WGCNA reveals a biomarker for cancer-associated fibroblasts to predict prognosis in cervical cancer.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are crucial components of the cervical cancer tumor microenvironment, playing a significant role in cervical cancer progression, treatment resistance, and immune evasion, but whether the expression of CAF-related genes can predict clinical outcomes in cervical cancer is still unknown. In this study, we sought to analyze genes associated with CAFs through weighted gene co-expression network analysis (WGCNA) and to create a predictive model for CAFs in cervical cancer. METHODS: We acquired transcriptome sequencing data and clinical information on cervical cancer patients from the TCGA and GEO databases. Weighted gene co-expression network analysis was conducted to identify genes related to CAFs. We developed a prognostic model based on CAF genes in cervical cancer using LASSO Cox regression analysis. Single-cell sequencing data analysis and in vivo experiments for validation of hub genes in CAFs. RESULTS: A prognostic model for cervical cancer was developed based on CAF genes including COL4A1, LAMC1, RAMP3, POSTN, and SERPINF1. Cervical cancer patients were divided into low and high risk groups based on the optimal cutoff value. Patients in the high risk group had significantly worse prognosis. Single-cell RNA sequencing data revealed that hub genes in the CAFs risk model were expressed mainly in fibroblasts. The real-time fluorescence quantitative PCR results revealed a significant difference in the expression levels of COL4A1, LAMC1, POSTN, and SERPINF1 between the cancer group and the normal group (p < 0.05). Consistently, the results of the immunohistochemical tests exhibited notable variations in COL4A1, LAMC1, RAMP3, POSTN, and SERPINF1 expression between the cancer and normal groups (p < 0.001). CONCLUSION: The CAF risk model for cervical cancer constructed in this study can be used to predict prognosis, while the CAF hub genes can be utilized as crucial markers for cervical cancer prognosis.

Influence of Solvent Selection on the Crystallizability and Polymorphic Selectivity Associated with the Formation of the "Disappeared" Form I Polymorph of Ritonavir.

The comparative crystallizability and polymorphic selectivity of ritonavir, a novel protease inhibitor for the treatment of acquired immune-deficiency syndrome, as a function of solvent selection are examined through an integrated and self-consistent experimental and computational molecular modeling study. Recrystallization at high supersaturation by rapid cooling at 283.15 K is found to produce the metastable "disappeared" polymorphic form I from acetone, ethyl acetate, acetonitrile, and toluene solutions in contrast to ethanol which produces the stable form II. Concomitant crystallization of the other known solid forms is not found under these conditions. Isothermal crystallization studies using turbidometric detection based upon classical nucleation theory reveal that, for an equal induction time, the required driving force needed to initiate solution nucleation decreases with solubility in the order of ethanol, acetone, acetonitrile, ethyl acetate, and toluene consistent with the expected desolvation behavior predicted from the calculated solute solvation free energies. Molecular dynamics simulations of the molecular and intermolecular chemistry reveal the presence of conformational interplay between intramolecular and intermolecular interactions within the solution phase. These encompass the solvent-dependent formation of intramolecular O-H...O hydrogen bonding between the hydroxyl and carbamate groups coupled with differing conformations of the hydroxyl's shielding phenyl groups. These conformational preferences and their relative interaction propensities, as a function of solvent selection, may play a rate-limiting role in the crystallization behavior by not only inhibiting to different degrees the nucleation process but also restricting the assembly of the optimal intermolecular hydrogen bonding network needed for the formation of the stable form II polymorph.

Diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging parameters and serum tumor markers in rectal carcinoma prognosis.

