Predicting the Ki-67 proliferation index in cervical cancer: a preliminary comparative study of four non-Gaussian diffusion-weighted imaging models combined with histogram analysis.

Background: The prognosis for patients with cervical cancer (CC) is strongly correlated with the Ki-67 proliferation index (PI). However, the Ki-67 PI obtained through biopsy has certain limitations. The non-Gaussian distribution diffusion model of magnetic resonance imaging (MRI) may play an important role in characterizing tissue heterogeneity. At present, there are limited data available concerning the prediction of Ki-67 PI using models based on histogram features of non-Gaussian diffusion distribution. This study aimed to determine whether preoperative histogram features from multiple non-Gaussian models of diffusion-weighted imaging can predict the Ki-67 PI in patients with CC. Methods: Our cross-sectional prospective study recruited a total of 53 patients suspected of having CC who underwent 3.0-T MRI at Sun Yat-sen Memorial Hospital of Sun Yat-sen University between January 2022 and January 2023. Fifteen b values (0-4,000 s/mm2) were used for diffusion-weighted imaging. A total of nine parameters from four non-Gaussian diffusion-weighted imaging models, including continuous-time random walk (CTRW), diffusion kurtosis imaging (DKI), fractional order calculus (FROC), and intravoxel incoherent motion (IVIM), were used. Whole-tumor volumetric histogram analysis of these parameters was then obtained. In logistic regression, significant histogram characteristics were statistically examined across two groups to build the final prediction model. To assess diagnostic parameters of the proposed model in the diagnosis of the Ki-67 PI, along with the sensitivity, specificity, and diagnostic accuracy of these various parameters from the four models, receiver operating feature analysis was applied. Results: Among the 53 patients (55.3±9.6 years, ranging from 23 to 79 years) included in the study, 15 had a Ki-67 PI ≤50% and 38 had a Ki-67 PI >50%. Univariable analysis determined that 12 histogram features were statistically different between the two groups. In multivariable logistic regression, we ultimately selected 6 histogram features to construct the final prediction model, with CTRW_α_10th percentile [odds ratio (OR) =0.955; 95% confidence interval (CI): 0.92-0.99; P=0.019], CTRW_α_robust mean absolute deviation (OR =0.893; 95% CI: 0.81-0.99; P=0.028), and CTRW_α_uniformity (OR =0.000, 95% CI: 0.00-0.90, P=0.047) being the independent predictive variables. The area under the curve of the combined prediction model was 0.845 (95% CI: 0.74-0.95), with a sensitivity of 78.9% (95% CI: 0.63-0.90), a specificity of 86.7% (95% CI: 0.60-0.98), an accuracy of 81.1% (95% CI: 0.68-0.91), a positive predictive value of 93.8% (95% CI: 0.79-0.99), and a negative predictive value of 61.9% (95% CI: 0.38-0.82). Conclusions: The histogram features of multiple non-Gaussian diffusion-weighted imaging can help to predict the Ki-67 PI of CC, providing a new method for the noninvasive evaluation of critical biological features of CC.

Multi-View Integrative Approach For Imputing Short-Chain Fatty Acids and Identifying Key factors predicting Blood SCFA.

Short-chain fatty acids (SCFAs) are the main metabolites produced by bacterial fermentation of dietary fiber within gastrointestinal tract. SCFAs produced by gut microbiotas (GMs) are absorbed by host, reach bloodstream, and are distributed to different organs, thus influencing host physiology. However, due to the limited budget or the poor sensitivity of instruments, most studies on GMs have incomplete blood SCFA data, limiting our understanding of the metabolic processes within the host. To address this gap, we developed an innovative multi-task multi-view integrative approach (M2AE, Multi-task Multi-View Attentive Encoders), to impute blood SCFA levels using gut metagenomic sequencing (MGS) data, while taking into account the intricate interplay among the gut microbiome, dietary features, and host characteristics, as well as the nuanced nature of SCFA dynamics within the body. Here, each view represents a distinct type of data input (i.e., gut microbiome compositions, dietary features, or host characteristics). Our method jointly explores both view-specific representations and cross-view correlations for effective predictions of SCFAs. We applied M2AE to two in-house datasets, which both include MGS and blood SCFAs profiles, host characteristics, and dietary features from 964 subjects and 171 subjects, respectively. Results from both of two datasets demonstrated that M2AE outperforms traditional regression-based and neural-network based approaches in imputing blood SCFAs. Furthermore, a series of gut bacterial species (e.g., Bacteroides thetaiotaomicron and Clostridium asparagiforme), host characteristics (e.g., race, gender), as well as dietary features (e.g., intake of fruits, pickles) were shown to contribute greatly to imputation of blood SCFAs. These findings demonstrated that GMs, dietary features and host characteristics might contribute to the complex biological processes involved in blood SCFA productions. These might pave the way for a deeper and more nuanced comprehension of how these factors impact human health.

