PNPLA3 is a triglyceride lipase that mobilizes polyunsaturated fatty acids to facilitate hepatic secretion of large-sized very low-density lipoprotein.

The I148M variant of PNPLA3 is closely associated with hepatic steatosis. Recent evidence indicates that the I148M mutant functions as an inhibitor of PNPLA2/ATGL-mediated lipolysis, leaving the role of wild-type PNPLA3 undefined. Despite showing a triglyceride hydrolase activity in vitro, PNPLA3 has yet to be established as a lipase in vivo. Here, we show that PNPLA3 preferentially hydrolyzes polyunsaturated triglycerides, mobilizing polyunsaturated fatty acids for phospholipid desaturation and enhancing hepatic secretion of triglyceride-rich lipoproteins. Under lipogenic conditions, mice with liver-specific knockout or acute knockdown of PNPLA3 exhibit aggravated liver steatosis and reduced plasma VLDL-triglyceride levels. Similarly, I148M-knockin mice show decreased hepatic triglyceride secretion during lipogenic stimulation. Our results highlight a specific context whereby the wild-type PNPLA3 facilitates the balance between hepatic triglyceride storage and secretion, and suggest the potential contribution of a loss-of-function by the I148M variant to the development of fatty liver disease in humans.

Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction.

OBJECTIVES: Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications. MATERIALS AND METHODS: A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions. RESULTS: Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032). DISCUSSION: These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions. CONCLUSIONS: Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.

Molecular weight control of poly-γ-glutamic acid reveals novel insights into extracellular polymeric substance synthesis in Bacillus licheniformis.

BACKGROUND: The structural diversity of extracellular polymeric substances produced by microorganisms is attracting particular attention. Poly-gamma-glutamic acid (γ-PGA) is a widely studied extracellular polymeric substance from Bacillus species. The function of γ-PGA varies with its molecular weight (Mw). RESULTS: Herein, different endogenous promoters in Bacillus licheniformis were selected to regulate the expression levels of pgdS, resulting in the formation of γ-PGA with Mw values ranging from 1.61 × 103 to 2.03 × 104 kDa. The yields of γ-PGA and exopolysaccharides (EPS) both increased in the pgdS engineered strain with the lowest Mw and viscosity, in which the EPS content was almost tenfold higher than that of the wild-type strain. Subsequently, the compositions of EPS from the pgdS engineered strain also changed. Metabolomics and RT-qPCR further revealed that improving the transportation efficiency of EPS and the regulation of carbon flow of monosaccharide synthesis could affect the EPS yield. CONCLUSIONS: Here, we present a novel insight that increased pgdS expression led to the degradation of γ-PGA Mw and changes in EPS composition, thereby stimulating EPS and γ-PGA production. The results indicated a close relationship between γ-PGA and EPS in B. licheniformis and provided an effective strategy for the controlled synthesis of extracellular polymeric substances.

CircBCL2L13 attenuates cardiomyocyte oxidative stress and apoptosis in cardiac ischemia‒reperfusion injury via miR-1246/PEG3 signaling.

Ischemia‒reperfusion (I/R) is a common complication in the clinical treatment of acute myocardial infarction (MI), in which cardiomyocytes play a pivotal role in the recovery of cardiac function after reperfusion injury. The expression of numerous circular ribonucleic acids (circRNAs) is disrupted in I/R-induced cardiac damage, but the potential role of circRNAs in I/R damage has not been fully elucidated. The purpose of the present study was to clarify the biological action and molecular mechanism of circRNA 002166 (also termed circCL2L13) in postmyocardial I/R. Oxygen-glucose deprivation/reoxygenation (OGD/R) in an in vivo model was performed to simulate I/R damage. real-time polymerase chain reaction analysis was also conducted to evaluate the relationships of the SOD1, SOD2, NRF2, HO1 and GPX4 indicators with oxidative stress injury. TUNEL immunofluorescence was used to evaluate the degree of cardiomyocyte apoptosis in the different treatment groups. The circBCL2L13 level was markedly upregulated in myocardial tissues from a mouse I/R model. Overexpression of circBCL2L13 markedly attenuated the expression of oxidative stress-related genes and apoptosis in OGD/R-induced cardiomyocytes. A mechanistic study revealed that circBCL2L13 functions as a ceRNA for miR-1246 and modulates paternally expressed gene 3 (PEG3). Eventually, circBCL2L13 was proven to regulate PEG3 by targeting miR-1246, thereby protecting against OGD/R-induced cardiomyocyte oxidative damage and apoptosis. In conclusion, our study confirmed that the circBCL2L13/miR-1246/PEG3 axis suppressed the progression of OGD/R injury in cardiomyocytes, which might lead to new therapeutic strategies for cardiac I/R injury.