BACKGROUND: Rectal carcinoma (RC), one of the most common malignancies globally, presents an increasing incidence and mortality year by year, especially among young people, which seriously affects the prognosis and quality of life of patients. At present, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters and serum carbohydrate antigen 19-9 (CA19-9) and CA125 Levels have been used in clinical practice to evaluate the T stage and differentiation of RC. However, the accuracy of these evaluation modalities still needs further research. This study explores the application and value of these methods in evaluating the T stage and differentiation degree of RC. AIM: To analyze the diagnostic performance of DCE-MRI parameters combined with serum tumor markers (TMs) in assessing pathological processes and prognosis of RC patients. METHODS: A retrospective analysis was performed on 104 RC patients treated at Yantai Yuhuangding Hospital from May 2018 to January 2022. Patients were categorized into stages T1, T2, T3, and T4, depending on their T stage and differentiation degree. In addition, they were assigned to low (L group) and moderate-high differentiation (M + H group) groups based on their differentiation degree. The levels of DCE-MRI parameters and serum CA19-9 and CA125 in different groups of patients were compared. In addition, the value of DCE-MRI parameters [volume transfer constant (Ktrans), rate constant (Kep), and extravascular extracellular volume fraction (Ve) in assessing the differentiation and T staging of RC patients was discussed. Furthermore, the usefulness of DCE-MRI parameters combined with serum CA19-9 and CA125 Levels in the evaluation of RC differentiation and T staging was analyzed. RESULTS: Ktrans, Ve, CA19-9 and CA125 were higher in the high-stage group and L group than in the low-stage group and M + H Group, respectively (P < 0.05). The areas under the curve (AUCs) of the Ktran and Ve parameters were 0.638 and 0.694 in the diagnosis of high and low stages, respectively, and 0.672 and 0.725 in diagnosing moderate-high and low differentiation, respectively. The AUC of DCE-MRI parameters (Ktrans + Ve) in the diagnosis of high and low stages was 0.742, and the AUC in diagnosing moderate-high and low differentiation was 0.769. The AUCs of CA19-9 and CA-125 were 0.773 and 0.802 in the diagnosis of high and low stages, respectively, and 0.834 and 0.796 in diagnosing moderate-high and low differentiation, respectively. Then, we combined DCE-MRI (Ktrans + Ve) parameters with CA19-9 and CA-125 and found that the AUC of DCE-MRI parameters plus serum TMs was 0.836 in the diagnosis of high and low stages and 0.946 in the diagnosis of moderate-high and low differentiation. According to the Delong test, the AUC of DCE-MRI parameters plus serum TMs increased significantly compared with serum TMs alone in the diagnosis of T stage and differentiation degree (P < 0.001). CONCLUSION: The levels of the DCE-MRI parameters Ktrans and Ve and the serum TMs CA19-9 and CA125 all increase with increasing T stage and decreasing differentiation degree of RC and can be used as indices to evaluate the differentiation degree of RC in clinical practice. Moreover, the combined evaluation of the above indices has a better effect and more obvious clinical value, providing important guiding importance for clinical condition judgment and treatment selection.

Automated Growth Rate Measurement of the Facet Surfaces of Single Crystals of the ß-Form of l-Glutamic Acid Using Machine Learning Image Processing.

Precision measurement of the growth rate of individual single crystal facets (hkl) represents an important component in the design of industrial crystallization processes. Current approaches for crystal growth measurement using optical microscopy are labor intensive and prone to error. An automated process using state-of-the-art computer vision and machine learning to segment and measure the crystal images is presented. The accuracies and efficiencies of the new crystal sizing approach are evaluated against existing manual and semi-automatic methods, demonstrating equivalent accuracy but over a much shorter time, thereby enabling a more complete kinematic analysis of the overall crystallization process. This is applied to measure in situ the crystal growth rates and through this determining the associated kinetic mechanisms for the crystallization of ß-form l-glutamic acid from the solution phase. Growth on the {101} capping faces is consistent with a Birth and Spread mechanism, in agreement with the literature, while the growth rate of the {021} prismatic faces, previously not available in the literature, is consistent with a Burton-Cabrera-Frank screw dislocation mechanism. At a typical supersaturation of σ = 0.78, the growth rate of the {101} capping faces (3.2 × 10-8 m s-1) is found to be 17 times that of the {021} prismatic faces (1.9 × 10-9 m s-1). Both capping and prismatic faces are found to have dead zones in their growth kinetic profiles, with the capping faces (σc = 0.23) being about half that of the prismatic faces (σc = 0.46). The importance of this overall approach as an integral component of the digital design of industrial crystallization processes is highlighted.

Predicting the role of the human gut microbiome in type 1 diabetes using machine-learning methods.

