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"Can order spring from Chaos?" is the title of an extensive Report on Italian science published by NATURE on 12 May 1983 and written by Robert Walgate, the Chief European Correspondent. It is a twenty pages complete paper touching all aspects of the struggle of Italian scientists to work in the "curious amalgam of ingenuity and muddle, a reflection of the political system". (Nature, 1983; 303: 109–128). To read it after four decades is interesting but somewhat depressing since the main problems unfolded in the paper have not been solved, starting with the largely insufficient support of fundamental curiosity driven research. At page 114 you could find a item called: ITALY's TOP SCIENTISTS: Four in the top one thousand. The Author refers to the data reported by the ISI (Institute of Scientific Information) that took two years to scan 3,000 major journals over the period 1965–78 and covered 5 millions articles and 67 millions references. The four top Italian scientists working in Italy were: Eraldo Antonini (3127 citations), Enrico Clementi (4001), Silvio Garattini (2833), and Giorgio Giacomelli (2483); 3 out of four were 52 years old, and one 55. Antonini did not see the Report since he passed away on March 18, 1983. However the information leaked before the publication of Nature because I remember the Messaggero of Rome reporting a whole page with the ranking of the four Italians, and even a picture of Eraldo. The students of the first year Medical course, his Class, welcomed the Professor with a standing ovation. After a short time the Board of the SIB (Società Italiana di Biochimica) casted a unanimous vote in favour of the motion of President Noris Siliprandi to begin the annual Congress with an Antonini Lecture, forever. As reported below, the tradition began immediately at the Congress in Saint-Vicent, Italy, and is continuing. In this paper I report an account of the Eraldo Antonini Lectures that I attended over the years and until September 2019, a few months before the pandemics lock down. [ABSTRACT FROM AUTHOR]

Background: Traditional problem-based learning (PBL) relying on tutored learning in small groups is very resource-intensive. Little is known about the benefits of PBL in a large classroom setting. This paper introduced a PBL case into the traditional didactic biochemistry course and investigated the acceptability of total online or partial online PBL in a large classroom setting introduced during the coronavirus pandemic. Methods: The students were allocated into either total online Group 1, partial online Group 2, or partial online and with poorer academic performance Group 3. A questionnaire comprising of 8 closed-ended questions and 2 open-ended questions and final exam performances were used to evaluate the acceptability of total online or partial online PBL in a large classroom setting. The 8 closed-ended questions were analysed by the Kruskal–Wallis test or chi-square tests. The word cloud analysis of the 2 open-ended questions were conducted by Wenjuanxing. Students' performances in the final examination were analysed by One-way Anova. Results: Both total online and partial online PBL were rated highly by the students. Overall, there were no significant differences in the effectiveness evaluation of PBL between Group 2 and Group 3. There were no significant differences in final exam performances between Group 1 and Group 2. However, Group 1 rated the effectiveness of PBL much higher than Group 2 and 3. Word cloud analysis of the 2 open-ended questions showed students' positive perspectives of PBL. In biochemistry teaching, from the perspective of the students, the expected optimal number of useful PBL cases might be 2. Conclusions: Both total online and partial online PBL in a large classroom setting were widely accepted as a beneficial supplement to traditional biochemistry classes. [ABSTRACT FROM AUTHOR]

Background: Cigarette smoking remains the leading cause of preventable disease and death in the United States. Primary care offers an ideal setting to reach adults who smoke cigarettes and improve uptake of evidence-based cessation treatment. Although U.S. Preventive Services Task Force Guidelines recommend the 5As model (Ask, Advise, Assess, Assist, Arrange) in primary care, there are many barriers to its implementation. Automated, comprehensive, and proactive tools are needed to overcome barriers. Our team developed and preliminarily evaluated a proactive electronic visit (e-visit) delivered via the Electronic Health Record patient portal to facilitate evidence-based smoking cessation treatment uptake in primary care, with promising initial feasibility and efficacy. This paper describes the rationale, design, and protocol for an ongoing Hybrid Type I effectiveness-implementation trial that will simultaneously assess effectiveness of the e-visit intervention for smoking cessation as well as implementation potential across diverse primary care settings. Methods: The primary aim of this remote five-year study is to examine the effectiveness of the e-visit intervention vs. treatment as usual (TAU) for smoking cessation via a clinic-randomized clinical trial. Adults who smoke cigarettes are recruited across 18 primary care clinics. Clinics are stratified based on their number of primary care providers and randomized 2:1 to either e-visit or TAU. An initial baseline e-visit gathers information about patient smoking history and motivation to quit, and a clinical decision support algorithm determines the best evidence-based cessation treatment to prescribe. E-visit recommendations are evaluated by a patient's own provider, and a one-month follow-up e-visit assesses cessation progress. Main outcomes include: (1) cessation treatment utilization (medication, psychosocial cessation counseling), (2) reduction in cigarettes per day, and (3) biochemically verified 7-day point prevalence abstinence (PPA) at six-months. We hypothesize that patients randomized to the e-visit condition will have better cessation outcomes (vs. TAU). A secondary aim evaluates e-visit implementation potential at patient, provider, and organizational levels using a mixed-methods approach. Implementation outcomes include acceptability, adoption, fidelity, implementation cost, penetration, and sustainability. Discussion: This asynchronous, proactive e-visit intervention could provide substantial benefits for patients, providers, and primary care practices and has potential to widely improve reach of evidence-based cessation treatment. Trial registration: NCT05493254. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA), characterized by inflammation and articular cartilage degradation, is a prevalent arthritis among geriatric population. This paper was to scrutinize the novel mechanism of long noncoding RNA (lncRNA) NEAT1 in OA etiology. Methods: A total of 10 OA patients and 10 normal individuals was included in this study. Cell model of OA was built in human normal chondrocytes induced by lipopolysaccharide (LPS). An OA Wistar rat model was established through intra-articular injection of L-cysteine and papain mixtures (proportion at 1:2) into the right knee. Quantitative reverse transcription-polymerase chain reaction was employed to ascertain the expression levels of NEAT1, microRNA (miR)-374b-5p and post-GPI attachment to protein 1 (PGAP1), while dual-luciferase reporter experiments were used for the validation of target relationship among them. Cell cycle and apoptosis were calculated by flow cytometry analysis. CCK-8 assay was done to evaluate the proliferative potentials of chondrocytes. The levels of cell cycle-related proteins (Cyclin A1, Cyclin B1 and Cyclin D2) and pro-apoptotic proteins (Caspase3 and Caspase9) were measured by western blotting. Tumor necrosis factor-alpha (TNF-α), interleukin-1beta (IL-1β) and IL-6 levels were determined via ELISA. Hematoxylin & eosin (HE) Staining was used for pathological examination in OA rats. Results: Pronounced downregulation of NEAT1 and PGAP1 and high amounts of miR-374b-5p were identified in OA patients, LPS-induced chondrocytes and OA rats. NEAT1 targeted miR-374b-5p to control PGAP1 expression. Loss of NEAT1 or upregulation of miR-374b-5p dramatically accelerated apoptosis, led to the G1/S arrest and promoted the secretion of inflammatory cytokines in LPS-induced chondrocytes, while ectopic expression of PGAP1 exhibited the opposite influences on chondrocytes. Additionally, we further indicated that upregulation of miR-374b-5p attenuated the effects of PGAP1 overexpression on LPS-induced chondrocytes. Conclusions: Reduced NEAT1 induces the development of OA via miR-374b-5p/PGAP1 pathway. This suggests that the regulatory axis NEAT1/miR-374b-5p/PGAP1 is a novel and prospective target for OA treatment. [ABSTRACT FROM AUTHOR]

Background: In recent years, mitochondrial dysfunction has been extensively studied and published, but research on the effects of mitochondrial dysfunction on bone metabolism and related diseases is only just beginning. Furthermore, no studies have been carried out to systematically illustrate this area from a scientometric point of view. The goal of this research is to review existing knowledge and identify new trends and possible hotspots in this area.Methods: All publications related to the relationship between mitochondrial dysfunction and bone metabolism and related diseases from 2003 to 2022 were searched at the Web of Science Core Collection (WoSCC) on May 7, 2022. Four different analytical tools: VOSviewer 1.6.18, CiteSpace V 6.1, HistorCite (12.03.07), and Excel 2021 were used for the scientometric research.Results: The final analysis included 555 valid records in total. Journal of Biological Chemistry (Co-citations = 916) is the most famous journal in this field. China (Percentage = 37%), the United States (Percentage = 24%), and Korea (Percentage = 12%) are the most productive countries. Blanco FJ and Choi EM are the main researchers with significant academic influence. Current research hotspots are basic research on mitochondrial dysfunction and the prevention or treatment of bone metabolism-related diseases.Conclusion: The study of the consequences of mitochondrial dysfunction on bone metabolism and associated diseases is advancing rapidly. Several prominent researchers have published extensive literature and are widely cited. Future research in this area will focus on oxidative stress, aging, gene expression, and the pathogenesis of bone metabolism-related diseases. [ABSTRACT FROM AUTHOR]