Multi-Omics Analysis Reveals Molecular Responses of Alkaloid Content Variations in Lycoris aurea Across Different Locations.

Lycoris aurea, celebrated for its visually striking flowers and significant medicinal value due to the presence of alkaloids such as lycorine and galanthamine, has intricate yet poorly understood regulatory mechanisms. This study provides a detailed examination of the transcriptomic, metabolomic and ecological dynamics of L. aurea, aiming to elucidate the underlying molecular mechanisms of alkaloid biosynthesis. Our comparative analysis across different ecological settings highlighted key genes involved in alkaloid biosynthesis, such as genes encoding aldehyde dehydrogenase and norbelladine 4'-O-methyltransferase, which were distinctively increased in the high alkaloids-producing group. We identified a total of 6871 differentially expressed genes and 915 metabolites involved in pathways like terpenoid backbone biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis. Protein interaction network analysis revealed significant upregulation of photosynthesis, photosystem and photosynthetic membrane pathways in the alkaloids-producing region. Furthermore, our research delineated the interactions among soil microbial communities, genes and plant and soil biochemical properties, noting that bacterial populations correlate with soil properties that favour the activation of metabolic pathways essential for alkaloid production. Collectively, this study advances our understanding of the genetic and metabolic alkaloid biosynthesis pathways in L. aurea, shedding light on the complex interactions that govern alkaloid production.

Sortilin-mediated translocation of mitochondrial ACSL1 impairs adipocyte thermogenesis and energy expenditure in male mice.

Beige fat activation involves a fuel switch to fatty acid oxidation following chronic cold adaptation. Mitochondrial acyl-CoA synthetase long-chain family member 1 (ACSL1) localizes in the mitochondria and plays a key role in fatty acid oxidation; however, the regulatory mechanism of the subcellular localization remains poorly understood. Here, we identify an endosomal trafficking component sortilin (encoded by Sort1) in adipose tissues that shows dynamic expression during beige fat activation and facilitates the translocation of ACSL1 from the mitochondria to the endolysosomal pathway for degradation. Depletion of sortilin in adipocytes results in an increase of mitochondrial ACSL1 and the activation of AMPK/PGC1α signaling, thereby activating beige fat and preventing high-fat diet (HFD)-induced obesity and insulin resistance. Collectively, our findings indicate that sortilin controls adipose tissue fatty acid oxidation by substrate fuel selection during beige fat activation and provides a potential targeted approach for the treatment of metabolic diseases.

A staged approach using machine learning and uncertainty quantification to predict the risk of hip fracture.

