transfection of PCAF siRNAs to IPEC-1 cell decreased NF-κB activities; embelin treatment downregulated (P < 0.05) the acetylation and activities of NF-κB by 31.7%-74.6% in IPEC-1 cells with overexpression of PCAF. CONCLUSIONS: These results suggested that embelin ameliorates intestinal inflammation in weaned pigs, which might be mediated by suppressing the PCAF/NF-κB signaling pathway.

Associations between statins and adverse events in secondary prevention of cardiovascular disease: Pairwise, network, and dose-response meta-analyses of 47 randomized controlled trials.

Objectives: To explore the associations between different types and doses of statins and adverse events in secondary prevention of cardiovascular disease. Methods: We searched PubMed, Embase, and Cochrane databases for randomized controlled trials that compared statins with non-statin controls or different types or doses of statins. The primary outcomes included muscle condition, transaminase elevations, renal insufficiency, gastrointestinal discomfort, cancer, new onset or exacerbation of diabetes, cognitive impairment, and eye condition. We also analyzed myocardial infarction (MI), stroke, death from cardiovascular diseases (CVD), and all-cause death as the secondary outcomes to compare the potential harms with the benefits of statins. We conducted pairwise meta-analyses to calculate the odds ratio (OR) and 95% confidence intervals (CIs) for each outcome. Network meta-analyses were performed to compare the adverse effects of different statins. An Emax model was used to examine the dose-response relationships of the adverse effects of each statin. Results: Forty-seven trials involving 107,752 participants were enrolled and followed up for 4.05 years. Compared with non-statin control, statins were associated with an increased risk of transaminase elevations [OR 1.62 (95% CI 1.20 to 2.18)]. Statins decreased the risk of MI [OR 0.66 (95% CI 0.61 to 0.71), P < 0.001], stroke [OR 0.78 (95% CI 0.72 to 0.84), P < 0.001], death from CVD [OR 0.77 (95% CI 0.72 to 0.83), P < 0.001] and all-cause death [OR 0.83 (95% CI 0.79 to 0.88), P < 0.001]. Atorvastatin showed a higher risk of transaminase elevations than non-statin control [OR 4.0 (95% CI 2.2 to 7.6)], pravastatin [OR 3.49 (95% CI 1.77 to 6.92)] and simvastatin [OR 2.77 (95% CI 1.31 to 5.09)], respectively. Compared with atorvastatin, simvastatin was associated with a lower risk of muscle problems [OR 0.70 (95% CI 0.55 to 0.90)], while rosuvastatin showed a higher risk [OR 1.75 (95% CI 1.17 to 2.61)]. An Emax dose-response relationship was identified for the effect of atorvastatin on transaminase elevations. Conclusion: Statins were associated with increased risks of transaminases elevations in secondary prevention. Our study provides the ranking probabilities of statins that can help clinicians make optimal decisions when there is not enough literature to refer to. Systematic review registration: [https://www.crd.york.ac.uk/prospero/], identifier [CRD42021285161].

Serum Cannabinoid 24 h and 1 Week Steady State Pharmacokinetic Assessment in Cats Using a CBD/CBDA Rich Hemp Paste.