 Mazusjiangshiense (Mazaceae), a new species from China: evidence from morphological and molecular analyses.

Utilising both morphological and molecular analyses, this study unveils Mazusjiangshiensesp. nov., a novel addition to the Mazaceae family, discovered in Shaowu County, Fujian Province, China. The comprehensive description and illustrations provided here are a result of a meticulous exploration of its morphological features. While bearing a resemblance to M.gracilis, this new-found species is distinguished by three distinct characteristics: its stems are delicately soft, its leaves possess a membranous quality and the ovary is notably villous at the apex. Integration of molecular evidence, derived from the nuclear ribosomal DNA (nrITS) and three plastid DNA sequences (rps16, rbcL and trnL-trnF), unequivocally supports the classification of M.jiangshiense as a distinct species. Notably, the molecular analysis positions it as a sister species to M.spicatus, underscoring the phylogenetic relationships within the genus Mazus. Our research not only introduces M.jiangshiense as a novel taxonomic entity, but also provides a nuanced understanding of its morphological differences and molecular affinities, enriching our comprehension of the diversity and evolutionary relationships of Mazaceae.

Effect of Mn/Ag Ratio on Microstructure and Mechanical Properties of Heat-Resistant Al-Cu Alloys.

This paper mainly investigated the effect of the Mn/Ag ratio on the microstructure and room temperature and high-temperature (350 °C) tensile mechanical properties of the as-cast and heat-treated Al-6Cu-xMn-yAg (x + y = 0.8, wt.%) alloys. The as-cast alloy has α-Al, Al2Cu, and a small amount of Al7Cu2 (Fe, Mn) and Al20Cu2 (Mn, Fe)3 phases. After T6 heat treatment, a massive dispersive and fine θ'-Al2Cu phase (100~400 nm) is precipitated from the matrix. The Mn/Ag ratio influences the quantity and size of the precipitates; when the Mn/Ag ratio is 1:1, the θ'-Al2Cu precipitation quantity reaches the highest and smallest. Compared with the as-cast alloy, the tensile strength of the heat-treated alloy at room temperature and high temperature is greatly improved. The strengthening effect of the alloy is mainly attributed to the nanoparticles precipitated from the matrix. The Mn/Ag ratio also affects the high-temperature tensile mechanical properties of the alloy. The high-temperature tensile strength of the alloy with a 1:1 Mn/Ag ratio is the highest, reaching 135.89 MPa, 42.95% higher than that of the as-cast alloy. The analysis shows that a synergistic effect between Mn and Ag elements can promote the precipitation and refinement of the θ'-Al2Cu phase, and there is an optimal ratio (1:1) that obtains the lowest interfacial energy for co-segregation of Mn and Ag at the θ'/Al interface that makes θ'-Al2Cu have the best resistance to coarsening.

Circ_BLNK is a Unique Molecular Marker in Non-small Cell Lung Cancer.

Non-small cell lung cancer (NSCLC) patients are characterized by distant metastasis and poor prognosis. Growing evidence has implied that circular RNAs (circRNAs) are involved in multiple tumor progression, including NSCLC. The objective of the present study was to functionally dissect the role and mechanism of circ_BLNK in NSCLC development and progression. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect the expression of circ_BLNK, miR-942-5p, and forkhead box protein O1 (FOXO1) in NSCLC tissues and cells. 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay, 5-ethynyl-2'-deoxyuridine (EdU) assay and colony formation assay detected cell proliferation; the protein expression levels were tested by western blot assay; cell apoptosis was measured by flow cytometry, and transwell assay detected cell migration and invasion. The molecular targeting relationship was determined by dual-luciferase reporter assay. The effect of circ_BLNK overexpression on tumor growth was detected by in vivo experiments and immunohistochemistry. Circ_BLNK was dramatically decreased in NSCLC, and overexpression of circ_BLNK inhibited proliferation, migration, and invasion of NSCLC cells and promoted cell apoptosis. Circ_BLNK level was negatively correlated with miR-942-5p expression and positively correlated with FOXO1 expression. Moreover, circ_BLNK acted as a sponge for miR-942-5p, which targeted FOXO1. Rescue assays presented that miR-942-5p reversed the anticancer action of circ_BLNK in NSCLC. Besides that, miR-942-5p inhibition suppressed the oncogenic behaviors, which were attenuated by FOXO1 knockdown. Animal experiments exhibited that circ_BLNK upregulation repressed tumor growth in vivo. Our study demonstrated a novel regulatory mechanism that circ_BLNK/miR-942-5p/FOXO1 axis adjusted non-small cell lung cancer development.