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. The study cohort included 547 patients, with 94 experiencing hip fracture. To assess the risk of hip fracture, clinical variables and clinical variables combined with hip DXA imaging features were evaluated as predictors, followed by a novel staged approach. Hip DXA imaging features included those extracted by convolutional neural networks (CNNs), shape measurements, and texture features. Two ensemble machine learning models were evaluated: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and imaging features) using the logistic regression as the base classifier and bootstrapping for ensemble learning. The staged approach was developed using uncertainty quantification from Ensemble 1 which was used to decide if hip DXA imaging features were necessary to improve prediction for each subject. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (AUC) of 0.95, an accuracy of 0.92, a sensitivity of 0.81, and a specificity of 0.94. The staged model also performed well, with an AUC of 0.85, an accuracy of 0.86, a sensitivity of 0.56, and a specificity of 0.92, outperforming Ensemble 1, which had an AUC of 0.55, an accuracy of 0.73, a sensitivity of 0.20, and a specificity of 0.83. Furthermore, the staged model suggested that 54.49 % of patients did not require DXA scanning, effectively balancing accuracy and specificity, while offering a robust solution when DXA data acquisition is not feasible. Statistical tests confirmed significant differences between the models, highlighting the advantages of advanced modeling strategies. Our staged approach offers a cost-effective holistic view of patient health. It can identify individuals at risk of hip fracture with a high accuracy while reducing unnecessary DXA scans. This approach has great promise to guide the need for interventions to prevent hip fracture while reducing diagnostic cost and exposure to radiation.

Low-methoxy-pectin and chlorogenic acid synergistically promote lipolysis and ß-oxidation by regulating AMPK signaling pathway in obese mice.

Chlorogenic acid (CGA) displays various biological activities in preventing high-calorie diet-induced metabolic complications. The absorption efficiency of CGA in the stomach and small intestine is relatively low, with approximately 70 % of CGA being metabolized by colonic microorganisms before it enters the bloodstream. In this study, we successfully developed CGA-LMP (Low-methoxy-pectin) conjugates to improve the absorption rate of CGA. C57BL/6J mice were fed high-fat diets (HFD) supplemented with CGA, LMP, or CGA-LMP conjugates for a duration of eight weeks. The results demonstrated that the CGA, LMP, or CGA-LMP conjugates prevented HFD-induced hyperlipidemia, inflammation, liver steatosis, and adipocyte hypertrophy in obese mice. Notably, the CGA-LMP conjugates demonstrated superior efficacy in alleviating obesity compared to CGA or LMP alone. Further studies revealed that the primary mechanism of weight loss was the activation of the AMPK signaling pathway, which facilitates lipolysis and lipid ß-oxidation. These findings highlight that the enhanced the anti-obesity effectiveness of CGA-LMP conjugates, expanding their potential applications in the field of functional nutrition and foods.

Harnessing the CD44-targeted delivery of self-assembled hyaluronan nanogel to reverse the antagonism between Cisplatin and Gefitinib in NSCLC cancer therapy.

The combination of the standard platinum-based chemotherapy with EGFR-tyrosine kinase inhibitor Gefitinib (Gef) principally boosts the anticancer efficacy of advanced non-small cell lung cancer (NSCLC) through non-overlapping mechanisms of action, however the clinical trials of cisplatin (Cis) and Gef combination failed to show a therapeutic improvement likely due to compromised cellular influx of Cis with the Gef interference. To overcome the antagonism between Cis and Gef in anti-NSCLC therapy, here we demonstrated a self-targeted hyaluronan (HA) nanogel to facilitate the anticancer co-delivery by utilizing the HA's intrinsic targeting towards CD44, a receptor frequently overexpressed on lung cancer cells. The co-assembly between HA, Cis and Gef generated a HA/Cis/Gef nanogel of 177.8 nm, featuring a prolonged drug release. Unlike the Gef inhibited the Cis uptake, the HA/Cis/Gef nanogel efficiently facilitated the drug internalization through CD44-targeted delivery as verified by HA competition and CD44 knocking down in H1975 NSCLC model both in vitro and in vivo. Moreover, the HA/Cis/Gef nanogel significantly improved the anticancer efficacy and meanwhile diminished the side effects in reference to the combination of free Cis and Gef. This CD44-targeted HA/Cis/Gef nanogel provided a potent strategy to advance the platinum-based combination therapy towards optimized NSCLC therapy.

Decoding potential targets and pharmacologic mechanisms of curcumin in treating non-small cell lung carcinoma via bioinformatics and molecular docking.