Gut microbes is a crucial factor in the pathogenesis of type 1 diabetes (T1D). However, it is still unclear which gut microbiota are the key factors affecting T1D and their influence on the development and progression of the disease. To fill these knowledge gaps, we constructed a model to find biomarker from gut microbiota in patients with T1D. We first identified microbial markers using Linear discriminant analysis Effect Size (LEfSe) and random forest (RF) methods. Furthermore, by constructing co-occurrence networks for gut microbes in T1D, we aimed to reveal all gut microbial interactions as well as major beneficial and pathogenic bacteria in healthy populations and type 1 diabetic patients. Finally, PICRUST2 was used to predict Kyoto Encyclopedia of Genes and Genomes (KEGG) functional pathways and KO gene levels of microbial markers to investigate the biological role. Our study revealed that 21 identified microbial genera are important biomarker for T1D. Their AUC values are 0.962 and 0.745 on discovery set and validation set. Functional analysis showed that 10 microbial genera were significantly positively associated with D-arginine and D-ornithine metabolism, spliceosome in transcription, steroid hormone biosynthesis and glycosaminoglycan degradation. These genera were significantly negatively correlated with steroid biosynthesis, cyanoamino acid metabolism and drug metabolism. The other 11 genera displayed an inverse correlation. In summary, our research identified a comprehensive set of T1D gut biomarkers with universal applicability and have revealed the biological consequences of alterations in gut microbiota and their interplay. These findings offer significant prospects for individualized management and treatment of T1D.

TMT-labeled quantitative malonylome analysis on the longissimus dorsi muscle of Laiwu pigs reveals the role of ACOT7 in fat deposition.

The Laiwu pig is an indigenous fatty pig breed distributed in North China, characterized by an extremely high level of intramuscular fat (IMF) content (9% âˆ¼ 12%), but the regulatory mechanism underlying intramuscular fat deposition in skeletal muscle is still unknown. In this study, the TMT-labeled quantitative malonylome of the longissimus dorsi muscle in Laiwu pigs at the fastest IMF deposition stage (240 d vs 120 d) was compared to analyze the molecular mechanism of IMF variation in pigs. In Laiwu pigs aged 240 days/120 days, we identified 291 malonylated lysine sites across 188 proteins in the longissimus dorsi muscle. Among these, 38 sites across 31 proteins exhibited differential malonylation. Annotation analysis and enrichment analysis were performed for differentially malonylated proteins (DMPs). These DMPs were mainly clustered into 12 GO functional categories accounting for 5 biological processes, 4 cellular components and 3 molecular functions, and 2 signaling pathways by KEGG enrichment analysis. The function of differentially malonylated protein ACOT7 in the process of fat deposition was further investigated during the differentiation of 3 T3-L1 cells. The results showed that the protein level of ACOT7 in 3 T3-L1 cells decreased but the malonylated level of ACOT7 increased significantly. The malonyl-CoA that is synthesized by ACSF3 affected the malonylation level of ACOT7 in 3 T3-L1 cells. SIGNIFICANCE: The intramuscular fat (IMF) content, by affecting sensory quality traits of meat, such as tenderness, flavor and juiciness, plays an important role in meat quality. Using TMT-based quantitative malonylated proteome analysis, we identified malonylated proteins in LD muscle samples in two stages (120 d and 240 d) of development and further identified differentially malonylated proteins, such as SLC25A4, ANXA5, TPM3 and ACOT7, that are associated with intramuscular fat deposition and fat metabolism in pigs. These differentially malonylated proteins could serve as candidates for elucidating the molecular mechanism of IMF deposition in pigs. In addition, we found that the malonyl-CoA in 3 T3-L1 cells is mainly synthesized by ACSF3, affecting the malonylated level of ACOT7. The study provides some data concerning the role of protein malonylation in regulating the variation in porcine IMF content.

Predicting coronary heart disease in Chinese diabetics using machine learning.

Diabetes, a common chronic disease worldwide, can induce vascular complications, such as coronary heart disease (CHD), which is also one of the main causes of human death. It is of great significance to study the factors of diabetic patients complicated with CHD for understanding the occurrence of diabetes/CHD comorbidity. In this study, by analyzing the risk of CHD in more than 300,000 diabetes patients in southwest China, an artificial intelligence (AI) model was proposed to predict the risk of diabetes/CHD comorbidity. Firstly, we statistically analyzed the distribution of four types of features (basic demographic information, laboratory indicators, medical examination, and questionnaire) in comorbidities, and evaluated the predictive performance of three traditional machine learning methods (eXtreme Gradient Boosting, Random Forest, and Logistic regression). In addition, we have identified nine important features, including age, WHtR, BMI, stroke, smoking, chronic lung disease, drinking and MSP. Finally, the model produced an area under the receiver operating characteristic curve (AUC) of 0.701 on the test samples. These findings can provide personalized guidance for early CHD warning for diabetic populations.