Hemp based cannabinoids have gained popularity in veterinary medicine due to the potential to treat pain, seizure disorders and dermatological maladies in dogs. Cat owners are also using hemp-based products for arthritis, anxiety and neoplastic disorders with no studies assessing hemp cannabinoids, namely cannabidiol efficacy, for such disorders. Initial twenty-four pharmacokinetic and chronic dosing serum concentration in cats are sparse. The aim of our study was to assess 8 cats physiological and 24 h and 1-week steady state pharmacokinetic response to a cannabidiol (CBD) and cannabidiolic acid (CBDA) rich hemp in a palatable oral paste. Using a standard dose of paste (6.4 mg/CBD + CBDA 5.3 mg/gram) across 8 cats weighing between 4.2 and 5.4 kg showed an average maximal concentration of CBD at 282.0 ± 149.4 ng/mL with a half-life of ~2.1 ± 1.1 h, and CBDA concentrations of 1,011.3 ± 495.4 ng/mL with a half-life of ~2.7 ± 1.4 h, showing superior absorption of CBDA. After twice daily dosing for 1 week the serum concentrations 6 h after a morning dosing showed that the acidic forms of the cannabinoids were approximately double the concentration of the non-acidic forms like CBD and Δ9- tetrahydrocannabinol (THC). The results of this study compared to two other recent studies suggest that the absorption in this specific paste product may be superior to oil bases used previously, and show that the acidic forms of cannabinoids appear to be absorbed better than the non-acidic forms. More importantly, physical and behavioral examinations every morning after dosing showed no adverse events related to neurological function or behavioral alterations. In addition, bloodwork after 1 week of treatment showed no clinically significant serum biochemical alterations as a reflection of hepatic and renal function all remaining within the reference ranges set by the diagnostic laboratory suggesting that short-term treatment was safe.

SPCS: a spatial and pattern combined smoothing method for spatial transcriptomic expression.

High-dimensional, localized ribonucleic acid (RNA) sequencing is now possible owing to recent developments in spatial transcriptomics (ST). ST is based on highly multiplexed sequence analysis and uses barcodes to match the sequenced reads to their respective tissue locations. ST expression data suffer from high noise and dropout events; however, smoothing techniques have the promise to improve the data interpretability prior to performing downstream analyses. Single-cell RNA sequencing (scRNA-seq) data similarly suffer from these limitations, and smoothing methods developed for scRNA-seq can only utilize associations in transcriptome space (also known as one-factor smoothing methods). Since they do not account for spatial relationships, these one-factor smoothing methods cannot take full advantage of ST data. In this study, we present a novel two-factor smoothing technique, spatial and pattern combined smoothing (SPCS), that employs the k-nearest neighbor (kNN) technique to utilize information from transcriptome and spatial relationships. By performing SPCS on multiple ST slides from pancreatic ductal adenocarcinoma (PDAC), dorsolateral prefrontal cortex (DLPFC) and simulated high-grade serous ovarian cancer (HGSOC) datasets, smoothed ST slides have better separability, partition accuracy and biological interpretability than the ones smoothed by preexisting one-factor methods. Source code of SPCS is provided in Github (https://github.com/Usos/SPCS).

Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease.

We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information "impressions," which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single-cell and patient bulk tissue transcriptomic datasets from glioblastoma multiforme (GBM), Alzheimer's disease (AD), and multiple myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on myeloma single-cell transcriptomics identified PHF19high myeloma cells associated with progression. Availability: https://github.com/tsteelejohnson91/DEGAS .

Diagnostic value of plasma RNF180 gene methylation for gastric cancer: A systematic review and meta-analysis.

Objective: A systematic evaluation of the diagnostic value of Ring finger protein 180 (RNF180) gene methylation as a novel tumor marker for gastric cancer (GC) is required to improve the early diagnosis of gastric cancer patients. Methods: Computer searches of PubMed, Web of Science, Embase, The Cochrane Library, CNKI, CBM, WanFang Data, National Research Register, Cclinical Controlled Trials, Opengrey and VIP databases were conducted from the database's inception to September 1, 2022. Two researchers independently screened the literature, extracted information, and assessed the risk of bias in studies that were included. The meta-analysis was carried out using RevMan 5.3 and Stata 16.0 software. Results: A total of 9 studies with a total of 1531 subjects were included. A random-effects meta-analysis revealed that the combined sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) of plasma RNF180 gene methylation for the diagnosis of GC were: 0.54 [95% CI (0.45, 0.62)], 0.80 [95% CI (0.72, 0.87)], 2.73 [95% CI (2.09, 3.57)], 0.58 [95% CI (0.51, 0.65)], 4.74 [95% CI (3.59, 6.62)], respectively. Conclusion: The detection of RNF180 gene methylation in plasma has a high diagnostic value for GC and is expected to be a potential biomarker for the diagnosis of gastric cancer, according to current evidence. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=370903, identifier CRD42022370903.