Background: Several local communities in Central, Western, Eastern, and Northern regions of Uganda have been using the whole leaf extracts of Aloe vera (L.) Burm. f. (Asphodelaceae) in the treatment of various ailments. Also, several commercial companies sell A. vera as soft drinks in Uganda. However, there are inadequate reports on the toxicities of such preparations. This paper reports the acute and sub-acute oral toxicity of aqueous extracts of whole leaf and green rind of A. vera in Wistar rats. Methods: Acute oral toxicity test was carried out in female Wistar rats at doses of 175, 550, 1750, and 5000 mg/kg, p.o. The animals were observed for signs of toxicity for 14 days. Similarly, a sub-acute oral toxicity test was performed in both sexes of rats at doses of 200, 400, and 800 mg/kg, p.o. daily for 28 days. All the groups of animals were monitored for behavioral, morphological, biochemical, and physiological changes, including mortality and compared with respective controls. Body weights were measured weekly while the animals' relative organ weights, hematological, biochemical, gross, and microscopic pathology were examined on day 29. Results: There was no mortality or apparent behavioral changes at the doses tested in acute and sub-acute oral toxicity tests. Thus, the Median Lethal Dose (LD50) of green rind and whole leaf aqueous extracts was above 5000 mg/kg. Gross anatomy revealed that the rats' relative spleen weight in green rind extract at 200 mg/kg significantly decreased compared to the control group. The creatinine levels in female rats that received green rind extract and the chloride ion levels in male rats administered whole leaf extract were significantly elevated. Conversely, Mean Corpuscular Hemoglobin Concentration (MCHC) levels significantly decreased at lower doses of the green rind extract compared to the control. Histopathology of the kidney revealed the renal interstitium's inflammation at doses of 200 and 800 mg/kg of the whole leaf extract. Conclusion: The findings demonstrated that A. vera green rind and whole leaf extracts are non-toxic at relatively high doses when used for a short duration. Prolonged use of the aqueous whole leaf extract might be associated with kidney toxicity. [ABSTRACT FROM AUTHOR]

Filifoliumsibiricum (L.)Kitam (F.sibiricum), a compositae plant, is especially used to inhibit drug-resistant bacteria in folk medicine. Modern pharmacological studies also confirmed a variety of pharmacological properties about sedative activities, antibacterial activity, anti-inflammatory activity, analgesic activities, antitussive and asthma relieving. In this paper, the research progress of F.sibiricum in botany, ethnopharmacology, phytochemistry, pharmacology, pharmacokinetics and toxicology was reviewed. Prospects for future investigation and application of this herb were also discussed. Information on F.sibiricum was gathered from various sources, including books on traditional Chinese herbal medicine and scientific databases such as PubMed, Google Scholar, Science Direct, Baidu Scholar, CNKI and other professional websites. The results indicate that ~ 66 chemical compounds were isolated and identified from F.sibiricum. Among them, flavonoids are generally considered to be the main bioactive and characteristic ingredients. F.sibiricum is a traditional Chinese medicine with pharmacological activities such as the immune system, nervous system, respiratory system and cardiovascular and cerebrovascular systems. Most importantly, we should concentrate on developing new drugs related to F.sibiricum, so as to exert greater potential for treatment. [ABSTRACT FROM AUTHOR]

Background: In this paper, we use: i) formalised anatomical knowledge of connectivity between body structures and ii) a formal theory of physiological transport between fluid compartments in order to define and make explicit the routes followed by proteins to a site of interaction. The underlying processes are the objects of mathematical models of physiology and, therefore, the motivation for the approach can be understood as using knowledge representation and reasoning methods to propose concrete candidate routes corresponding to correlations between variables in mathematical models of physiology. In so doing, the approach projects physiology models onto a representation of the anatomical and physiological reality which underpins them. Results: The paper presents a method based on knowledge representation and reasoning for eliciting physiological communication routes. In doing so, the paper presents the core knowledge representation and algorithms using it in the application of the method. These are illustrated through the description of a prototype implementation and the treatment of a simple endocrine scenario whereby a candidate route of communication between ANP and its receptors on the external membrane of smooth muscle cells in renal arterioles is elicited. The potential of further development of the approach is illustrated through the informal discussion of a more complex scenario. Conclusions: The work presented in this paper supports research in intercellular communication by enabling knowledge-based inference on physiologically-related biomedical data and models. [ABSTRACT FROM AUTHOR]

Background: Estimating the rate constants of a biochemical reaction system with known stoichiometry from noisy time series measurements of molecular concentrations is an important step for building predictive models of cellular function. Inference techniques currently available in the literature may produce rate constant values that defy necessary constraints imposed by the fundamental laws of thermodynamics. As a result, these techniques may lead to biochemical reaction systems whose concentration dynamics could not possibly occur in nature. Therefore, development of a thermodynamically consistent approach for estimating the rate constants of a biochemical reaction system is highly desirable. Results: We introduce a Bayesian analysis approach for computing thermodynamically consistent estimates of the rate constants of a closed biochemical reaction system with known stoichiometry given experimental data. Our method employs an appropriately designed prior probability density function that effectively integrates fundamental biophysical and thermodynamic knowledge into the inference problem. Moreover, it takes into account experimental strategies for collecting informative observations of molecular concentrations through perturbations. The proposed method employs a maximization-expectation-maximization algorithm that provides thermodynamically feasible estimates of the rate constant values and computes appropriate measures of estimation accuracy. We demonstrate various aspects of the proposed method on synthetic data obtained by simulating a subset of a well-known model of the EGF/ERK signaling pathway, and examine its robustness under conditions that violate key assumptions. Software, coded in MATLAB®, which implements all Bayesian analysis techniques discussed in this paper, is available free of charge at http://www.cis.jhu.edu/∼goutsias/CSS%20lab/ software.html. Conclusions: Our approach provides an attractive statistical methodology for estimating thermodynamically feasible values for the rate constants of a biochemical reaction system from noisy time series observations of molecular concentrations obtained through perturbations. The proposed technique is theoretically sound and computationally feasible, but restricted to quantitative data obtained from closed biochemical reaction systems. This necessitates development of similar techniques for estimating the rate constants of open biochemical reaction systems, which are more realistic models of cellular function. [ABSTRACT FROM AUTHOR]

Background: The role of dietary fat quality in promotion of cardiovascular diseases is studies before. However, the results are inconsistent. Recently, cholesterol to saturated fatty acid index (CSI) is suggested as a novel indicator of the atherogenicity and thrombogenicity potential of a diet. However, due to limited number of studies, in the current cross-sectional study, we aimed to evaluate the role of CSI in metabolic and inflammatory response among obese individuals. Methods: In the current cross-sectional study 488 obese individuals aged 18–50 years old were involved in volunteer based invitation from outpatient obesity clinics. Subjects underwent anthropometric assays including weight, height, waist circumference (WC) and body composition and their fasting blood sample were obtained for biochemical assessments including blood sugar, serum lipids, hs-CRP and IL-6 concentrations by commercial kits. Physical activity was also assessed by short form of international physical activity questionnaire (IPAQ). Results: According to our results, being at the top tetile of CSI was associated with higher anthropometric indices including weight, height, WC, FFM, and basal metabolic rate (BMR) compared with those at the lowest tertile (P < 0.05). Similarly, those at the highest category of CSI had significantly higher levels of serum glucose and hs-CRP both in crude and adjusted models in ANCOVA and in multinomial logistic regression models (P < 0.05). Conclusion: In the current study, for the first time, we identified the possible triggering role of dietary cholesterol to saturated fat index in increasing serum glucose and hs-CRP levels. due to cross-sectional design of the current study, causal inference is impossible. Further studies will help for better scientific justification. [ABSTRACT FROM AUTHOR]