Replacement of saturated fatty acids with linoleic acid in western diet attenuates atherosclerosis in a mouse model with inducible ablation of hepatic LDL receptor.

Dietary saturate fatty acids (SFAs) have been consistently linked to atherosclerosis and obesity, both of which are characterized by chronic inflammation and impaired lipid metabolism. In comparison, the effects of linoleic acid (LA), the predominant polyunsaturated fatty acid in the Western diet, seem to diverge. Data from human studies suggest a positive association between high dietary intake of LA and the improvement of cardiovascular risk. However, excessive LA intake has been implicated in the development of obesity. Concerns have also been raised on the potential pro-inflammatory properties of LA metabolites. Herein, by utilizing a mouse model with liver-specific Ldlr knockdown, we directly determined the effects of replacing SFAs with LA in a Western diet on the development of obesity and atherosclerosis. Specifically, mice treated with a Ldlr ASO were placed on a Western diet containing either SFA-rich butter (WD-B) or LA-rich corn oil (WD-CO) for 12 weeks. Despite of showing no changes in body weight gain or adiposity, mice on WD-CO exhibited significantly less atherosclerotic lesions compared to those on WD-B diet. Reduced lesion formation in the WD-CO-fed mice corresponded with a reduction of plasma triglyceride and cholesterol content, especially in VLDL and LDL, and ApoB protein levels. Although it increased expression of proinflammatory cytokines TNF-α and IL-6 in the liver, WD-CO did not appear to affect hepatic injury or damage when compared to WD-B. Collectively, our results indicate that replacing SFAs with LA in a Western diet could reduce the development of atherosclerosis independently of obesity.

Substrate-Specific Function of PNPLA3 Facilitates Hepatic VLDL-Triglyceride Secretion During Stimulated Lipogenesis.

The I148M variant of PNPLA3 is strongly linked to hepatic steatosis. Evidence suggests a gain-of-function role for the I148M mutant as an ATGL inhibitor, leaving the physiological relevance of wild-type PNPLA3 undefined. Here we show that PNPLA3 selectively degrades triglycerides (TGs) enriched in polyunsaturated fatty acids (PUFAs) independently of ATGL in cultured cells and mice. Lipidomics and metabolite tracing analyses demonstrated that PNPLA3 mobilizes PUFAs from intracellular TGs for phospholipid desaturation, supporting hepatic secretion of TG-rich lipoproteins. Consequently, mice with liver-specific knockout or acute knockdown of PNPLA3 both exhibited aggravated liver steatosis and concomitant decreases in plasma VLDL-TG, phenotypes that manifest only under lipogenic conditions. I148M-knockin mice similarly displayed impaired hepatic TG secretion during lipogenic stimulation. Our results highlight a specific context whereby PNPLA3 facilitates the balance between hepatic TG storage and secretion and suggest the potential contributions of I148M variant loss-of-function to the development of hepatic steatosis in humans. Summary Statement: We define the physiological role of wild type PNPLA3 in maintaining hepatic VLDL-TG secretion.

Artificial Intelligence-based Efficacy Prediction of Phase 3 Clinical Trial for Repurposing Heart Failure Therapies.