Evolution and interaction relationship of "eco-economy" dual grid pattern in Lingwu City, Ningxia, China.

Strengthening ecological network construction is an important measure to improve urban ecological environment and protect biodiversity. With Lingwu City of Ningxia as an example, based on remote sensing and social and economic data, we comprehensively analyzed the "double network" (ecological network and social and economic landscape) pattern and their relationship by using morphological spatial pattern analysis, Linkage Mapper, and improved gravity model. The results showed that land use/cover changed dramatically from 2000 to 2020 in Lingwu City. The area of sandy land and grassland decreased sharply, that of forest and construction land increased, and industrial zones expanded rapidly. The connectivity and stability of "dual network" were improved. The intensive areas of human activities were distributed in urban area, oasis agricultural areas, and Ningdong energy industrial zone. The ecological source areas and ecological corridors were mainly distributed in the middle of the nature reserve, while some extended to the other three functional areas. The transportation corridor presented an "H" pattern that was dense in oasis agricultural area and Ningdong energy industrial zone, and connected between the two regions. The "double network" were centered around their respective functional area spaces and had interwoven distributions. The trend of the "double network" changed from conflict to coordinated situation, with obvious zoning and interaction in space. The oasis agricultural area and Ningdong energy industrial area destroyed the eco-logy in the early stage and improved that in the later stage. In the future, it is necessary to strengthen the coordination between ecological protection and social and economic development, which could improve ecological quality by combining measures such as improving the quality of ecological sources and corridors, constructing ecological nodes, and repairing ecological disturbance points.

High efficiency removal of organic and inorganic iodine with ferrate[Fe(VI)] through oxidation and adsorption.

I- is a halogen species existing in natural waters, and the transformation of organic and inorganic iodine in natural and artificial processes would impact the quality of drinking water. Herein, it was found that Fe(VI) could oxidize organic and inorganic iodine to IO3-and simultaneously remove the resulted IO3- through Fe(III) particles. For the river water, wastewater treatment plant (WWTP) effluent, and shale gas wastewater treated by 5 mg/L of Fe(VI) (as Fe), around 63 %, 55 % and 71 % of total iodine (total-I) had been removed within 10 min, respectively. Fe(VI) was superior to coagulants in removing organic and inorganic iodine from the source water. Adsorption kinetic analysis suggested that the equilibrium adsorption amount of I- and IO3- were 11 and 10.1 µg/mg, respectively, and the maximum adsorption capacity of IO3- by Fe(VI) resulted Fe(III) particles was as high as 514.7 µg/mg. The heterogeneous transformation of Fe(VI) into Fe(III) effectively improved the interaction probability of IO3- with iron species. Density functional theory (DFT) calculation suggested that the IO3- was mainly adsorbed in the cavity (between the γ-FeOOH shell and γ-Fe2O3 core) of Fe(III) particles through electrostatic adsorption, van der Waals force and hydrogen bond. Fe(VI) treatment is effective for inhibiting the formation of iodinated disinfection by-products in chlor(am)inated source water.

Predicting the Strength of Cohesive and Adhesive Interparticle Interactions for Dry Powder Inhalation Blends of Terbutaline Sulfate with α-Lactose Monohydrate.

Grid-based systematic search methods are used to investigate molecule-molecule, molecule-surface, and surface-surface contributions to interparticle interactions in order to identify the crystal faces that most strongly affect particle behavior during powder blend formulation and delivery processes. The model system comprises terbutaline sulfate (TBS) as an active pharmaceutical ingredient (API) and α-form lactose monohydrate (LMH). A combination of systematic molecular modeling and X-ray computed tomography (XCT) is used to determine not only the adhesive and cohesive interparticle energies but, also the agglomeration behavior during manufacturing and de-agglomeration behavior during delivery after inhalation. This is achieved through a detailed examination of the balance between the adhesive and cohesive energies with the XCT results confirming the blend segregation tendencies, through the particle-particle de-agglomeration process. The results reveal that the cohesive interaction energies of TBS-TBS are higher than the adhesive energies between TBS and LMH, but that the cohesive energies of LMH-LMH are the smallest between molecule and molecule, molecule and surface, and surface and surface. This shows how systematic grid-search molecular modeling along with XCT can guide the digital formulation design of inhalation powders in order to achieve optimum aerosolization and efficacy for inhaled medicines. This will lead to faster pharmaceutical design with less variability, higher quality, and enhanced performance.