Chromosome-level reference genome of tetraploid Isoetes sinensis provides insights into evolution and adaption of lycophytes.

BACKGROUND: The Lycophyta species are the extant taxa most similar to early vascular plants that were once abundant on Earth. However, their distribution has greatly diminished. So far, the absence of chromosome-level assembled lycophyte genomes has hindered our understanding of evolution and environmental adaption of lycophytes. FINDINGS: We present the reference genome of the tetraploid aquatic quillwort, Isoetes sinensis, a lycophyte. This genome represents the first chromosome-level assembled genome of a tetraploid seed-free plant. Comparison of genomes between I. sinensis and Isoetestaiwanensis revealed conserved and different genomic features between diploid and polyploid lycophytes. Comparison of the I. sinensis genome with those of other species representing the evolutionary lineages of green plants revealed the inherited genetic tools for transcriptional regulation and most phytohormones in I. sinensis. The presence and absence of key genes related to development and stress responses provide insights into environmental adaption of lycophytes. CONCLUSIONS: The high-quality reference genome and genomic analysis presented in this study are crucial for future genetic and environmental studies of not only I. sinensis but also other lycophytes.

Weakly Supervised Deep Ordinal Cox Model for Survival Prediction From Whole-Slide Pathological Images.

Whole-Slide Histopathology Image (WSI) is generally considered the gold standard for cancer diagnosis and prognosis. Given the large inter-operator variation among pathologists, there is an imperative need to develop machine learning models based on WSIs for consistently predicting patient prognosis. The existing WSI-based prediction methods do not utilize the ordinal ranking loss to train the prognosis model, and thus cannot model the strong ordinal information among different patients in an efficient way. Another challenge is that a WSI is of large size (e.g., 100,000-by-100,000 pixels) with heterogeneous patterns but often only annotated with a single WSI-level label, which further complicates the training process. To address these challenges, we consider the ordinal characteristic of the survival process by adding a ranking-based regularization term on the Cox model and propose a weakly supervised deep ordinal Cox model (BDOCOX) for survival prediction from WSIs. Here, we generate amounts of bags from WSIs, and each bag is comprised of the image patches representing the heterogeneous patterns of WSIs, which is assumed to match the WSI-level labels for training the proposed model. The effectiveness of the proposed method is well validated by theoretical analysis as well as the prognosis and patient stratification results on three cancer datasets from The Cancer Genome Atlas (TCGA).

MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification.

To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems.

Correlation between TLR4 gene polymorphism and acute respiratory distress syndrome after esophageal cancer surgery.

BACKGROUND: One of the major postoperative complications of esophageal cancer is acute respiratory distress syndrome (ARDS), which poses a great threat to patients' lives. In this research, the cause of ARDS after esophageal cancer surgery was explained from the aspect of the single-nucleotide polymorphism at rs7873784, rs10759930 and rs10983755 of the Toll-like receptor 4 (TLR4) gene. METHODS: A total of 75 patients complicated with ARDS after esophageal cancer surgery in our hospital were collected as the ARDS group and 150 patients without ARDS after surgery as the control group. Deoxyribonucleic acids (DNAs) in the peripheral blood of patients were extracted, and the polymorphism loci (rs7873784, rs10759930 and rs10983755) of the TLR4 gene were amplified through polymerase chain reaction (PCR) and sent to a company for sequencing. The concentration of serum TLR4 was detected by kits. RESULTS: The frequencies of the G allele at rs7873784 (P=0.011) and C allele at rs10759930 (P=0.000) in the ARDS group were remarkably lower than those in the control group. Besides, the frequencies of GG genotype at rs7873784 (P=0.000) and CC and CT genotypes at rs10759930 (P=0.000) in the control group were notably higher than those in the ARDS group, while the frequency of AA genotype at rs10983755 (P=0.001) in the ARDS group was clearly lower than that in control group. The survival status of patients with complications of ARDS was notably correlated with CT genotype at rs10759930 of the TLR4 gene since patients with genotype CT were more likely to die (P=0.001). The GG genotype at rs10983755 of the TLR4 gene was remarkably related to the mean mechanical ventilation time (P=0.003) and the average length of intensive care unit (ICU) stay (P=0.018). The ARDS group had a lower frequency of GCG haplotype (P=0.009) and a higher frequency of GTA haplotype (P=0.001) than the control group. The linkage disequilibrium D' was 0.781 between rs7873784 and rs10759930 of the TLR4 gene, and two loci were linked to each other. In addition, the concentration of serum TRL4 in patients with genotype CC at rs7873784 (P=0.034), genotype CT at rs7873784 (P=0.000) and genotype GG at rs10983755 (P=0.000) of the TLR4 gene in the ARDS group was higher than that in the control group. CONCLUSION: The single-nucleotide polymorphisms at rs7873784, rs10759930 and rs10983755 of the TLR4 gene are significantly related to ARDS after esophageal cancer surgery.