Introduction: Drug repurposing involves finding new therapeutic uses for already approved drugs, which can save costs as their pharmacokinetics and pharmacodynamics are already known. Predicting efficacy based on clinical endpoints is valuable for designing phase 3 trials and making Go/No-Go decisions, given the potential for confounding effects in phase 2. Objectives: This study aims to predict the efficacy of the repurposed Heart Failure (HF) drugs for the Phase 3 Clinical Trial. Methods: Our study presents a comprehensive framework for predicting drug efficacy in phase 3 trials, which combines drug-target prediction using biomedical knowledgebases with statistical analysis of real-world data. We developed a novel drug-target prediction model that uses low-dimensional representations of drug chemical structures and gene sequences, and biomedical knowledgebase. Furthermore, we conducted statistical analyses of electronic health records to assess the effectiveness of repurposed drugs in relation to clinical measurements (e.g., NT-proBNP). Results: We identified 24 repurposed drugs (9 with a positive effect and 15 with a non-positive) for heart failure from 266 phase 3 clinical trials. We used 25 genes related to heart failure for drug-target prediction, as well as electronic health records (EHR) from the Mayo Clinic for screening, which contained over 58,000 heart failure patients treated with various drugs and categorized by heart failure subtypes. Our proposed drug-target predictive model performed exceptionally well in all seven tests in the BETA benchmark compared to the six cutting-edge baseline methods (i.e., best performed in 266 out of 404 tasks). For the overall prediction of the 24 drugs, our model achieved an AUCROC of 82.59% and PRAUC (average precision) of 73.39%. Conclusion: The study demonstrated exceptional results in predicting the efficacy of repurposed drugs for phase 3 clinical trials, highlighting the potential of this method to facilitate computational drug repurposing.

Using artificial intelligence to learn optimal regimen plan for Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurological disorder with no specific curative medications. Sophisticated clinical skills are crucial to optimize treatment regimens given the multiple coexisting comorbidities in the patient population. OBJECTIVE: Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians' decisions for AD patients based on the longitude data from electronic health records. METHODS: In this study, we selected 1736 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We focused on the two most frequent concomitant diseases-depression, and hypertension, thus creating 5 data cohorts (ie, Whole Data, AD, AD-Hypertension, AD-Depression, and AD-Depression-Hypertension). We modeled the treatment learning into an RL problem by defining states, actions, and rewards. We built a regression model and decision tree to generate multiple states, used six combinations of medications (ie, cholinesterase inhibitors, memantine, memantine-cholinesterase inhibitors, hypertension drugs, supplements, or no drugs) as actions, and Mini-Mental State Exam (MMSE) scores as rewards. RESULTS: Given the proper dataset, the RL model can generate an optimal policy (regimen plan) that outperforms the clinician's treatment regimen. Optimal policies (ie, policy iteration and Q-learning) had lower rewards than the clinician's policy (mean -3.03 and -2.93 vs. -2.93, respectively) for smaller datasets but had higher rewards for larger datasets (mean -4.68 and -2.82 vs. -4.57, respectively). CONCLUSIONS: Our results highlight the potential of using RL to generate the optimal treatment based on the patients' longitude records. Our work can lead the path towards developing RL-based decision support systems that could help manage AD with comorbidities.

LncRNA GSCAR promotes glioma stem cell maintenance via stabilizing SOX2 expression.

Gliomas are the most aggressive type of malignant brain tumors. Recent studies have demonstrated that the existence of glioma stem cells (GSCs) is critical for glioma recurrence, metastasis, and chemo- or radio-therapy resistance. Temozolomide (TMZ) has been used as an initial therapy for gliomas. However, the overall survival time is still limiting due to the lack of effective targets and treatment options. Therefore, identifying novel biomarkers for gliomas, especially for GSCs, is important to improve the clinical outcome in the future. In this study, we identify a human-specific long non-coding RNA (lncRNA, ENSG00000250377), termed GSCAR (glioma stem cell associated lncRNA), which is highly expressed in glioma cancerous tissues and cell lines. We reveal that GSCAR positively correlates with tumor grade. Glioma patients with GSCAR high expression exhibit shortened overall survival time, compared to patients with GSCAR low expression. Furthermore, we show that GSCAR knockdown by shRNAs or antisense oligonucleotide (ASO) reduces tumor cell proliferation, migration and xenograft tumor formation abilities. Mechanistic study shows that GSCAR acts as a ceRNA (competing endogenous RNA) for miR-6760-5p to promote the expression of oncogene SRSF1 (serine and arginine rich splicing factor 1). In addition, GSCAR mediates the protein complex formation between DHX9 (DExH-Box helicase 9) and IGF2BP2 (insulin-like growth factor 2 mRNA-binding protein 2), leading to the stabilization of SOX2 (sex-determining region Y-box 2) mRNA and then the transcriptional activation of GSCAR. Depleting GSCAR reduces SOX2 expression and GSC self-renewal ability, but promotes tumor cell responses to TMZ. These findings uncover that GSCAR/miR-6760-5p/SRSF1 axis and GSCAR/DHX9-IGF2BP2/SOX2 positive feedback loop are critical for glioma progression, which could be used as prognostic biomarkers and therapeutic targets in the future.