The Influence of Solvent Selection upon the Crystallizability and Nucleation Kinetics of Tolfenamic Acid Form II.

The influence of the solution environment on the solution thermodynamics, crystallizability, and nucleation of tolfenamic acid (TFA) in five different solvents (isopropanol, ethanol, methanol, toluene, and acetonitrile) is examined using an integrated workflow encompassing both experimental studies and intermolecular modeling. The solubility of TFA in isopropanol is found to be the highest, consistent with the strongest solvent-solute interactions, and a concomitantly higher than ideal solubility. The crystallizability is found to be highly dependent on the solvent type with the overall order being isopropanol < ethanol < methanol < toluene < acetonitrile with the widest solution metastable zone width in isopropanol (24.49 to 47.41 °C) and the narrowest in acetonitrile (8.23 to 16.17 °C). Nucleation is found to occur via progressive mechanism in all the solvents studied. The calculated nucleation parameters reveal a considerably higher interfacial tension and larger critical nucleus radius in the isopropanol solutions, indicating the higher energy barrier hindering nucleation and hence lowering the nucleation rate. This is supported by diffusion coefficient measurements which are lowest in isopropanol, highlighting the lower molecular diffusion in the bulk of solution compared to the other solutions. The TFA concentration and critical supersaturation at the crystallization onset is found to be directly correlated with TFA/isopropanol solutions having the highest values of solubility and critical supersaturation. Intermolecular modeling of solute-solvent interactions supports the experimental observations of the solubility and crystallizability, highlighting the importance of understanding solvent selection and solution state structure at the molecular level in directing the solubility, solute mass transfer, crystallizability, and nucleation kinetics.

A gender specific risk assessment of coronary heart disease based on physical examination data.

Large-scale screening for the risk of coronary heart disease (CHD) is crucial for its prevention and management. Physical examination data has the advantages of wide coverage, large capacity, and easy collection. Therefore, here we report a gender-specific cascading system for risk assessment of CHD based on physical examination data. The dataset consists of 39,538 CHD patients and 640,465 healthy individuals from the Luzhou Health Commission in Sichuan, China. Fifty physical examination characteristics were considered, and after feature screening, ten risk factors were identified. To facilitate large-scale CHD risk screening, a CHD risk model was developed using a fully connected network (FCN). For males, the model achieves AUCs of 0.8671 and 0.8659, respectively on the independent test set and the external validation set. For females, the AUCs of the model are 0.8991 and 0.9006, respectively on the independent test set and the external validation set. Furthermore, to enhance the convenience and flexibility of the model in clinical and real-life scenarios, we established a CHD risk scorecard base on logistic regression (LR). The results show that, for both males and females, the AUCs of the scorecard on the independent test set and the external verification set are only slightly lower (<0.05) than those of the corresponding prediction model, indicating that the scorecard construction does not result in a significant loss of information. To promote CHD personal lifestyle management, an online CHD risk assessment system has been established, which can be freely accessed at http://lin-group.cn/server/CHD/index.html .

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms.

Diabetes mellitus is a chronic metabolic disease, which causes an imbalance in blood glucose homeostasis and further leads to severe complications. With the increasing population of diabetes, there is an urgent need to develop drugs to treat diabetes. The development of artificial intelligence provides a powerful tool for accelerating the discovery of antidiabetic drugs. This work aims to establish a predictor called iPADD for discovering potential antidiabetic drugs. In the predictor, we used four kinds of molecular fingerprints and their combinations to encode the drugs and then adopted minimum-redundancy-maximum-relevance (mRMR) combined with an incremental feature selection strategy to screen optimal features. Based on the optimal feature subset, eight machine learning algorithms were applied to train models by using 5-fold cross-validation. The best model could produce an accuracy (Acc) of 0.983 with the area under the receiver operating characteristic curve (auROC) value of 0.989 on an independent test set. To further validate the performance of iPADD, we selected 65 natural products for case analysis, including 13 natural products in clinical trials as positive samples and 52 natural products as negative samples. Except for abscisic acid, our model can give correct prediction results. Molecular docking illustrated that quercetin and resveratrol stably bound with the diabetes target NR1I2. These results are consistent with the model prediction results of iPADD, indicating that the machine learning model has a strong generalization ability. The source code of iPADD is available at https://github.com/llllxw/iPADD.