Background: Obesity is a global health concern associated with increased risk of diseases like cardiovascular conditions including ischemic heart disease, a leading cause of mortality. The ketogenic diet (KD) has potential therapeutic applications in managing obesity and related disorders. However, the intricate effects of KD on diverse physiological conditions remain incompletely understood. The PI3K-Akt signaling pathway is critical for heart health, and its dysregulation implicates numerous cardiac diseases. Methods: We developed comprehensive mathematical models of the PI3K-Akt signaling pathway under high-fat diet (HFD) and KD conditions to elucidate their differential impacts and quantify apoptosis. Simulations and sensitivity analysis were performed. Results: Simulations demonstrate that KD can reduce the activation of key molecules like Erk and Trp53 to mitigate apoptosis compared to HFD. Findings align with experimental data, highlighting the potential cardiac benefits of KD. Sensitivity analysis identifies regulators like Trp53 and Bcl2l1 that critically influence apoptosis under HFD. Conclusions: Mathematical modeling provides quantitative insights into the contrasting effects of HFD and KD on cardiac PI3K-Akt signaling and apoptosis. Findings have implications for precision nutrition and developing novel therapeutic strategies to address obesity-related cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Background: Natural compounds can positively impact health, and various studies suggest that they regulate glucose‒lipid metabolism by influencing short-chain fatty acids (SCFAs). This metabolism is key to maintaining energy balance and normal physiological functions in the body. This review explores how SCFAs regulate glucose and lipid metabolism and the natural compounds that can modulate these processes through SCFAs. This provides a healthier approach to treating glucose and lipid metabolism disorders in the future. Methods: This article reviews relevant literature on SCFAs and glycolipid metabolism from PubMed and the Web of Science Core Collection (WoSCC). It also highlights a range of natural compounds, including polysaccharides, anthocyanins, quercetins, resveratrols, carotenoids, and betaines, that can regulate glycolipid metabolism through modulation of the SCFA pathway. Results: Natural compounds enrich SCFA-producing bacteria, inhibit harmful bacteria, and regulate operational taxonomic unit (OTU) abundance and the intestinal transport rate in the gut microbiota to affect SCFA content in the intestine. However, most studies have been conducted in animals, lack clinical trials, and involve fewer natural compounds that target SCFAs. More research is needed to support the conclusions and to develop healthier interventions. Conclusions: SCFAs are crucial for human health and are produced mainly by the gut microbiota via dietary fiber fermentation. Eating foods rich in natural compounds, including fruits, vegetables, tea, and coarse fiber foods, can hinder harmful intestinal bacterial growth and promote beneficial bacterial proliferation, thus increasing SCFA levels and regulating glucose and lipid metabolism. By investigating how these compounds impact glycolipid metabolism via the SCFA pathway, novel insights and directions for treating glucolipid metabolism disorders can be provided. [ABSTRACT FROM AUTHOR]

Background: MiR-221, acting as onco-miR or oncosuppressor-miR, plays an important role in tumor progression; however, the prognostic value of miR-221 in human carcinomas is controversial and inconclusive. The objective of our study was to conducted a systematic review and meta-analysis of miR-221 in various types of human cancers.Methods: An online search of up-to-date electronic databases, including PubMed and Embase, was conducted to identify as many relevant papers as possible. 32 papers involving 3041 patients with different carcinomas were included in the analysis. Hazard ratios (HRs) of miR-221 were used to evaluate prognostic values.Results: Thirty-two papers involving 15 cancers were included. MiR-221 was associated with a worse overall survival (OS) in patients, and a combined HR was 1.93 (95% CI of 1.43-2.60, 2080 patients, 22 studies, I-squared = 80.4%, P = 0.000); however, the combined HR for relapse-free survival (RFS) was 1.37 (95% CI of 0.75-2.48, 625 patients, 7 studies, I-squared = 78.8%, P = 0.000), and disease-free survival (DFS) was 1.24 (95% CI of 0.60-2.56, 539 patients, 5 studies, I-squared = 81.8%, P = 0.000).Conclusion: MiR-221 was shown to be associated with a poor OS in human carcinomas, and thus may serve as a useful predictor of clinical outcomes. [ABSTRACT FROM AUTHOR]

Background: Identifying protein functional sites (PFSs) and, particularly, the physicochemical interactions at these sites is critical to understanding protein functions and the biochemical reactions involved. Several knowledge-based methods have been developed for the prediction of PFSs; however, accurate methods for predicting the physicochemical interactions associated with PFSs are still lacking. Results: In this paper, we present a sequence-based method for the prediction of physicochemical interactions at PFSs. The method is based on a functional site and physicochemical interaction-annotated domain profile database, called fiDPD, which was built using protein domains found in the Protein Data Bank. This method was applied to 13 target proteins from the very recent Critical Assessment of Structure Prediction (CASP10/11), and our calculations gave a Matthews correlation coefficient (MCC) value of 0.66 for PFS prediction and an 80% recall in the prediction of the associated physicochemical interactions. Conclusions: Our results show that, in addition to the PFSs, the physical interactions at these sites are also conserved in the evolution of proteins. This work provides a valuable sequence-based tool for rational drug design and side-effect assessment. The method is freely available and can be accessed at http://202.119.249.49. [ABSTRACT FROM AUTHOR]

Background: Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect. Results: We have developed a novel prediction method called INSPiRE which benefits from a knowledge base built from data available in Protein Data Bank. All proteins involved in PPIs were converted into labeled graphs with nodes corresponding to amino acids and edges to pairs of neighboring amino acids. A structural neighborhood of each node was then encoded into a bit string and stored in the knowledge base. When predicting PPIs, INSPiRE labels amino acids of unknown proteins as interface or non-interface based on how often their structural neighborhood appears as interface or non-interface in the knowledge base. We evaluated INSPiRE's behavior with respect to different types and sizes of the structural neighborhood. Furthermore, we examined the suitability of several different features for labeling the nodes. Our evaluations showed that INSPiRE clearly outperforms existing methods with respect to Matthews correlation coefficient. Conclusion: In this paper we introduce a new knowledge-based method for identification of protein-protein interaction sites called INSPiRE. Its knowledge base utilizes structural patterns of known interaction sites in the Protein Data Bank which are then used for PPI prediction. Extensive experiments on several well-established datasets show that INSPiRE significantly surpasses existing PPI approaches. [ABSTRACT FROM AUTHOR]

Background: Seed aging, a natural and inevitable process occurring during storage. Oats, an annual herb belonging to the Gramineae family and pooideae. In addition to being a healthy food, oats serve as ecological pastures, combating soil salinization and desertification. They also play a role in promoting grassland agriculture and supplementing winter livestock feed. However, the high lipid and fat derivatives contents of oat seeds make them susceptible to deterioration, as fat derivatives are prone to rancidity, affecting oat seed production, storage, development, and germplasm resource utilization. Comparative studies on the effects of aging on physiology and cytological structure in covered and naked oat seeds are limited. Thus, our study aimed to determine the mechanism underlying seed deterioration in artificially aged 'LongYan No. 3' (A. sativa) and 'BaiYan No. 2' (A. nuda) seeds, providing a basis for the physiological evaluation of oat seed aging and serving as a reference for scientifically safe storage and efficient utilization of oats. Results: In both oat varieties, superoxide dismutase and catalase activities in seeds showed increasing and decreasing trends, respectively. Variance analysis revealed significant differences and interaction in all measured indicators of oat seeds between the two varieties at different aging times. 'LongYan No. 3' seeds, aged for 24–96 h, exhibited a germination rate of < 30%, Conductivity, malondialdehyde, soluble sugar, and soluble protein levels increased more significantly than the 'BaiYan No. 2'. With prolonged aging leading to cell membrane degradation, reactive oxygen species accumulation, disrupted antioxidant enzyme system, evident embryo cell swelling, and disordered cell arrangement, blocking the nutrient supply route. Simultaneously, severely concentrated chromatin in the nucleus, damaged mitochondrial structure, and impaired energy metabolism were noted, resulting in the loss of 'LongYan No. 3' seed vitality and value. Conversely, 'BaiYan No. 2' seeds showed a germination rate of 73.33% after 96 h of aging, consistently higher antioxidant enzyme activity during aging, normal embryonic cell shape, and existence of the endoplasmic reticulum. Conclusions: ROS accumulation and antioxidant enzyme system damage in aged oat seeds, nuclear chromatin condensation, mitochondrial structure damage, nucleic acid metabolism and respiration weakened, oat seed vigor decreased. 'LongYan No. 3' seeds were more severely damaged under artificial aging than 'BaiYan No. 2' seeds, highlighting their heightened susceptibility to aging effects. [ABSTRACT FROM AUTHOR]