IKZF4 acts as a novel tumor suppressor in non-small cell lung cancer by suppressing Notch signaling pathway.

Non-small cell lung cancer (NSCLC) is the predominant cause of cancer-related mortality globally, although many clinical efforts have been developed to improve the outcomes. The Ikaros zing-finger family transcription factors (IKZFs) have been proved to play pivotal roles in lymphopoiesis and myeloma progression, but their roles in solid tumors development remain unclear. We performed integrative bioinformatical analysis to determine the dysregulation expression of IKZFs in multiple tumors and the correlation between IKZF4 and NSCLC tumor environment. We showed that IKZFs were dysregulated in multiple tumors and IKZF4 was significantly decreased in NSCLC tissues and cell lines due to promoter hypermethylation. We found that low IKZF4 expression obviously correlated with patients' poor clinical outcome. We revealed that IKZF4 overexpression inhibited NSCLC cell growth, migration and xenograft tumor growth, supporting the inhibitory role of IKZF4 in NSCLC tumorigenesis. Additionally, integrative bioinformatical analysis showed that IKZF4 was involved in NSCLC tumor microenvironment. Mechanically, RNA-seq results showed that IKZF4 forced-expression remarkably suppressed Notch signaling pathway in NSCLC, which was validated by qRT-PCR and immunoblot assays. Moreover, we screened several potential agonists for IKZF4.

HILPDA promotes NASH-driven HCC development by restraining intracellular fatty acid flux in hypoxia.

BACKGROUND & AIMS: The prevalence of non-alcoholic steatohepatitis (NASH)-driven hepatocellular carcinoma (HCC) is rising rapidly, yet its underlying mechanisms remain unclear. Herein, we aim to determine the role of hypoxia-inducible lipid droplet associated protein (HILPDA)/hypoxia-inducible gene 2 (HIG2), a selective inhibitor of intracellular lipolysis, in NASH-driven HCC. METHODS: The clinical significance of HILPDA was assessed in human NASH-driven HCC specimens by immunohistochemistry and transcriptomics analyses. The oncogenic effect of HILPDA was assessed in human HCC cells and in 3D epithelial spheroids upon exposure to free fatty acids and either normoxia or hypoxia. Lipidomics profiling of wild-type and HILPDA knockout HCC cells was assessed via shotgun and targeted approaches. Wild-type (Hilpdafl/fl) and hepatocyte-specific Hilpda knockout (HilpdaΔHep) mice were fed a Western diet and high sugar in drinking water while receiving carbon tetrachloride to induce NASH-driven HCC. RESULTS: In patients with NASH-driven HCC, upregulated HILPDA expression is strongly associated with poor survival. In oxygen-deprived and lipid-loaded culture conditions, HILPDA promotes viability of human hepatoma cells and growth of 3D epithelial spheroids. Lack of HILPDA triggered flux of polyunsaturated fatty acids to membrane phospholipids and of saturated fatty acids to ceramide synthesis, exacerbating lipid peroxidation and apoptosis in hypoxia. The apoptosis induced by HILPDA deficiency was reversed by pharmacological inhibition of ceramide synthesis. In our experimental mouse model of NASH-driven HCC, HilpdaΔHep exhibited reduced hepatic steatosis and tumorigenesis but increased oxidative stress in the liver. Single-cell analysis supports a dual role of hepatic HILPDA in protecting HCC cells and facilitating the establishment of a pro-tumorigenic immune microenvironment in NASH. CONCLUSIONS: Hepatic HILPDA is a pivotal oncometabolic factor in the NASH liver microenvironment and represents a potential novel therapeutic target. IMPACT AND IMPLICATIONS: Non-alcoholic steatohepatitis (NASH, chronic metabolic liver disease caused by buildup of fat, inflammation and damage in the liver) is emerging as the leading risk factor and the fastest growing cause of hepatocellular carcinoma (HCC), the most common form of liver cancer. While curative therapeutic options exist for HCC, it frequently presents at a late stage when such options are no longer effective and only systemic therapies are available. However, systemic therapies are still associated with poor efficacy and some side effects. In addition, no approved drugs are available for NASH. Therefore, understanding the underlying metabolic alterations occurring during NASH-driven HCC is key to identifying new cancer treatments that target the unique metabolic needs of cancer cells.