Role of Molecular, Crystal, and Surface Chemistry in Directing the Crystallization of Entacapone Polymorphs on the Au(111) Template Surface.

The pharmaceutical compound entacapone ((E)-2-cyano-3-(3,4-dihydroxy-5-nitrophenyl)-N,N-diethylprop-2-enamide) is important in the treatment of Parkinson's disease, exhibiting interesting polymorphic behavior upon crystallization from solution. It consistently produces its stable form A with a uniform crystal size distribution on the surface of an Au(111) template while concomitantly forming its metastable form D within the same bulk solution. Molecular modeling using empirical atomistic force-fields reveals more complex molecular and intermolecular structures for form D compared to form A, with the crystal chemistry of both polymorphs being dominated by van der Waals and π-π stacking interactions with lower contributions (ca. 20%) from hydrogen bonding and electrostatic interactions. Comparative lattice energies and convergence for the polymorphs are consistent with the observed concomitant polymorphic behavior. Synthon characterization reveals an elongated needle-like morphology for form D crystals in contrast to the more equant form A crystals with the surface chemistry of the latter exposing the molecules' cyano groups on its {010} and {011} habit faces. Density functional theory modeling of surface adsorption reveals preferential interactions between Au and the synthon GA interactions of form A on the Au surface. Molecular dynamics modeling of the entacapone/gold interface reveals the entacapone molecular structure within the first adsorbed layer to show nearly identical interaction distances, for both the molecules within form A or D with respect to the Au surface, while in the second and third layers when entacapone molecule-molecule interactions overtake the interactions between those of molecule-Au, the intermolecular structures are found to be closer to the form A structure than form D. In these layers, synthon GA (form A) could be reproduced with just two small azimuthal rotations (5° and 15°) whereas the closest alignment to a form D synthon requires larger azimuthal rotations (15° and 40°). The cyano functional group interactions with the Au template dominate interfacial interactions with these groups being aligned parallel to the Au surface and with nearest neighbor distances to Au atoms more closely matching those in form A than form D. The overall polymorph direction pathway thus encompasses consideration of molecular, crystal, and surface chemistry factors.

Early autoimmunity and outcome in virus encephalitis: a retrospective study based on tissue-based assay.

To explore the autoimmune response and outcome in the central nervous system (CNS) at the onset of viral infection and correlation between autoantibodies and viruses. METHODS: A retrospective observational study was conducted in 121 patients (2016-2021) with a CNS viral infection confirmed via cerebrospinal fluid (CSF) next-generation sequencing (cohort A). Their clinical information was analysed and CSF samples were screened for autoantibodies against monkey cerebellum by tissue-based assay. In situ hybridisation was used to detect Epstein-Barr virus (EBV) in brain tissue of 8 patients with glial fibrillar acidic protein (GFAP)-IgG and nasopharyngeal carcinoma tissue of 2 patients with GFAP-IgG as control (cohort B). RESULTS: Among cohort A (male:female=79:42; median age: 42 (14-78) years old), 61 (50.4%) participants had detectable autoantibodies in CSF. Compared with other viruses, EBV increased the odds of having GFAP-IgG (OR 18.22, 95% CI 6.54 to 50.77, p<0.001). In cohort B, EBV was found in the brain tissue from two of eight (25.0%) patients with GFAP-IgG. Autoantibody-positive patients had a higher CSF protein level (median: 1126.00 (281.00-5352.00) vs 700.00 (76.70-2899.00), p<0.001), lower CSF chloride level (mean: 119.80±6.24 vs 122.84±5.26, p=0.005), lower ratios of CSF-glucose/serum-glucose (median: 0.50[0.13-0.94] vs 0.60[0.26-1.23], p=0.003), more meningitis (26/61 (42.6%) vs 12/60 (20.0%), p=0.007) and higher follow-up modified Rankin Scale scores (1 (0-6) vs 0 (0-3), p=0.037) compared with antibody-negative patients. A Kaplan-Meier analysis revealed that autoantibody-positive patients experienced significantly worse outcomes (p=0.031). CONCLUSIONS: Autoimmune responses are found at the onset of viral encephalitis. EBV in the CNS increases the risk for autoimmunity to GFAP.

i2OM: Toward a better prediction of 2'-O-methylation in human RNA.