Background: The study aimed to explore the relationship between low-density lipoprotein cholesterol (LDL-C) genetic variants and obstructive sleep apnea (OSA) and its complications, including cardiovascular diseases (CVD), insulin resistance (IR), and metabolic syndrome (MS). Method: 4329 individuals with suspected OSA who underwent a comprehensive assessment of anthropometric, biochemical, and polysomnography (PSG) data, along with 30 LDL-C single nucleotide polymorphisms (SNPs) were enrolled. The 10-year Framingham CVD risk score (FRS), IR and MS were evaluated for each subject. Linear regression and logistic regression were utilized to examine the correlations among these variables. Results: After the Benjamini-Hochberg correction, linear regression results indicated positive correlations between variants rs3741297 and rs629301 with FRS (β = 0.031, PBH=0.002; β = 0.026, PBH=0.015). Logistic regression revealed that rs3741297 increased MS risk among total subjects [OR = 1.67 (95% CI:1.369–2.038), PBH=1.32 × 10− 5] and increased IR risk in females [OR = 3.475 (95% CI:1.653–7.307), PBH=0.03]. In males, rs2642438 decreased MS risk [OR = 0.81 (95% CI:0.703–0.933), PBH=0.045]. Conclusions: The rs3741297 variant correlated with susceptibility to CVD, IR, and MS in the OSA population. OSA, CVD, IR and MS share a potentially common genetic background, which may promote precision medicine. Cinical trial registration: The study protocol was registered with the Chinese Clinical Trial Registry (ChiCTR1900025714). [ABSTRACT FROM AUTHOR]

Background: Shift work has been identified as a risk factor for several chronic health conditions including obesity. This study evaluated the impact of a low-calorie meal replacement (MR) as a dinner substitute on body composition and metabolic parameters in shift workers with overweight and obesity. Methods: An 8-week parallel, randomized controlled trial was conducted on overweight and obese shift workers in a large hospital. An intervention group (IG) (n = 25) was provided with a low-calorie MR shake (∼200 kcal) as a replacement for dinner, every day for 8 weeks, while the control group (CG) (n = 25) continued their habitual diet. Anthropometric measurements, body composition, biochemical, and lifestyle data were assessed at the first and last visits. Analyses were done per protocol (PP) and by intention to treat (ITT). Results: Over the study duration, both groups displayed moderate changes in anthropometric measurements and body composition, although these were not statistically significant according to the PP analysis. In the ITT analysis, apart from the hip circumference (HC), all other anthropometric parameters demonstrated significant group and time interactions, suggesting the advantageous effects of the meal replacement over the study period (P < 0.05). HDL and VLDL cholesterol measures showed significant main effects, influenced by both group (P = 0.031) and time (P = 0.050) respectively. The most pronounced dietary shift in the IG was a reduction in carbohydrate consumption and an increase in protein intake. Throughout the study, the meal replacement was well-tolerated, with no adverse events reported. Conclusions: The meal replacement dietary intervention appears to offer beneficial health effects over time. Extended research is crucial to understand the broader implications of meal replacements across diverse populations. Trial registration: Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12622000231741. Registered on 09 February 2022. https://www.anzctr.org.au/ACTRN12622000231741.aspx. [ABSTRACT FROM AUTHOR]

Introduction: Stromal cell-derived factor-1 (SDF-1) is a newly discovered small molecule adipocytokine, and research has shown that it is closely related to the occurrence and development of obesity. However, there are currently few research reports on SDF-1 in childhood obesity and nonalcoholic fatty liver disease (NAFLD), and this study aims to explore the relationship between SDF-1 and obesity related indicators in obese children. Methods: Serum SDF-1 concentrations were measured using enzyme-linked immunosorbent assay (ELISA). Clinical and biochemical data were collected, such as body mass index (BMI), waist and hip circumference, blood pressure, liver enzymes, cholesterol, and fasting insulin. Children with NAFLD or not were evaluated through Color Doppler Ultrasound. Results: Serum SDF-1 concentrations were significantly higher in obese subjects than in non-obese subjects (P < 0.05), and were elevated in the NAFLD obese subjects than in the non-NAFLD obese subjects (P < 0.05). SDF-1 was positively correlated with BMI, waist-to-hip ratio, systolic blood pressure, body fat percentage (BFP), basal metabolic rate (BMR), alanine transaminase (ALT), aspartate transaminase (AST), glutyltranspeptidase (GT), and homoeostasis model of HOMA-IR, independent of their uric acid (UA), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), very-low-density lipoprotein (VLDL), gender and age. BFP and BMR were associated with the serum SDF-1 concentrations in multivariable linear regression analysis. Conclusion: These results suggest that SDF-1 levels are elevated in obese children and are associated with NAFLD, indicating that SDF-1 may play a role in the development of childhood obesity and metabolic disorders. [ABSTRACT FROM AUTHOR]

We review key research papers in cardiology and intensive care published during 2010 in Critical Care and quote related studies published in other journals if appropriate. Papers were grouped into the following categories: cardiovascular therapies, biomarkers, hemodynamic monitoring, cardiovascular diseases, and microcirculation. [ABSTRACT FROM AUTHOR]

Background: The most widely used multiple sequence alignment methods require sequences to be clustered as an initial step. Most sequence clustering methods require a full distance matrix to be computed between all pairs of sequences. This requires memory and time proportional to N² for N sequences. When N grows larger than 10,000 or so, this becomes increasingly prohibitive and can form a significant barrier to carrying out very large multiple alignments. Results: In this paper, we have tested variations on a class of embedding methods that have been designed for clustering large numbers of complex objects where the individual distance calculations are expensive. These methods involve embedding the sequences in a space where the similarities within a set of sequences can be closely approximated without having to compute all pair-wise distances. Conclusions: We show how this approach greatly reduces computation time and memory requirements for clustering large numbers of sequences and demonstrate the quality of the clusterings by benchmarking them as guide trees for multiple alignment. Source code is available for download from http://www.clustal.org/mbed.tgz. [ABSTRACT FROM AUTHOR]

Background: Topological descriptors, other graph measures, and in a broader sense, graph-theoretical methods, have been proven as powerful tools to perform biological network analysis. However, the majority of the developed descriptors and graph-theoretical methods does not have the ability to take vertex- and edge-labels into account, e.g., atom- and bond-types when considering molecular graphs. Indeed, this feature is important to characterize biological networks more meaningfully instead of only considering pure topological information. Results: In this paper, we put the emphasis on analyzing a special type of biological networks, namely biochemical structures. First, we derive entropic measures to calculate the information content of vertex- and edgelabeled graphs and investigate some useful properties thereof. Second, we apply the mentioned measures combined with other well-known descriptors to supervised machine learning methods for predicting Ames mutagenicity. Moreover, we investigate the influence of our topological descriptors - measures for only unlabeled vs. measures for labeled graphs - on the prediction performance of the underlying graph classification problem. Conclusions: Our study demonstrates that the application of entropic measures to molecules representing graphs is useful to characterize such structures meaningfully. For instance, we have found that if one extends the measures for determining the structural information content of unlabeled graphs to labeled graphs, the uniqueness of the resulting indices is higher. Because measures to structurally characterize labeled graphs are clearly underrepresented so far, the further development of such methods might be valuable and fruitful for solving problems within biological network analysis. [ABSTRACT FROM AUTHOR]

Background: John Cunningham virus (JCV) constitutes a family of polyoma viruses, which plays important roles in the progressive multifocal leukoencephalopathy (PML) and tumorigenesis. However, no bibliometric investigation has been reported to guide the researchers and potential readers. Methods: Papers were collected from database Sci-expanded and Pubmed until May 22, 2008. The highly-productive authors, institutes and countries, highly-cited authors and journals were ranked. The highly-cited articles were subjected to co-citation and chronological analysis with highly-frequent MeSH words for co-occurrence analysis. Results: Until now, 1785 articles about JCV were indexed in Sci-expanded and 1506 in Pubmed. The main document type was original article. USA, Japan and Italy were the largest three producers about JCV. Temple University published 128 papers and ranked the top, followed by University of Tokyo. Khalili K and Yogo Y became the core authors due to more than 20 documents produced. Journal of Neurovirology published more than 15 papers and ranked the top. Padgett BL and Berger JR were the first two highly-cited authors. Journal of Virology and Journal of Neurovirology respectively ranked to the first two highly-cited journals. These top highly-cited articles were divided into 5 aspects: (1) The correlation between JC virus and tumors; (2) Causal correlation of JCV with PML; (3) Polyoma virus infection and its related diseases in renal-allograft recipients; (4) Detection of JCV antibody, oncogene and its encoding protein; (5) Genetics and molecular biology of JCV. The MeSH/subheadings were classified into five groups: (1) JCV and virus infectious diseases; (2) JCV pathogenicity and pathological appearance of PML; (3) JCV isolation and detection; (4) Immunology of JCV and PML; (5) JCV genetics and tumors. Conclusion: JCV investigation mainly focused on its isolation and detection, as well as its correlation with PML and tumors. Establishment of transgenic animal model using JCV T antigen would be a hopeful and useful project in the further study. [ABSTRACT FROM AUTHOR]