Emerging evidence demonstrates that curcumin has an inhibitory effect on non-small cell lung cancer (NSCLC), and its targets and mechanism of action need further exploration. The goal of this study was to explore the potential targets and mechanism of curcumin against NSCLC by network pharmacology, bioinformatics, and experimental validation, thereby providing more insight into combination treatment with curcumin for NSCLC in preclinical and clinical research. Curcumin targets against NSCLC were predicted based on HIT2.0, STD, CTD, and DisGeNET, and the core targets were analyzed via protein-protein interaction network construction (PPI), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and molecular docking. The gene expression levels of samples in A549 cells, NCI-H460, and curcumin treated groups were detected by real-time quantitative PCR. A total of 67 common targets between curcumin and NSCLC were collected by screening public databases. GO and KEGG analysis suggested that curcumin treatment of NSCLC mainly involves cancer-related pathways, such as PI3K-AKT signaling pathway, Foxo signaling pathway, microRNAs, MAPK signaling pathway, HIF-1 signaling pathway, etc. The targets with the highest degree were identified through the PPI network, namely CASP3, CTNNB1, JUN, IL6, MAPK3, HIF1A, STAT3, AKT1, TP53, CCND1, VEGFA, and EGFR. The results of the in vitro experiments showed that curcumin treatment of NSCLC down-regulated the gene expressions of CCND1, CASP3, HIF1A, IL-6, MAPK3, STAT3, AKT1, and TP53. Our findings revealed that curcumin functions as a potential therapeutic candidate for NSCLC by suppressing multiple signaling pathways and interacting with multiple gene targets.

MicroRNA-411-5p alleviates lipid deposition in metabolic dysfunction-associated steatotic liver disease by targeting the EIF4G2/FOXO3 axis.

BACKGROUND: Abnormal lipid deposition is an important driver of the progression of metabolic dysfunction-associated steatotic liver disease (MASLD). MicroRNA-411-5p (miR-411-5p) and eukaryotic translation initiation factor 4γ2 (EIF4G2) are related to abnormal lipid deposition, but the specific mechanism is unknown. METHODS: A high-fat, high-cholesterol diet (HFHCD) and a choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) and a high-fructose diet (HFrD) were used to establish MASLD rat and mouse models, respectively. MiR-411-5p agomir and mimic were used to upregulate the miR-411-5p in vivo and in vitro, respectively. Adeno-associated virus type 8 (AAV8) carrying EIF4G2 short hairpin RNA (shRNA) and small interfering RNA (siRNA) were used to downregulate the EIF4G2 expression in vivo and in vitro, respectively. Liver histopathological analysis, Biochemical analysis and other experiments were used to explore the functions of miR-411-5p and EIF4G2. RESULTS: MiR-411-5p was decreased in both MASLD rats and mice, and was negatively correlated with liver triglycerides and serum alanine transaminase (ALT) and aspartate transaminase (AST) levels. Upregulation of miR-411-5p alleviated liver lipid deposition and hepatocellular steatosis. Moreover, miR-411-5p targeted and downregulated EIF4G2. Downregulation of EIF4G2 not only reduced liver triglycerides and serum ALT and AST levels in MASLD model, but also alleviated lipid deposition. Notably, upregulation of miR-411-5p and downregulation of EIF4G2 led to the reduction of forkhead box class O3 (FOXO3) and inhibited the expression of sterol regulatory-element binding protein 1 (SREBP1), acetyl-CoA carboxylase 1 (ACC1) and fatty acid synthase (FASN), thereby reducing fatty acid synthesis. CONCLUSIONS: Upregulation of miR-411-5p inhibits EIF4G2 to reduce the FOXO3 expression, thereby reducing fatty acid synthesis and alleviating abnormal lipid deposition in MASLD.

Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study.

BACKGROUND: Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. METHODS AND FINDINGS: We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. CONCLUSIONS: In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups.

An atlas of genetic effects on the monocyte methylome across European and African populations.