TSUNAMI: Translational Bioinformatics Tool Suite for Network Analysis and Mining.

Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It enables researchers to discover latent gene/molecule interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus helping to identify disease biomarkers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis, as well as modules that may share common members. To address this need, we developed an online GCN mining tool package: TSUNAMI (Tools SUite for Network Analysis and MIning). TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data (microarray, RNA-seq, or any other numerical omics data), and then performs downstream gene set enrichment analysis for the identified modules. It has several features and advantages: 1) a user-friendly interface and real-time co-expression network mining through a web server; 2) direct access and search of NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, as well as user-input gene expression matrices for GCN module mining; 3) multiple co-expression analysis tools to choose from, all of which are highly flexible in regards to parameter selection options; 4) identified GCN modules are summarized to eigengenes, which are convenient for users to check their correlation with other clinical traits; 5) integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools; and 6) visualization of gene loci by Circos plot in any step of the process. The web service is freely accessible through URL: https://biolearns.medicine.iu.edu/. Source code is available at https://github.com/huangzhii/TSUNAMI/.

Protective effects of orally administered shark compound peptides from Chiloscyllium plagiosum against acute inflammation.

The anti-inflammatory effects of shark compound peptides (SCP) from Chiloscyllium plagiosum were investigated. Results showed that SCP enhanced the viability of RAW 264.7 macrophages in vitro in a dose-dependent manner. Orally administered SCP exhibited potent anti-inflammatory activity in lipopolysaccharide (LPS)-challenged mice by suppressing serum levels of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-8 (IL-8), as well as nitric oxide (NO). Moreover, SCP significantly inhibited the inflammatory rise of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), lactate dehydrogenase (LDH), and creatinine (CRE), while blocking the decline of cholinesterase (CHE), with an efficacy close to aspirin. This research showed that orally administered SCP from C. plagiosum notably downregulated uncontrolled inflammatory responses, and conferred substantial protection from endotoxin-induced acute hepatic damage and renal functional impairment. Therefore, oral supplementation of SCP can be used as a preventive approach to reduce the risk of inflammatory-related diseases.

Garcinol promotes hepatic gluconeogenesis by inhibiting P300/CBP-associated factor in late-pregnant sows.

Disorder of hepatic glucose metabolism is the characteristic of late-pregnant sows. The purpose of our study was to look into the mechanism of garcinol on the improvement of hepatic gluconeogenic enzyme in late-pregnant sows. Thirty second- and third-parity sows (Duroc × Yorkshire × Landrace, n 10/diet) were fed a basal diet (control) or that diet supplemented with 100 mg/kg (Low Gar) or 500 mg/kg (High Gar) garcinol from day 90 of gestation to the end of farrowing. The livers were processed to measure enzymatic activity. Hepatocytes from pregnant sows were transfected with P300/CBP-associating factor (PCAF) small interfering RNA (siRNA) or treated with garcinol. Dietary garcinol had no effect on average daily feed intake, body weight (BW), backfat and BW gain of late-pregnant sows. Garcinol promoted plasma glucose levels in pregnant sows and newborn piglets. Garcinol up-regulated hepatic gluconeogenic enzyme expression and decreased PCAF activity. Garcinol had no effect on the expression of PPAR-γ co-activator 1α (PGC-1α) and Forkhead box O1 (FOXO1) but significantly increased their activity and decreased their acetylation in late-pregnant sows. Transfection of PCAF siRNA to hepatocytes of pregnant sows increased PGC-1α and FOXO1 activities. Furthermore, in hepatocytes of pregnant sows, garcinol treatment also up-regulated the activities of PGC-1α and FOXO1 and inhibited the acetylation of PGC-1α and FOXO1. Garcinol improves hepatic gluconeogenic enzyme expression in late-pregnant sows, and this may be due to the mechanism of down-regulating the acetylation of PGC-1α and FOXO1 induced by PCAF in isolated hepatocytes.

Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer.

Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status based on histopathology images, and interpreted the pathomics-based model with multi-omics correlation. Methods: Two cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from an Asian colorectal cancer (CRC) cohort (Asian-CRC). We established the pathomics model, named Ensembled Patch Likelihood Aggregation (EPLA), based on two consecutive stages: patch-level prediction and WSI-level prediction. The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model were analyzed with genomic and transcriptomic profiles for model interpretation. Results: The EPLA model achieved an area-under-the-curve (AUC) of 0.8848 (95% CI: 0.8185-0.9512) in the TCGA-COAD test set and an AUC of 0.8504 (95% CI: 0.7591-0.9323) in the external validation set Asian-CRC after transfer learning. Notably, EPLA captured the relationship between pathological phenotype of poor differentiation and MSI (P < 0.001). Furthermore, the five pathological imaging signatures identified from the EPLA model were associated with mutation burden and DNA damage repair related genotype in the genomic profiles, and antitumor immunity activated pathway in the transcriptomic profiles. Conclusions: Our pathomics-based deep learning model can effectively predict MSI from histopathology images and is transferable to a new patient cohort. The interpretability of our model by association with pathological, genomic and transcriptomic phenotypes lays the foundation for prospective clinical trials of the application of this artificial intelligence (AI) platform in ICB therapy.

Multi-task multi-modal learning for joint diagnosis and prognosis of human cancers.

With the tremendous development of artificial intelligence, many machine learning algorithms have been applied to the diagnosis of human cancers. Recently, rather than predicting categorical variables (e.g., stages and subtypes) as in cancer diagnosis, several prognosis prediction models basing on patients' survival information have been adopted to estimate the clinical outcome of cancer patients. However, most existing studies treat the diagnosis and prognosis tasks separately. In fact, the diagnosis information (e.g., TNM Stages) indicates the extent of the disease severity that is highly correlated with the patients' survival. While the diagnosis is largely made based on histopathological images, recent studies have also demonstrated that integrative analysis of histopathological images and genomic data can hold great promise for improving the diagnosis and prognosis of cancers. However, direct combination of these two types of data may bring redundant features that will negatively affect the prediction performance. Therefore, it is necessary to select informative features from the derived multi-modal data. Based on the above considerations, we propose a multi-task multi-modal feature selection method for joint diagnosis and prognosis of cancers. Specifically, we make use of the task relationship learning framework to automatically discover the relationships between the diagnosis and prognosis tasks, through which we can identify important image and genomics features for both tasks. In addition, we add a regularization term to ensure that the correlation within the multi-modal data can be captured. We evaluate our method on three cancer datasets from The Cancer Genome Atlas project, and the experimental results verify that our method can achieve better performance on both diagnosis and prognosis tasks than the related methods.

Dietary garcinol supplementation improves diarrhea and intestinal barrier function associated with its modulation of gut microbiota in weaned piglets.