Using Artificial Intelligence to Learn Optimal Regimen Plan for Alzheimer's Disease.

Background: Alzheimer's Disease (AD) is a progressive neurological disorder with no specific curative medications. While only a few medications are approved by FDA (i.e., donepezil, galantamine, rivastigmine, and memantine) to relieve symptoms (e.g., cognitive decline), sophisticated clinical skills are crucial to optimize the appropriate regimens given the multiple coexisting comorbidities in this patient population. Objective: Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians' decisions for AD patients based on the longitude records from Electronic Health Records (EHR). Methods: In this study, we withdraw 1,736 patients fulfilling our criteria, from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database. We focused on the two most frequent concomitant diseases, depression, and hypertension, thus resulting in five main cohorts, 1) whole data, 2) AD-only, 3) AD-hypertension, 4) AD-depression, and 5) AD-hypertension-depression. We modeled the treatment learning into an RL problem by defining the three factors (i.e., states, action, and reward) in RL in multiple strategies, where a regression model and a decision tree are developed to generate states, six main medications extracted (i.e., no drugs, cholinesterase inhibitors, memantine, hypertension drugs, a combination of cholinesterase inhibitors and memantine, and supplements or other drugs) are for action, and Mini-Mental State Exam (MMSE) scores are for reward. Results: Given the proper dataset, the RL model can generate an optimal policy (regimen plan) that outperforms the clinician's treatment regimen. With the smallest data samples, the optimal-policy (i.e., policy iteration and Q-learning) gained a lesser reward than the clinician's policy (mean -2.68 and -2.76 vs . -2.66, respectively), but it gained more reward once the data size increased (mean -3.56 and -2.48 vs . -3.57, respectively). Conclusions: Our results highlight the potential of using RL to generate the optimal treatment based on the patients' longitude records. Our work can lead the path toward the development of RL-based decision support systems which could facilitate the daily practice to manage Alzheimer's disease with comorbidities.

Predicting Physiological Response in Heart Failure Management: A Graph Representation Learning Approach using Electronic Health Records.

Heart failure management is challenging due to the complex and heterogenous nature of its pathophysiology which makes the conventional treatments based on the "one size fits all" ideology not suitable. Coupling the longitudinal medical data with novel deep learning and network-based analytics will enable identifying the distinct patient phenotypic characteristics to help individualize the treatment regimen through the accurate prediction of the physiological response. In this study, we develop a graph representation learning framework that integrates the heterogeneous clinical events in the electronic health records (EHR) as graph format data, in which the patient-specific patterns and features are naturally infused for personalized predictions of lab test response. The framework includes a novel Graph Transformer Network that is equipped with a self-attention mechanism to model the underlying spatial interdependencies among the clinical events characterizing the cardiac physiological interactions in the heart failure treatment and a graph neural network (GNN) layer to incorporate the explicit temporality of each clinical event, that would help summarize the therapeutic effects induced on the physiological variables, and subsequently on the patient's health status as the heart failure condition progresses over time. We introduce a global attention mask that is computed based on event co-occurrences and is aggregated across all patient records to enhance the guidance of neighbor selection in graph representation learning. We test the feasibility of our model through detailed quantitative and qualitative evaluations on observational EHR data.

The safety and efficacy of alfentanil combined with midazolam in fiberoptic bronchoscopy sedation: A randomized, double-blind, controlled trial.