Background: Flap endonuclease 1 (FEN1) is a structure-specific nuclease that plays a role in a variety of DNA metabolism processes. FEN1 is important for maintaining genomic stability and regulating cell growth and development. It is associated with the occurrence and development of several diseases, especially cancers. There is a lack of systematic bibliometric analyses focusing on research trends and knowledge structures related to FEN1.Purpose: To analyze hotspots, the current state and research frontiers performed for FEN1 over the past 15 years.Methods: Publications were retrieved from the Web of Science Core Collection (WoSCC) database, analyzing publication dates ranging from 2005 to 2019. VOSviewer1.6.15 and Citespace5.7 R1 were used to perform a bibliometric analysis in terms of countries, institutions, authors, journals and research areas related to FEN1. A total of 421 publications were included in this analysis.Results: Our findings indicated that FEN1 has received more attention and interest from researchers in the past 15 years. Institutes in the United States, specifically the Beckman Research Institute of City of Hope published the most research related to FEN1. Shen BH, Zheng L and Bambara Ra were the most active researchers investigating this endonuclease and most of this research was published in the Journal of Biological Chemistry. The main scientific areas of FEN1 were related to biochemistry, molecular biology, cell biology, genetics and oncology. Research hotspots included biological activities, DNA metabolism mechanisms, protein-protein interactions and gene mutations. Research frontiers included oxidative stress, phosphorylation and tumor progression and treatment.Conclusion: This bibliometric study may aid researchers in the understanding of the knowledge base and research frontiers associated with FEN1. In addition, emerging hotspots for research can be used as the subjects of future studies. [ABSTRACT FROM AUTHOR]

Background: The cereal fibre β-glucan reduces postprandial glycaemia, however, the underlying mechanisms are not fully understood. Thus, the aim of this study was to investigate the acute effect of a β-glucan-enriched oat bread on gastric emptying half-time (T1/2), gastric emptying lag phase (Tlag), and gastric emptying rate (GER), and the secretion of glucagon-like peptide-1 (GLP-1) as potential means to influence postprandial glycaemia. Methods: A randomised crossover trial was conducted in 22 healthy adults (age 24.6 ± 3.1 years, BMI 23.1 ± 2.7 kg/m2) receiving 25 g available carbohydrates from a β-glucan-enriched oat bread or a control whole-wheat bread at two non-consecutive days. T1/2, Tlag, and GER were determined based on ultrasound measures of the cross-sectional gastric antrum area in the fasting state and 15, 30, 45, 60, 90, and 120 min postprandially. Capillary glucose, serum insulin, and plasma GLP-1 concentrations were measured at the same time points. Results: A biphasic pattern of gastric emptying with a distinct Tlag before the commencement of emptying was observed in most subjects for both bread types. While no differences in GER were evident (p = 0.562), consumption of the oat bread significantly increased T1/2 by 18 min and Tlag by 14 min compared with the whole-wheat bread (p = 0.005 and p = 0.010, respectively). In addition, the oat bread significantly reduced iAUC2h for glucose and insulin responses compared with the whole-wheat bread (p = 0.001 and p < 0.001, respectively). There were no significant differences in GLP-1 response between the two breads (p = 0.892). Conclusion: The increased T1/2 and Tlag could offer a potential mechanism for the observed attenuation of postprandial glycaemia and insulinemia after consumption of the β-glucan-enriched oat bread compared with the whole-wheat bread. Trial registration: The study is registered at clinicaltrails.gov (NCT04571866). [ABSTRACT FROM AUTHOR]

Background: The prevalence of obesity is a growing global public health concern. Certain dietary amino acids have been shown to have a potential therapeutic role in improving metabolic syndrome parameters and body composition in individuals with obesity. However, some amino acids have been linked to an increased risk of cardiometabolic disorders. This cross-sectional study aims to investigate the association between dietary amino acid patterns and cardiometabolic risk factors in individuals with obesity. Methods: This cross-sectional study included 335 participants with obesity (57.9% males and 41.5% females) from Tabriz and Tehran, Iran. The participants were between the ages of 20–50, with a body mass index (BMI) of 30 kg/m2 or higher, and free from certain medical conditions. The study examined participants' general characteristics, conducted anthropometric assessments, dietary assessments, and biochemical assessments. The study also used principal component analysis to identify amino acid intake patterns and determined the association between these patterns and cardiometabolic risk factors in individuals with obesity. Results: Upon adjusting for potential confounders, the study found that individuals in the third tertiles of pattern 1 and 2 were more likely to have lower LDL levels (OR = 0.99 and 95% CI (0.98–0.99)) for both. Additionally, a significant decrease in total cholesterol was observed in the third tertiles of pattern 2 in model II (OR = 0.99, 95% CI (0.98–0.99)). These findings suggest a potential cardioprotective effect of these amino acid patterns in managing cardiometabolic risk factors in individuals with obesity. Conclusions: This study found that two identified amino acid patterns were associated with lower serum LDL and total cholesterol levels, while a third pattern was associated with higher serum triglycerides. The specific amino acids contributing to these patterns highlight the importance of targeted dietary interventions in managing cardiometabolic risk factors in individuals with obesity. [ABSTRACT FROM AUTHOR]

Background: Osteoking has been used for fracture therapy with a satisfying clinical efficacy. However, its therapeutic properties and the underlying mechanisms remain elusive. Method: A bone defect rat model was established to evaluate the pharmacological effects of Osteoking by the dynamic observation of X-ray, micro-CT and histopathologic examination. Transcriptome profiling was performed to identify bone defect-related genes and Osteoking effective targets. Then, a "disease-related gene–drug target" interaction network was constructed and a list of key network targets were screened, which were experimentally verified. Results: Osteoking effectively promoted bone defect repair in rats by accelerating the repair of cortical bone and the growth of trabeculae. Histopathologically, the bone defect rats displayed lower histopathologic scores in cortical bone, cancellous bone and bone connection than normal controls. In contrast, Osteoking exerted a favorable effect with a dose-dependent manner. The abnormal serum levels of bone turnover markers, bone growth factors and bone metabolism-related biochemical indexes in bone defect rats were also reversed by Osteoking treatment. Following the transcriptome-based network investigation, we hypothesized that osteoking might attenuate the levels of ZBP1–STAT1–PKR–MLKL-mediated necroptosis involved into bone defect. Experimentally, the expression levels of ZBP1, STAT1, PKR and the hallmark inflammatory cytokines for the end of necroptosis were distinctly elevated in bone defect rats, but were all effectively reversed by Osteoking treatment, which were also suppressed the activities of RIPK1, RIPK3 and MLKL in bone tissue supernatants. Conclusions: Osteoking may promote bone formation and bone defect repair by regulating ZBP1–STAT1–PKR axis, leading to inhibit RIPK1/RIPK3/MLKL activation-mediated necroptosis. [ABSTRACT FROM AUTHOR]

Background: Metagenomics holds great promises for deepening our knowledge of key bacterial driven processes, but metagenome assembly remains problematic, typically resulting in representation biases and discarding significant amounts of non-redundant sequence information. In order to alleviate constraints assembly can impose on downstream analyses, and/or to increase the fraction of raw reads assembled via targeted assemblies relying on pre-assembly binning steps, we developed a set of binning modules and evaluated their combination in a new "assembly-free" binning protocol. Results: We describe a scalable multi-tiered binning algorithm that combines frequency and compositional features to cluster unassembled reads, and demonstrate i) significant runtime performance gains of the developed modules against state of the art software, obtained through parallelization and the efficient use of large lock-free concurrent hash maps, ii) its relevance for clustering unassembled reads from high complexity (e.g., harboring 700 distinct genomes) samples, iii) its relevance to experimental setups involving multiple samples, through a use case consisting in the "de novo" identification of sequences from a target genome (e.g., a pathogenic strain) segregating at low levels in a cohort of 50 complex microbiomes (harboring 100 distinct genomes each), in the background of closely related strains and the absence of reference genomes, iv) its ability to correctly identify clusters of sequences from the E. coli O104:H4 genome as the most strongly correlated to the infection status in 53 microbiomes sampled from the 2011 STEC outbreak in Germany, and to accurately cluster contigs of this pathogenic strain from a cross-assembly of these 53 microbiomes. Conclusions: We present a set of sequence clustering ("binning") modules and their application to biomarker (e.g., genomes of pathogenic organisms) discovery from large synthetic and real metagenomics datasets. Initially designed for the "assembly-free" analysis of individual metagenomic samples, we demonstrate their extension to setups involving multiple samples via the usage of the "alignment-free" d2S statistic to relate clusters across samples, and illustrate how the clustering modules can otherwise be leveraged for de novo "pre-assembly" tasks by segregating sequences into biologically meaningful partitions. [ABSTRACT FROM AUTHOR]

Background: Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem., Results: This paper presents an effective search strategy for a particle swarm optimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particle swarm optimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios., Conclusions: The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study.