BACKGROUND: The effects of dietary garcinol on diarrhea and intestinal barrier function associated with its modulation of gut microbiota in weaned piglets were investigated. METHOD: One hundred forty four weaned piglets (Duroc × Yorkshire × Landrace) from 16 pens (9 piglets per pen) were randomly divided into four treatment groups: controls (CON) or those supplemented with 200 mg/kg (LOW), 400 mg/kg (MID), or 600 mg/kg (HIGH) diet garcinol. After 14-day trial, three piglets per pen were chosen to collect plasma, intestinal tissue and colonic digesta samples. RESULTS: We demonstrated for the first time that garcinol promoted growth performance, as increased average daily feed intake (ADFI) and decreased feed/gain ratio (F/G); and reduced diarrhea incidence (P < 0.05); and strengthened antioxidant capacity, as an increased antioxidative index (P < 0.05). Additionally, garcinol ameliorated intestinal barrier dysfunction, as an increased villus height to crypt depth ratio, increased zonula occludens protein 1 (ZO-1), occludin and claudin-1 expression in the jejunum and ileum (P < 0.05), and decreased intestinal permeability (P < 0.05); and reduced inflammation, as decreased cytokine interleukin (IL)-6, IL-10, IL-1ß and tumor necrosis factor-α (TNF-α) levels in the mucosa of the jejunum and ileum, and NF-κB p65 translocation (P < 0.05). Moreover, garcinol inhibited the growth of most harmful bacteria in the gut, especially Escherichia coli, and increased the growth of the beneficial bacteria Lactobacillus. CONCLUSION: This work provides a fundamental basis for the future development of garcinol-functional food use for improving diarrhea and intestinal barrier function in weaned piglets and for understanding the biological effects of garcinol and its potential as a functional feed additive.

Bone-strengthening effects and safety of compound peptides from skin of Chiloscyllium plagiosum and Mustelus griseus.

Fish processing produces a lot of by-products highly containing large amount of proteins which mainly consist of collagen, implying great potential value for application as nutraceutical ingredients. In present study, two kinds of sharks, Chiloscyllium plagiosum and Mustelus griseus, were used as raw material to gain three kinds of "compound peptides" (CPs) by enzymolysis, FCP (CPs from the flesh of C. plagiosum), SCP (CPs from the skin of C. plagiosum), and SMG (CPs from the skin of M. griseus). According to a series of constituent analysis, the molecule weights of FCP, SCP, and SMG were under 800 Da; amino acids composition analysis of FCP, SCP, and SMG showed that there were high glycine, proline, and hydroxyproline and low cysteine contents in SCP and SMG, which is the characteristic of collagen peptides; their total protein contents were 87.500%, 91.875%, and 95.625%, respectively; and heavy metal contents of CPs were all beneath national standards. After three kinds of CPs were administrated intragastrically to C57BL/6 mice at a total dosage of 15 g/kg, bone-strengthening effects of SCP and SMG were manifested by osteoblasts activity promotion, bone mineral density (BMD) increase, and marrow adipocyte number decrease, yet nonsignificant effects were shown in FCP group. No index showed toxicity of SCP and SMG in subacute toxicology trial, indicating their safety as functional foods. Herein, industrial application foundation of the skins from these two sharks was explored but more efforts should subsequently be implemented for further exploitation.

Integrative Analysis of Pathological Images and Multi-Dimensional Genomic Data for Early-Stage Cancer Prognosis.

The integrative analysis of histopathological images and genomic data has received increasing attention for studying the complex mechanisms of driving cancers. However, most image-genomic studies have been restricted to combining histopathological images with the single modality of genomic data (e.g., mRNA transcription or genetic mutation), and thus neglect the fact that the molecular architecture of cancer is manifested at multiple levels, including genetic, epigenetic, transcriptional, and post-transcriptional events. To address this issue, we propose a novel ordinal multi-modal feature selection (OMMFS) framework that can simultaneously identify important features from both pathological images and multi-modal genomic data (i.e., mRNA transcription, copy number variation, and DNA methylation data) for the prognosis of cancer patients. Our model is based on a generalized sparse canonical correlation analysis framework, by which we also take advantage of the ordinal survival information among different patients for survival outcome prediction. We evaluate our method on three early-stage cancer datasets derived from The Cancer Genome Atlas (TCGA) project, and the experimental results demonstrated that both the selected image and multi-modal genomic markers are strongly correlated with survival enabling effective stratification of patients with distinct survival than the comparing methods, which is often difficult for early-stage cancer patients.