2'-O-methylation (2OM) is an omnipresent post-transcriptional modification in RNAs. It is important for the regulation of RNA stability, mRNA splicing and translation, as well as innate immunity. With the increase in publicly available 2OM data, several computational tools have been developed for the identification of 2OM sites in human RNA. Unfortunately, these tools suffer from the low discriminative power of redundant features, unreasonable dataset construction or overfitting. To address those issues, based on four types of 2OM (2OM-adenine (A), cytosine (C), guanine (G), and uracil (U)) data, we developed a two-step feature selection model to identify 2OM. For each type, the one-way analysis of variance (ANOVA) combined with mutual information (MI) was proposed to rank sequence features for obtaining the optimal feature subset. Subsequently, four predictors based on eXtreme Gradient Boosting (XGBoost) or support vector machine (SVM) were presented to identify the four types of 2OM sites. Finally, the proposed model could produce an overall accuracy of 84.3 % on the independent set. To provide a convenience for users, an online tool called i2OM was constructed and can be freely access at i2om.lin-group.cn. The predictor may provide a reference for the study of the 2OM.

TMEM151A variants associated with paroxysmal kinesigenic dyskinesia.

TMEM151A, located at 11q13.2 and encoding transmembrane protein 151A, was recently reported as causative for autosomal dominant paroxysmal kinesigenic dyskinesia (PKD). Here, through comprehensive analysis of sporadic and familial cases, we expand the clinical and mutation spectrum of PKD. In doing so, we clarify the clinical and genetic features of Chinese PKD patients harboring TMEM151A variants and further explore the relationship between TMEM151A mutations and PKD. Whole exome sequencing was performed on 26 sporadic PKD patients and nine familial PKD pedigrees without PRRT2 variants. Quantitative real-time PCR was used to assess the gene expression of frameshift mutant TMEM151A in a PKD patient. TMEM151A variants reported to date were reviewed. Four TMEM151A variants were detected in four unrelated families with 12 individuals, including a frameshift mutation [c.606_607insA (p.Val203fs)], two missense mutations [c.166G > A (p.Gly56Arg) and c.791T > C (p.Val264Ala)], and a non-pathogenic variant [c.994G > A (p.Gly332Arg)]. The monoallelic frameshift mutation [c.606_607insA (p.Val203fs)] may cause TMEM151A mRNA decay, suggesting a potential pathogenic mechanism of haploinsufficiency. Patients with TMEM151A variants had short-duration attacks and presented with dystonia. Our study provides a detailed clinical description of PKD patients with TMEM151A mutations and reports a new disease-causing mutation, expanding the known phenotypes caused by TMEM151A mutations and providing further detail about the pathoetiology of PKD.

Circ-sh3rf3/GATA-4/miR-29a regulatory axis in fibroblast-myofibroblast differentiation and myocardial fibrosis.

The transdifferentiation from cardiac fibroblasts to myofibroblasts is an important event in the initiation of cardiac fibrosis. However, the underlying mechanism is not fully understood. Circ-sh3rf3 (circular RNA SH3 domain containing Ring Finger 3) is a novel circular RNA which was induced in hypertrophied ventricles by isoproterenol hydrochloride, and our work has established that it is a potential regulator in cardiac hypertrophy, but whether circ-sh3rf3 plays a role in cardiac fibrosis remains unclear, especially in the conversion of cardiac fibroblasts into myofibroblasts. Here, we found that circ-sh3rf3 was down-regulated in isoproterenol-treated rat cardiac fibroblasts and cardiomyocytes as well as during fibroblast differentiation into myofibroblasts. We further confirmed that circ-sh3rf3 could interact with GATA-4 proteins and reduce the expression of GATA-4, which in turn abolishes GATA-4 repression of miR-29a expression and thus up-regulates miR-29a expression, thereby inhibiting fibroblast-myofibroblast differentiation and myocardial fibrosis. Our work has established a novel Circ-sh3rf3/GATA-4/miR-29a regulatory cascade in fibroblast-myofibroblast differentiation and myocardial fibrosis, which provides a new therapeutic target for myocardial fibrosis.

Concentrations, seasonal trends, sources, health risk and subchronic toxicity to the respiratory and immune system of PAHs in PM2.5 in Xi'an.

PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) have been proved to be hazardous to health. Previous studies have focused on the distribution and sources of PAHs, whereas there is little knowledge of the damage to organs. Here we sought to investigate the pollution level and seasonal variation characteristics of PAHs in PM2.5 in Xi'an and assess the health risk, to establish a PAHs exposure model, and investigate the toxicological effects of PAHs on the respiratory and immune functions. A sub-chronic exposure model of PAHs was established by inhalation. The pathological changes of lung tissues were observed with a light microscope. Inflammatory reactions in alveolar lavage fluid were determined using the corresponding kit. The levels of interleukin-6 (IL-6) and interleukin-8 (IL-8) were detected with enzyme linked immunosorbent assay (ELISA) kit; the proliferation of lymphocytes in spleen was detected with methyl tetrazolium (MTT); DNA immune damage was determined with DNA gel electrophoresis. The results showed that (1) the total concentration of 16 PAHs ranged from 41.1 to 387 ng/m3, with a mean value of 170 ng/m3, and the concentration of PAHs in PM2.5 was higher in winter than in other seasons. (2) The sources of PAHs in the atmosphere of Xi'an urban area were mainly coal combustion, and the equivalent carcinogenic concentration of PAHs in PM2.5 was 3.9 ng/m3. (3) Foreign body granuloma formation and inflammatory cell damage were observed in the lungs of rats infected with toxin; the levels of reactive oxygen species (ROS) and mobile device assistant (MDA) increased while nitric oxide synthase (NOS) decreased with the increase of dose; the expression levels of IL-6 and IL-8 elevated with the increase of toxin dose, showing an obvious dose-effect relationship; the level of PAHs damage to cells showed a dose-effect relationship. Sub-chronic exposure to PAHs could cause sustained inflammatory injury to the organism. Measures should be taken to counter the problems of PAHs in PM2.5 in Xi'an and relevant health promotion strategies should be developed.

Prognostic and predictive significance of serum soluble scavenger receptor A in acute primary basal ganglia hemorrhage: A prospective cohort study.

BACKGROUND: Scavenger receptor A (SRA) can regulate immune response and is involved in pathophysiological processes of acute brain injury. We analyzed the prognostic role of serum soluble SRA in intracerebral hemorrhage (ICH). METHODS: In this prospective cohort study of 110 healthy controls and 110 patients with acute basal ganglia hemorrhage, serum soluble SRA concentrations were detected. Univariate analyses, followed by multivariate logistic regression analyses, were utilized to explore the relationship between serum soluble SRA concentrations and early neurologic deterioration (END) plus post-stroke 3-month poor prognosis (modified Rankin Scale scores of 3-6). RESULTS: Serum soluble SRA concentrations of patients were significantly higher than those of controls (median, 3.6 vs 0.9 ng/ml; P < 0.001). Serum soluble SRA concentrations of patients were independently correlated with hematoma volume (ß, 0.201; 95 % confidence interval (CI), 0.093-0.309; P = 0.001), National Institutes of Health Stroke Scale (NIHSS) scores (ß, 0.118; 95 % CI, 0.024-0.213; P = 0.024), and 3-month modified Rankin Scale scores (ß, 0.148; 95 % CI, 0.063-0.232; P = 0.001). Serum soluble SRA concentrations independently predicted END and poor 3-month prognosis with odds ratio values of 1.394 (95 % CI, 1.024-1.899; P = 0.035) and 1.441 (95 % CI, 1.016-2.044; P = 0.040) respectively. Serum soluble SRA concentrations were efficiently predictive of the development of END (ROC AUC 0.746; 95 % CI, 0.631-0.861) and poor 3-month prognosis (AUC, 0.773; 95 % CI, 0.685-0.861). Serum soluble SRA concentrations significantly improved AUCs of NIHSS score and hematoma volume to 0.889 (95 % CI, 0.829-0.948; P = 0.035) and 0.873 (95 % CI, 0.811-0.936; P = 0.036) for prognostic prediction. The END predictive ability of serum sSRA concentrations combined with NIHSS score and ICH volume (AUC, 0.900; 95 % CI, 0.835-0.965) was significantly superior to those of NIHSS score (P = 0.020) and hematoma volume (P = 0.022). The prognostic predictive capability of serum sSRA concentrations combined with NIHSS score and ICH volume (AUC, 0.907; 95 % CI, 0.852-0.962) substantially exceeded those of NIHSS score (P = 0.009) and hematoma volume (P = 0.005). CONCLUSIONS: Serum soluble SRA concentrations may reflect illness severity and neurologic function after ICH, indicating serum soluble SRA may serve as a promising prognostic biochemical marker of ICH.