Background: Brucella microti was first isolated from common vole (Microtus arvalis) in the Czech Republic in Central Europe in 2007. As B. microti is the only Brucella species known to live in soil, its distribution, ecology, zoonotic potential, and genomic organization is of particular interest. The present paper is the first to report the isolation of B. microti from a wild boar (Sus scrofa), which is also the first isolation of this bacterial species in Hungary. Results: The B. microti isolate was cultured, after enrichment in Brucella-selective broth, from the submandibular lymph node of a female wild boar that was taken by hunters in Hungary near the Austrian border in September 2014. Histological and immunohistological examinations of the lymph node sections with B. abortus-, B. suis- and B. canis-specific sera gave negative results. The isolate did not require CO2 for growth, was oxidase, catalase, and urease positive, H2S negative, grew well in the presence of 20 μg/ml basic fuchsin and thionin, and had brownish pigmentation after three days of incubation. It gave strong positive agglutination with anti-A and anti-M but had a negative reaction with anti-R monospecific sera. The API 20 NE test identified it as Ochrobactrum anthropi with 99.9 % identity, and it showed B. microti-specific banding pattern in the Bruce- and Suis-ladder multiplex PCR systems. Whole genome re-sequencing identified 30 SNPs in orthologous loci when compared to the B. microti reference genome available in GenBank, and the MLVA analysis yielded a unique profile. Conclusions: Given that the female wild boar did not develop any clinical disease, we hypothesize that this host species only harboured the bacterium, serving as a possible reservoir capable of maintaining and spreading this pathogen. The infectious source could have been either a rodent, a carcass that had been eaten or infection occurred via the boar rooting in soil. The low number of discovered SNPs suggests an unexpectedly high level of genetic homogeneity in this Brucella species. [ABSTRACT FROM AUTHOR]

Background: Self-directed learning (SDL) is one of the most important abilities for medical students in terms of their future clinical medical practice. During the blended teaching process, teachers can design a variety of learning activities to cultivate students’ SDL abilities. This study aimed to assess the differences between the SDL abilities of medical students using blended and traditional didactic teaching. Methods: This study included 239 medical students from eight administrative classes. The students were divided into two groups: (1) the experimental group (EG), which included 119 students from four administrative classes, and (2) the control group (CG), which included 120 students from the remaining four classes. From February to July 2022, blended teaching methods were applied to the EG group, and SDL abilities were assessed in comparison to the CG group receiving traditional didactic teaching methods. Results: At the end of the semester, significant differences (p < 0.05) were observed between EG and CG in all six SDL ability factors. Furthermore, when k-means cluster analysis was used to analyze the learning behavior of students in the EG after classifying them as comprehensive, interactive, and passive types, significant differences were observed in all six Self-directed learning factors of students with the comprehensive type, whereas significant differences were observed in four factors (setting learning goals and plans, self-monitoring and regulation, information processing, and communication and cooperation) of students with the interactive type. For students with passive type, only one factor of SDL (information processing) showed significant improvement. There were on differences between comprehensive, interactive, and passive types of CG. Conclusion: The blended teaching approach is better than the conventional didactic teaching for cultivating clinical medical students’ SDL abilities. [ABSTRACT FROM AUTHOR]

Breast cancer, as a daunting global health threat, has driven an exponential growth in related research activity in recent decades. An area of research of paramount importance is protein synthesis, and the analysis of specific proteins inextricably linked to breast cancer. In this article, we undertake a bibliometric analysis of the literature on breast cancer and protein synthesis, aiming to provide crucial insights into this esoteric realm of investigation. Our approach was to scour the Web of Science database, between 2003 and 2022, for articles containing the keywords "breast cancer" and "protein synthesis" in their title, abstract, or keywords. We deployed bibliometric analysis software, exploring a range of measures such as publication output, citation counts, co-citation analysis, and keyword analysis. Our search yielded 2998 articles that met our inclusion criteria. The number of publications in this area has steadily increased, with a significant rise observed after 2003. Most of the articles were published in oncology or biology-related journals, with the most publications in Journal of Biological Chemistry, Cancer Research, Proceedings of the National Academy of Sciences of the United States of America, and Oncogene. Keyword analysis revealed that "breast cancer," "expression," "cancer," "protein," and "translation" were the most commonly researched topics. In conclusion, our bibliometric analysis of breast cancer and related protein synthesis literature underscores the burgeoning interest in this research. The focus of the research is primarily on the relationship between protein expression in breast cancer and the development and treatment of tumors. These studies have been instrumental in the diagnosis and treatment of breast cancer. Sustained research in this area will yield essential insights into the biology of breast cancer and the genesis of cutting-edge therapies. [ABSTRACT FROM AUTHOR]

Background: Diabetic nephropathy (DN) is a chronic kidney disease that develops in patients with diabetes mellitus. Cordycepin (CRD), a secondary metabolite produced by Cordyceps militaris, has a variety of bioactive properties. In this study, DN mice and high glucose (HG)-treated HK-2 were used to evaluate the diagnostic value of CRD. Methods: Quantitative real-time PCR (qRT-PCR), western blotting, immunofluorescence analysis, and immunohistochemical staining were used to assess changes in mRNA and protein expression. Oxidative stress was evaluated by detecting the production of reactive oxygen species (ROS) and the activity of antioxidant enzymes. Cell apoptosis was detected by the TUNEL and flow cytometric methods. The interaction of miR-193b-5p and myeloid leukemia 1 (MCL-1) was examined by bioinformatics analysis and luciferase reporter assay. The protective effects of CRD on DN mice were evaluated by examining DN related biochemical indicators and renal histopathology. Results: In response to HG, the level of miR-193b-5p was elevated, whilst the level of MCL-1 was downregulated, and CRD therapy reversed this behavior. MCL-1 was further identified to be miR-193b-5p target. CRD attenuated HG-induced cell damage, inflammation and abnormal energy metabolism. Mechanistic investigations on in vitro models confirmed that protective effect of CRD against HG challenge to HK-2 cells is mediated through the regulation of expression of miR-193b-5p/MCL-1 axis. By examining DN related biochemical markers and renal histopathology, the protective effects of CRD on DN mice was assessed. Conclusions: In summary, CRD decreased oxidative stress and inflammation by increasing miR-193b-5p and inactivating downstream MCL-1 in DN, hinting the pivotal values of CRD and miR-193b-5p in the management of DN. [ABSTRACT FROM AUTHOR]

This paper discusses the use of 13C-based metabolism analysis for the assessment of intrinsic product yields — the actual carbon contribution from a single carbon substrate to the final product via a specific biosynthesis route — in the following four cases. First, undefined nutrients (such as yeast extract) in fermentation may contribute significantly to product synthesis, which can be quantified through an isotopic dilution method. Second, product and biomass synthesis may be dependent on the co-metabolism of multiple-carbon sources. 13C labeling experiments can track the fate of each carbon substrate in the cell metabolism and identify which substrate plays a main role in product synthesis. Third, 13C labeling can validate and quantify the contribution of the engineered pathway (versus the native pathway) to the product synthesis. Fourth, the loss of catabolic energy due to cell maintenance (energy used for functions other than production of new cell components) and low P/O ratio (Phosphate/Oxygen Ratio) significantly reduces product yields. Therefore, 13C-metabolic flux analysis is needed to assess the influence of suboptimal energy metabolism on microbial productivity, and determine how ATP/NAD(P)H are partitioned among various cellular functions. Since product yield is a major determining factor in the commercialization of a microbial cell factory, we foresee that 13C-isotopic labeling experiments, even without performing extensive flux calculations, can play a valuable role in the development and verification of microbial cell factories. [ABSTRACT FROM AUTHOR]

Background: "Stoichioproteomics" relates the elemental composition of proteins and proteomes to variation in the physiological and ecological environment. To help harness and explore the wealth of hypotheses made possible under this framework, we introduce GRASP (www.graspdb.net), a public bioinformatic knowledgebase containing information on the frequencies of 20 amino acids and atomic composition of their side chains. GRASP integrates comparative protein composition data with annotation data from multiple public databases. Currently, GRASP includes information on proteins of 12 sequenced Drosophila (fruit fly) proteomes, which will be expanded to include increasingly diverse organisms over time. In this paper we illustrate the potential of GRASP for testing stoichioproteomic hypotheses by conducting an exploratory investigation into the composition of 12 Drosophila proteomes, testing the prediction that protein atomic content is associated with species ecology and with protein expression levels. Results: Elements varied predictably along multivariate axes. Species were broadly similar, with the D. willistoni proteome a clear outlier. As expected, individual protein atomic content within proteomes was influenced by protein function and amino acid biochemistry. Evolution in elemental composition across the phylogeny followed less predictable patterns, but was associated with broad ecological variation in diet. Using expression data available for D. melanogaster, we found evidence consistent with selection for efficient usage of elements within the proteome: as expected, nitrogen content was reduced in highly expressed proteins in most tissues, most strongly in the gut, where nutrients are assimilated, and least strongly in the germline. Conclusions: The patterns identified here using GRASP provide a foundation on which to base future research into the evolution of atomic composition in Drosophila and other taxa. [ABSTRACT FROM AUTHOR]