Elucidating the genetic architecture of DNA methylation (DNAm) is crucial for decoding the etiology of complex diseases. However, current epigenomic studies often suffer from incomplete coverage of methylation sites and the use of tissues containing heterogeneous cell populations. To address these challenges, we present a comprehensive human methylome atlas based on deep whole-genome bisulfite sequencing (WGBS) and whole-genome sequencing (WGS) of purified monocytes from 298 European Americans (EA) and 160 African Americans (AA) in the Louisiana Osteoporosis Study. Our atlas enables the analysis of over 25 million DNAm sites. We identified 1,383,250 and 1,721,167 methylation quantitative trait loci (meQTLs) in cis -regions for EA and AA populations, respectively, with 880,108 sites shared between ancestries. While cis -meQTLs exhibited population-specific patterns, primarily due to differences in minor allele frequencies, shared cis -meQTLs showed high concordance across ancestries. Notably, cis -heritability estimates revealed significantly higher mean values in the AA population (0.09) compared to the EA population (0.04). Furthermore, we developed population-specific DNAm imputation models using Elastic Net, enabling methylome-wide association studies (MWAS) for 1,976,046 and 2,657,581 methylation sites in EA and AA, respectively. The performance of our MWAS models was validated through a systematic multi-ancestry analysis of 41 complex traits from the Million Veteran Program. Our findings bridge the gap between genomics and the monocyte methylome, uncovering novel methylation-phenotype associations and their transferability across diverse ancestries. The identified meQTLs, MWAS models, and data resources are freely available at www.gcbhub.org and https://osf.io/gct57/ .

Design of an Oxide Monolayer with High ZT by a Strong Anharmonicity Unit.

In this paper, a new strategy to obtain a transition-metal oxide (TMO) thermoelectric monolayer is demonstrated. We show that the TMO thermoelectric monolayer can be achieved by the replacement of a transition-metal atom with a cluster, which is composed of heavy transition atoms with abundant valence electrons. Specifically, the transition-metal atom in the XO2 (X = Ti, Zr, Hf) monolayer is replaced by the [Ag6]4+ cluster and a stable structure Ag6O2 is achieved. Due to the abundant valence electrons in the [Ag6]4+ cluster unit, n-type Ag6O2 has high electrical conductivity, which leads to a satisfactory power factor. More importantly, Ag6O2 has an extremely low phonon thermal conductivity of 0.16 W·m-1·K-1, which is one of the lowest values in thermoelectric materials. An in-depth study reveals that the extremely low value originates from the strong phonon anharmonicity and weak metal bond of the [Ag6]4+ cluster unit. Due to the satisfactory power factor and ultralow phonon thermal conductivity, Ag6O2 has high ZT at 300-700 K, and the maximum ZT is 3.77, corresponding to an energy conversion efficiency of 22.24%. Our results demonstrate that replacement of the transition-metal atom by an appropriate cluster is a good way to obtain a TMO thermoelectric monolayer.

Pan-cancer analysis of TMEM45A and exploration of its prognostic value and mechanism in gastric cancer.

Cancer is a major category of diseases that need to be addressed urgently, bringing a huge burden to the world. Gastric cancer (GC) is a frequent malignant tumor of the digestive system with the highest incidence and mortality rate among all tumors. The purpose of this study was to explore the mechanism of action of TMEM45A in pan-cancer and gastric cancer. First, GEO and TCGA database were employed to analyze the expression of TMEM45A in GC patients. Then, we determined the association between TMEM45A expression and survival of GC patients using the Kaplan-Meier Plotter database and TCGA database and verified the accuracy of TMEM45A in predicting prognosis. Next, we analyzed the effect of CTHRC expression on TIICs in GC tissues. A prognostic model was constructed using immunomodulatory genes associated with TMEM45A. The specificity and accuracy of the model were verified. TMEM45A expression was markedly higher in GC tissue than in normal tissue. GC patients with TMEM45A overexpression had a poor prognosis. The AUC value of 5-year survival on the ROC curve was 0.705, indicating that TMEM45A is a reliable prognostic factor and can be used as a clinicopathological indicator alone to predict patient prognosis. Three high-risk immunomodulatory genes (CXCR4 and TGFB1) and one low-risk immunomodulatory gene (PDCD1) were obtained using both univariate and multivariate COX methods. These three immunomodulatory molecules were used to construct prognostic models. GC patients with TMEM45A overexpression have a poor prognosis and are associated with immune cell infiltration. Hence, TMEM45A is a fairly reliable independent prognostic marker.