Sedation is recommended by most guidelines to be offered to all patients undergoing diagnostic flexible bronchoscopy (DFB) without contraindications, and the most commonly reported regimen is midazolam in combination with a short-acting opioid (fentanyl or alfentanil) to provide both sedative and antitussive effects. However, the optimal dose or ideal regimen of the combination therapy with midazolam and opioids has not yet been found. So this randomized, double-blinded clinical trial was designed and registered (ChiCTR2100049052) to assess the safety and efficacy of midazolam combined with different doses of alfentanil in DFB sedation. Our study showed that relative high doses of alfentanil (10-25 µg/kg) combined with a fixed low dose of midazolam can markedly reduce hemodynamic fluctuations, cough reactions, patients' discomforts, and improve their satisfaction in a dose-dependent manner during DFB, with no significant increase in the desaturation risks.

Identification of motifs and mechanisms for lipid droplet targeting of the lipolytic inhibitors G0S2 and HIG2.

G0S2 and HIG2 are two selective inhibitors of ATGL (also known as PNPLA2), the key enzyme for intracellular lipolysis. Whereas G0S2 regulates triglyceride (TG) mobilization in adipocytes and hepatocytes, HIG2 functions to enhance intracellular TG accumulation under hypoxic conditions. A homologous hydrophobic domain (HD) is shared by G0S2 and HIG2 (also known as HILPDA) for binding to ATGL. However, the determinants of their lipid droplet (LD) localization are unknown. Here, we study how G0S2 and HIG2 are targeted to LDs, and identify both ATGL-independent and -dependent mechanisms. Structural prediction and studies in cells reveal that ATGL-independent localization of G0S2 to both the endoplasmic reticulum (ER) and LDs is mediated by a hairpin structure consisting of two hydrophobic sequences. Positively charged residues in the hinge region play a crucial role in sorting G0S2, which initially localizes to ER, to LDs. Interestingly, the role of these positive charges becomes dispensable when ATGL is co-expressed. In comparison, HIG2, which lacks a similar hairpin structure, is dependent on ATGL for its full LD targeting. Thus, our studies identify specific structural features and mechanisms for mediating accumulation of these two ATGL inhibitors on LDs.

MrgprF acts as a tumor suppressor in cutaneous melanoma by restraining PI3K/Akt signaling.

The incidence of cutaneous melanoma (CM) has been increasing annually worldwide. In this study, we identify that MrgprF, a MAS related GPR family member, is decreased in cutaneous melanoma tissues and cell lines due to hypermethylation of its promoter region, and show that patients with CM expressing high levels of MrgprF exhibit an improved clinical outcome. We demonstrate that MrgprF forced expression inhibits tumor cell proliferation, migration, xenograft tumor growth, and metastasis. On the contrary, MrgprF knockdown promotes tumor cell proliferation and transformation of immortalized human keratinocyte-HaCaT cells, supporting the inhibitory role of MrgprF during tumor progression. Mechanistic studies reveal that MrgprF reduces the phosphoinositol­3­kinase (PI3K) complex formation between p101 and p110γ subunits, the critical step for phosphatidylinositol-(3, 4)-P2 (PIP2) conversion to phosphatidylinositol-(3, 4, 5)-P3 (PIP3), and then reduces the activation of PI3K/Akt signaling. This effect can be reversed by Akt specific agonist SC79. In addition, AMG 706, a previously documented inhibitor for endothelial cell proliferation, is identified as a potential agonist for MrgprF, and can impede tumor growth both in vitro and in vivo. Taken together, our findings suggest that MrgprF, a novel tumor suppressor in cutaneous melanoma, may be useful as a therapeutic target in the future.

Exosomes Participate in the Radiotherapy Resistance of Cancers.

Radiotherapy is one of the main treatment modalities for cancer. However, some cancer patients will gradually develop resistance to radiotherapy, leading to tumor recurrence and metastasis. Radiation therapy usually promotes the secretion of exosomes from tumor cells and causes changes in their internal components. Accumulating evidence reveals that exosomes-mediated radiation-induced bystander effect (RIBE) is closely involved in radiotherapy resistance. In this article, we will discuss the relationship between exosomes and RIBE, highlight the effect of exosome components on resistance to radiation, and emphasize the role of exosome inclusion as a tumor biomarker for the prognosis of tumor treatment to develop new therapeutic approaches.