Background: The success of molecular systems biology hinges on the ability to use computational models to design predictive experiments, and ultimately unravel underlying biological mechanisms. A problem commonly encountered in the computational modelling of biological networks is that alternative, structurally different models of similar complexity fit a set of experimental data equally well. In this case, more than one molecular mechanism can explain available data. In order to rule out the incorrect mechanisms, one needs to invalidate incorrect models. At this point, new experiments maximizing the difference between the measured values of alternative models should be proposed and conducted. Such experiments should be optimally designed to produce data that are most likely to invalidate incorrect model structures., Results: In this paper we develop methodologies for the optimal design of experiments with the aim of discriminating between different mathematical models of the same biological system. The first approach determines the 'best' initial condition that maximizes the L2 (energy) distance between the outputs of the rival models. In the second approach, we maximize the L2-distance of the outputs by designing the optimal external stimulus (input) profile of unit L2-norm. Our third method uses optimized structural changes (corresponding, for example, to parameter value changes reflecting gene knock-outs) to achieve the same goal. The numerical implementation of each method is considered in an example, signal processing in starving Dictyostelium amoebae., Conclusions: Model-based design of experiments improves both the reliability and the efficiency of biochemical network model discrimination. This opens the way to model invalidation, which can be used to perfect our understanding of biochemical networks. Our general problem formulation together with the three proposed experiment design methods give the practitioner new tools for a systems biology approach to experiment design.

Background: The surge in biomarker development calls for research on statistical evaluation methodology to rigorously assess emerging biomarkers and classification models. Recently, several authors reported the puzzling observation that, in assessing the added value of new biomarkers to existing ones in a logistic regression model, statistical significance of new predictor variables does not necessarily translate into a statistically significant increase in the area under the ROC curve (AUC). Vickers et al. concluded that this inconsistency is because AUC "has vastly inferior statistical properties," i.e., it is extremely conservative. This statement is based on simulations that misuse the DeLong et al. method. Our purpose is to provide a fair comparison of the likelihood ratio (LR) test and the Wald test versus diagnostic accuracy (AUC) tests. Discussion: We present a test to compare ideal AUCs of nested linear discriminant functions via an F test. We compare it with the LR test and the Wald test for the logistic regression model. The null hypotheses of these three tests are equivalent; however, the F test is an exact test whereas the LR test and the Wald test are asymptotic tests. Our simulation shows that the F test has the nominal type I error even with a small sample size. Our results also indicate that the LR test and the Wald test have inflated type I errors when the sample size is small, while the type I error converges to the nominal value asymptotically with increasing sample size as expected. We further show that the DeLong et al. method tests a different hypothesis and has the nominal type I error when it is used within its designed scope. Finally, we summarize the pros and cons of all four methods we consider in this paper. Summary: We show that there is nothing inherently less powerful or disagreeable about ROC analysis for showing the usefulness of new biomarkers or characterizing the performance of classification models. Each statistical method for assessing biomarkers and classification models has its own strengths and weaknesses. Investigators need to choose methods based on the assessment purpose, the biomarker development phase at which the assessment is being performed, the available patient data, and the validity of assumptions behind the methodologies. [ABSTRACT FROM AUTHOR]

Background: Motifs are significant patterns in DNA, RNA, and protein sequences, which play an important role in biological processes and functions, like identification of open reading frames, RNA transcription, protein binding, etc. Several versions of the motif search problem have been studied in the literature. One such version is called the Planted Motif Search (PMS) or (l, d)-motif Search. PMS is known to be NP complete. The time complexities of most of the planted motif search algorithms depend exponentially on the alphabet size. Recently a new version of the motif search problem has been introduced by Kuksa and Pavlovic. We call this version as the Motif Stems Search (MSS) problem. A motif stem is an l-mer (for some relevant value of l) with some wildcard characters and hence corresponds to a set of l-mers (without wildcards), some of which are (l, d)-motifs. Kuksa and Pavlovic have presented an efficient algorithm to find motif stems for inputs from large alphabets. Ideally, the number of stems output should be as small as possible since the stems form a superset of the motifs. Results: In this paper we propose an efficient algorithm for MSS and evaluate it on both synthetic and real data. This evaluation reveals that our algorithm is much faster than Kuksa and Pavlovic's algorithm. Conclusions: Our MSS algorithm outperforms the algorithm of Kuksa and Pavlovic in terms of the run time as well as the number of stems output. Specifically, the stems output by our algorithm form a proper (and much smaller) subset of the stems output by Kuksa and Pavlovic's algorithm. [ABSTRACT FROM AUTHOR]

Background: Recent microbiologic studies have shown that quorum sensing mechanisms, which serve as one of the fundamental requirements for bacterial survival, exist widely in bacterial intra- and inter-species cell-cell communication. Many simulation models, inspired by the social behavior of natural organisms, are presented to provide new approaches for solving realistic optimization problems. Most of these simulation models follow population-based modelling approaches, where all the individuals are updated according to the same rules. Therefore, it is difficult to maintain the diversity of the population. Results: In this paper, we present a computational model termed LCM-QS, which simulates the bacterial quorumsensing (QS) mechanism using an individual-based modelling approach under the framework of Agent- Environment-Rule (AER) scheme, i.e. bacterial lifecycle model (LCM). LCM-QS model can be classified into three main sub-models: chemotaxis with QS sub-model, reproduction and elimination sub-model and migration submodel. The proposed model is used to not only imitate the bacterial evolution process at the single-cell level, but also concentrate on the study of bacterial macroscopic behaviour. Comparative experiments under four different scenarios have been conducted in an artificial 3-D environment with nutrients and noxious distribution. Detailed study on bacterial chemotatic processes with quorum sensing and without quorum sensing are compared. By using quorum sensing mechanisms, artificial bacteria working together can find the nutrient concentration (or global optimum) quickly in the artificial environment. Conclusions: Biomimicry of quorum sensing mechanisms using the lifecycle model allows the artificial bacteria endowed with the communication abilities, which are essential to obtain more valuable information to guide their search cooperatively towards the preferred nutrient concentrations. It can also provide an inspiration for designing new swarm intelligence optimization algorithms, which can be used for solving the real-world problems. [ABSTRACT FROM AUTHOR]

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) imaging mass spectrometry, also called MALDI-imaging, is a label-free bioanalytical technique used for spatially-resolved chemical analysis of a sample. Usually, MALDI-imaging is exploited for analysis of a specially prepared tissue section thaw mounted onto glass slide. A tremendous development of the MALDI-imaging technique has been observed during the last decade. Currently, it is one of the most promising innovative measurement techniques in biochemistry and a powerful and versatile tool for spatially-resolved chemical analysis of diverse sample types ranging from biological and plant tissues to bio and polymer thin films. In this paper, we outline computational methods for analyzing MALDIimaging data with the emphasis on multivariate statistical methods, discuss their pros and cons, and give recommendations on their application. The methods of unsupervised data mining as well as supervised classification methods for biomarker discovery are elucidated. We also present a high-throughput computational pipeline for interpretation of MALDI-imaging data using spatial segmentation. Finally, we discuss current challenges associated with the statistical analysis of MALDI-imaging data. [ABSTRACT FROM AUTHOR]

Background: Pathway data are important for understanding the relationship between genes, proteins and many other molecules in living organisms. Pathway gene relationships are crucial information for guidance, prediction, reference and assessment in biochemistry, computational biology, and medicine. Many well-established databases- e.g., KEGG, WikiPathways, and BioCyc-are dedicated to collecting pathway data for public access. However, the effectiveness of these databases is hindered by issues such as incompatible data formats, inconsistent molecular representations, inconsistent molecular relationship representations, inconsistent referrals to pathway names, and incomprehensive data from different databases. Results: In this paper, we overcome these issues through extraction, normalization and integration of pathway data from several major public databases (KEGG, WikiPathways, BioCyc, etc). We build a database that not only hosts our integrated pathway gene relationship data for public access but also maintains the necessary updates in the long run. This public repository is named IntPath (Integrated Pathway gene relationship database for model organisms and important pathogens). Four organisms-S. cerevisiae, M. tuberculosis H37Rv, H. Sapiens and M. musculus-are included in this version (V2.0) of IntPath. IntPath uses the "full unification" approach to ensure no deletion and no introduced noise in this process. Therefore, IntPath contains much richer pathway-gene and pathway-gene pair relationships and much larger number of non-redundant genes and gene pairs than any of the single-source databases. The gene relationships of each gene (measured by average node degree) per pathway are significantly richer. The gene relationships in each pathway (measured by average number of gene pairs per pathway) are also considerably richer in the integrated pathways. Moderate manual curation are involved to get rid of errors and noises from source data (e.g., the gene ID errors in WikiPathways and relationship errors in KEGG). We turn complicated and incompatible xml data formats and inconsistent gene and gene relationship representations from different source databases into normalized and unified pathway-gene and pathway-gene pair relationships neatly recorded in simple tab-delimited text format and MySQL tables, which facilitates convenient automatic computation and large-scale referencing in many related studies. IntPath data can be downloaded in text format or MySQL dump. IntPath data can also be retrieved and analyzed conveniently through web service by local programs or through web interface by mouse clicks. Several useful analysis tools are also provided in IntPath. Conclusions: We have overcome in IntPath the issues of compatibility, consistency, and comprehensiveness that often hamper effective use of pathway databases. We have included four organisms in the current release of IntPath. Our methodology and programs described in this work can be easily applied to other organisms; and we will include more model organisms and important pathogens in future releases of IntPath. IntPath maintains regular updates and is freely available at http://compbio.ddns.comp.nus.edu.sg:8080/IntPath. [ABSTRACT FROM AUTHOR]