Comprehensive evaluation of the distribution, transport and ecological risk of heavy metals in intra-urban river sediments using high-resolution techniques.

Determining the distribution trends, transport mechanisms, and ecological risks of heavy metals (HMs) in urban river sediments is essential for the government to conduct appropriate remediation work. In this study, we collected sediment cores from the Yayao Waterway in Foshan City, China. The vertical distribution profiles of dissolved and labile Fe, Mn, Cd, Zn, Cu, Cr, Ni, Pb, As, and Co in the sediments were obtained using the thin-film diffusive gradient (DGT) and high-resolution peeper (HR-Peeper) techniques. In addition, the transport rates, contamination levels, and ecological concerns of the HMs were evaluated using the European Community Bureau of Reference (BCR) sequential extraction technique, the DGT-induced sediment fluxes (DIFS) model, and multiple contamination evaluation metrics. The results showed that most of the DGT-labile HMs were associated with Fe/Mn (hydrogen) oxides, and in particular, Zn, Ni, and Cr showed a significant negative correlation with Fe/Mn (p < 0.001). Additionally, Cd had the highest bioavailability (89.17%), and its net diffusive flux at the sediment-water interface (SWI) was positive, which indicated a high release risk from the sediment. However, the R-value of Cd based on the DGT-induced sediment fluxes (DIFS) operation was extremely low, suggesting that although Cd had the biggest supply pool of releases, its release rate was slow. The majority of sampling sites had significantly higher total HM contents in the surface sediments than the background values. The HM contamination in the sediments originated from human activities, primarily from industrial enterprises and with a large contribution from both agricultural and domestic sources. The most polluted HM with the highest ecological danger was Cd, followed by Cu, Zn, Ni, and As when the results of the four pollution evaluation indicators were combined. Consequently, the risk of contamination by HMs in inner-city river sediments should receive more attention.

Aurantio­obtusin regulates lipogenesis and ferroptosis of liver cancer cells through inhibiting SCD1 and sensitizing RSL3.

Ferroptosis, characterized by iron­mediated non­apoptotic cell death and alterations in lipid redox metabolism, has emerged as a critical process implicated in various cellular functions, including cancer. Aurantio­obtusin (AO), a bioactive compound derived from Cassiae semen (the dried mature seeds of Cassie obtusifolia L. or Cassia toral L.), has anti­hyperlipidemic and antioxidant properties; however, to the best of our knowledge, the effect of AO on liver cancer cells remains unclear. The Cell Counting Kit­8, EdU staining and migration assays were employed to assess the anti­liver cancer activity of AO. Intracellular levels of glutathione peroxidase 4 protein and lipid peroxidation were measured as indicators of ferroptotic status. Immunohistochemical analyses, bioinformatics analyses and western blotting were conducted to evaluate the potential of stearoyl­CoA desaturase 1 (SCD1) in combination with ferroptosis inducers for the personalized treatment of liver cancer. The present study revealed that AO significantly inhibited the proliferation of liver cancer cells in vitro and in vivo. Mechanistically, AO inhibited AKT/mammalian target of rapamycin (mTOR) signaling, suppressed sterol regulatory element­binding protein 1 (SREBP1) expression, and downregulated fatty acid synthase expression, thereby inhibiting de novo fatty acid synthesis. Further investigations demonstrated that AO suppressed glutathione peroxidase 4 protein expression through the nuclear factor erythroid 2­related factor 2/heme oxygenase­1 pathway, induced ferroptosis in liver cancer cells, and simultaneously inhibited lipogenesis by suppressing SCD1 expression through the AKT/mTOR/SREBP1 pathway. Consequently, this increased the sensitivity of liver cancer cells to the ferroptosis inducer RSL3. Additionally, the enhanced effects of AO and RSL3, which resulted in significant tumor suppression, were confirmed in a xenograft mouse model. In conclusion, the present study demonstrated that AO induced ferroptosis, downregulated the expression of SCD1 and enhanced the sensitivity of liver cancer cells to the ferroptosis inducer RSL3. The synergistic use of AO and a ferroptosis inducer may have promising therapeutic effects in liver cancer cells.

DNA Hyper-methylation Associated With Schizophrenia May Lead to Increased Levels of Autoantibodies.

Background and Hypothesis: Environmental stressors may influence immune surveillance in B lymphocytes and stimulate autoimmune responses via epigenetic DNA methylation modifications in schizophrenia (SCZ). Study Design: A total of 2722, Chinese Han origin subjects were recruited in this study (2005-2011), which included a discovery follow-up cohort with 40 remitters of SCZ (RSCZ), 40 nonremitters of SCZ (NRSCZ), and 40 controls (CTL), and a replication follow-up cohort (64 RSCZ, 16 NRSCZ, and 84 CTL), as well as a case-control validation cohort (1230 SCZ and 1208 CTL). Genomic DNA methylation, target gene mRNA transcripts, and plasma autoantibody levels were measured across cohorts. Study Results: We found extensive differences in global DNA methylation profiles between RSCZ and NRSCZ groups, wherein differential methylation sites (DMS) were enriched with immune cell maturation and activation in the RSCZ group. Out of 2722 participants, the foremost DMS cg14341177 was hyper-methylated in the SCZ group and it inhibited the alternative splicing of its target gene BICD2 and may have increased its autoantigen exposure, leading to an increase in plasma anti-BICD2 IgG antibody levels. The levels of cg14341177 methylation and anti-BICD2 IgG decreased significantly in RSCZ endpoint samples but not in NRSCZ endpoint samples. There are strong positive correlations between cg14341177 methylation, anti-BICD2 IgG, and positive and negative syndrome scale (PANSS) scores in the RSCZ groups, but not in the NRSCZ groups. Conclusions: These data suggest that abnormal DNA methylation could affect autoreactive responses in SCZ, and that cg14341177 methylation and anti-BICD2 IgG levels may potentially serve as useful biomarkers.

Causal relationship between OHSS and immune cells: A Mendelian randomization study.

OBJECTIVE: To confirm the causal relationship between immune cells and Ovarian Hyperstimulation Syndrome. DESIGN: Obtaining data, collecting single nucleotide polymorphisms, detecting instrumental variables heterogeneity, assessing causality, and assessing bidirectional causality. SUBJECTS: A two sample Mendelian study to confirm the causal relationship between immune cells and Ovarian Hyperstimulation Syndrome. EXPOSURE: Immune cell phenotype (including 22 million SNPs from GWAS on 3757 European individuals). MAIN OUTCOME MEASURES: Inverse variance weighting, one-sample analysis, MR-Egger, weighted median and weighted mode are used to assess the causal relationship between 731 immunophenotypes and Ovarian Hyperstimulation Syndrome. The weighted median and Mendelian Randomization multi-effect residuals and Mendelian Randomization multi-effect residuals and outlier tests are used to assess bidirectional causality between this two. RESULTS: After False Discovery Rate correction, 9 immunophenotypes were found to be significantly associated with the risk of Ovarian Hyperstimulation Syndrome. B cell panel: IgD+ AC (OR, 0.90) 、CD19 on CD24+ CD27+ (OR, 0.86) 、BAFF-R on CD20- CD38 (OR, -1.22); Mature T cell group panel: EM DN (CD4 -CD8-) AC (OR, 1.46); Myeloid cell panel: Mo MDSC AC (OR, 1.13) 、CD45 on CD33br HLA-DR+ (OR, 0.87); Monocyte panel: HLA-DR on monocyte (OR, 0.86) 、CCR2 on CD14+ CD16+ monocyte (OR, 1.15) 、cDC panel: HLA-DR on myeloid DC (OR, 0.89). CONCLUSION: This study shows the potential link between OHSS and immune cells by genetic means, providing new ideas for future clinical and basic research.

ResSAT: Enhancing Spatial Transcriptomics Prediction from H&E- Stained Histology Images with Interactive Spot Transformer.

Spatial transcriptomics (ST) revolutionizes RNA quantification with high spatial resolution. Hematoxylin and eosin (H&E) images, the gold standard in medical diagnosis, offer insights into tissue structure, correlating with gene expression patterns. Current methods for predicting spatial gene expression from H&E images often overlook spatial relationships. We introduce ResSAT (Residual networks - Self-Attention Transformer), a framework generating spatially resolved gene expression profiles from H&E images by capturing tissue structures and using a self-attention transformer to enhance prediction.Benchmarking on 10× Visium datasets, ResSAT significantly outperformed existing methods, promising reduced ST profiling costs and rapid acquisition of numerous profiles.

Genomic structural variations link multiple genes to bone mineral density in a multi-ethnic cohort study: Louisiana osteoporosis study.

Osteoporosis, characterized by low BMD, is a highly heritable metabolic bone disorder. Although single nucleotide variations (SNVs) have been extensively studied, they explain only a fraction of BMD heritability. Although genomic structural variations (SVs) are large-scale genomic alterations that contribute to genetic diversity in shaping phenotypic variations, the role of SVs in osteoporosis susceptibility remains poorly understood. This study aims to identify and prioritize genes that harbor BMD-related SVs. We performed whole genome sequencing on 4982 subjects from the Louisiana Osteoporosis Study. To obtain high-confidence SVs, the detection of SVs was performed using an ensemble approach. The SVs were tested for association with BMD variation at the hip (HIP), femoral neck (FNK), and lumbar spine (SPN), respectively. Additionally, we conducted co-occurrence analysis using multi-omics approaches to prioritize the identified genes based on their functional importance. Stratification was employed to explore the sex- and ethnicity-specific effects. We identified significant SV-BMD associations: 125 for FNK-BMD, 99 for SPN-BMD, and 83 for HIP-BMD. We observed SVs that were commonly associated with both FNK and HIP BMDs in our combined and stratified analyses. These SVs explain 13.3% to 19.1% of BMD variation. Novel bone-related genes emerged, including LINC02370, ZNF family genes, and ZDHHC family genes. Additionally, FMN2, carrying BMD-related deletions, showed associations with FNK or HIP BMDs, with sex-specific effects. The co-occurrence analysis prioritized an RNA gene LINC00494 and ZNF family genes positively associated with BMDs at different skeletal sites. Two potential causal genes, IBSP and SPP1, for osteoporosis were also identified. Our study uncovers new insights into genetic factors influencing BMD through SV analysis. We highlight BMD-related SVs, revealing a mix of shared and specific genetic influences across skeletal sites and gender or ethnicity. These findings suggest potential roles in osteoporosis pathophysiology, opening avenues for further research and therapeutic targets.

Interferon-α could induce liver steatosis to promote HBsAg loss by increasing triglyceride level.

Background: The correlation between metabolic syndrome (MetS) and hepatitis B surface antigen (HBsAg) loss remains to be further elucidated, particularly in patients receiving pegylated interferon-α (PEG-IFN) treatment. Methods: 758 patients with low HBsAg quantification who had received nucleos(t)ide analog (NUC) therapy for at least one year and subsequently switched to or add on PEG-IFN therapy over an unfixed course were enrolled. 412 patients were obtained with baseline data matched. A total of 206 patients achieved HBsAg loss (cured group) within 48 weeks. Demographic and biochemical data associated with MetS were gathered for analysis. HepG2.2.15 cell line was used in vitro experiments to validate the efficacy of interferon-α (IFN-α). Results: The proportion of patients with diabetes or hypertension in the uncured group was significantly higher than in the cured group. The levels of fasting blood glucose (FBG) and glycated albumin remained elevated in the uncured group over the 48 weeks. In contrast, the levels of blood lipids and uric acid remained higher in the cured group within 48 weeks. Triglycerides levels and liver steatosis of all patients increased after PEG-IFN therapy. Baseline elevated uric acid levels and hepatic steatosis may be beneficial for HBsAg loss. IFN-α could induce hepatic steatosis and indirectly promote HBsAg loss by increasing triglyceride level through upregulation of acyl-CoA synthetase long-chain family member 1(ACSL1). Conclusions: IFN-α could induce liver steatosis to promote HBsAg loss by increasing triglyceride level through upregulation of ACSL1. Comorbid diabetes may be detrimental to obtaining HBsAg loss with PEG-IFN therapy in CHB patients.