Metabolic network alignment is a system scale comparative analysis that discovers important similarities and differences across different metabolisms and organisms. Although the problem of aligning metabolic networks has been considered in the past, the computational complexity of the existing solutions has so far limited their use to moderately sized networks. In this paper, we address the problem of aligning two metabolic networks, particularly when both of them are too large to be dealt with using existing methods. We develop a generic framework that can significantly improve the scale of the networks that can be aligned in practical time. Our framework has three major phases, namely the compression phase, the alignment phase and the refinement phase. For the first phase, we develop an algorithm which transforms the given networks to a compressed domain where they are summarized using fewer nodes, termed supernodes, and interactions. In the second phase, we carry out the alignment in the compressed domain using an existing network alignment method as our base algorithm. This alignment results in supernode mappings in the compressed domain, each of which are smaller instances of network alignment problem. In the third phase, we solve each of the instances using the base alignment algorithm to refine the alignment results. We provide a user defined parameter to control the number of compression levels which generally determines the tradeoff between the quality of the alignment versus how fast the algorithm runs. Our experiments on the networks from KEGG pathway database demonstrate that the compression method we propose reduces the sizes of metabolic networks by almost half at each compression level which provides an expected speedup of more than an order of magnitude. We also observe that the alignments obtained by only one level of compression capture the original alignment results with high accuracy. Together, these suggest that our framework results in alignments that are comparable to existing algorithms and can do this with practical resource utilization for large scale networks that existing algorithms could not handle. As an example of our method's performance in practice, the alignment of organism-wide metabolic networks of human (1615 reactions) and mouse (1600 reactions) was performed under three minutes by only using a single level of compression. [ABSTRACT FROM AUTHOR]

Background: Distance-based phylogenetic reconstruction methods use evolutionary distances between species in order to reconstruct the phylogenetic tree spanning them. There are many different methods for estimating distances from sequence data. These methods assume different substitution models and have different statistical properties. Since the true substitution model is typically unknown, it is important to consider the effect of model misspecification on the performance of a distance estimation method. Results: This paper continues the line of research which attempts to adjust to each given set of input sequences a distance function which maximizes the expected topological accuracy of the reconstructed tree. We focus here on the effect of systematic error caused by assuming an inadequate model, but consider also the stochastic error caused by using short sequences. We introduce a theoretical framework for analyzing both sources of error based on the notion of deviation from additivity, which quantifies the contribution of model misspecification to the estimation error. We demonstrate this framework by studying the behavior of the Jukes-Cantor distance function when applied to data generated according to Kimura's two-parameter model with a transition-transversion bias. We provide both a theoretical derivation for this case, and a detailed simulation study on quartet trees. Conclusions: We demonstrate both analytically and experimentally that by deliberately assuming an oversimplified evolutionary model, it is possible to increase the topological accuracy of reconstruction. Our theoretical framework provides new insights into the mechanisms that enables statistically inconsistent reconstruction methods to outperform consistent methods. [ABSTRACT FROM AUTHOR]

Background: Protein-protein recognition is of fundamental importance in the vast majority of biological processes. However, it has already been demonstrated that it is very hard to distinguish true complexes from false complexes in so-called cross-docking experiments, where binary protein complexes are separated and the isolated proteins are all docked against each other and scored. Does this result, at least in part, reflect a physical reality? False complexes could reflect possible nonspecific or weak associations. Results: In this paper, we investigate the twilight zone of protein-protein interactions, building on an interesting outcome of cross-docking experiments: false complexes seem to favor residues from the true interaction site, suggesting that randomly chosen partners dock in a non-random fashion on protein surfaces. Here, we carry out arbitrary docking of a non-redundant data set of 198 proteins, with more than 300 randomly chosen "probe" proteins. We investigate the tendency of arbitrary partners to aggregate at localized regions of the protein surfaces, the shape and compositional bias of the generated interfaces, and the potential of this property to predict biologically relevant binding sites. We show that the non-random localization of arbitrary partners after protein-protein docking is a generic feature of protein structures. The interfaces generated in this way are not systematically planar or curved, but tend to be closer than average to the center of the proteins. These results can be used to predict biological interfaces with an AUC value up to 0.69 alone, and 0.72 when used in combination with evolutionary information. An appropriate choice of random partners and number of docking models make this method computationally practical. It is also noted that nonspecific interfaces can point to alternate interaction sites in the case of proteins with multiple interfaces. We illustrate the usefulness of arbitrary docking using PEBP (Phosphatidylethanolamine binding protein), a kinase inhibitor with multiple partners. Conclusions: An approach using arbitrary docking, and based solely on physical properties, can successfully identify biologically pertinent protein interfaces. [ABSTRACT FROM AUTHOR]

Background: Increasingly biological text mining research is focusing on the extraction of complex relationships relevant to the construction and curation of biological networks and pathways. However, one important category of pathway-metabolic pathways-has been largely neglected. Here we present a relatively simple method for extracting metabolic reaction information from free text that scores different permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presence and location of stemmed keywords. This method extends an approach that has proved effective in the context of the extraction of protein-protein interactions. Results: When evaluated on a set of manually-curated metabolic pathways using standard performance criteria, our method performs surprisingly well. Precision and recall rates are comparable to those previously achieved for the well-known protein-protein interaction extraction task. Conclusions: We conclude that automated metabolic pathway construction is more tractable than has often been assumed, and that (as in the case of protein-protein interaction extraction) relatively simple text-mining approaches can prove surprisingly effective. It is hoped that these results will provide an impetus to further research and act as a useful benchmark for judging the performance of more sophisticated methods that are yet to be developed. [ABSTRACT FROM AUTHOR]

Background: Many consensus-based and Position Weight Matrix-based methods for recognizing transcription factor binding sites (TFBS) are not well suited to the variability in the lengths of binding sites. Besides, many methods discard known binding sites while building the model. Moreover, the impact of Information Content (IC) and the positional dependence of nucleotides within an aligned set of TFBS has not been well researched for modeling variable-length binding sites. In this paper, we propose ML-Consensus (Mixed-Length Consensus): a consensus model for variable-length TFBS which does not exclude any reported binding sites. Methods: We consider Pairwise Score (PS) as a measure of positional dependence of nucleotides within an alignment of TFBS. We investigate how the prediction accuracy of ML-Consensus is affected by the incorporation of IC and PS with a particular binding site alignment strategy. We perform cross-validations for datasets of six species from the TRANSFAC public database, and analyze the results using ROC curves and the Wilcoxon matched-pair signed-ranks test. Results: We observe that the incorporation of IC and PS in ML-Consensus results in statistically significant improvement in the prediction accuracy of the model. Moreover, the existence of a core region among the known binding sites (of any length) is witnessed by the pairwise coexistence of nucleotides within the core length. Conclusions: These observations suggest the possibility of an efficient multiple sequence alignment algorithm for aligning TFBS, accommodating known binding sites of any length, for optimal (or near-optimal) TFBS prediction. However, designing such an algorithm is a matter of further investigation. [ABSTRACT FROM AUTHOR]

The article focuses on a research paper which presents information on a comprehensive non-redundant reaction database including known enzyme-catalyzed and biochemical reactions. It is said that the systematic and complete reconstruction of genome-scale metabolic networks is very important for systems biology research. The database consists of 18,172 unique biochemical reactions and it is based on biochemical databases BRENDA, KEGG, and MetaCyc.

Background: Automatic extraction of motifs from biological sequences is an important research problem in study of molecular biology. For proteins, it is desired to discover sequence motifs containing a large number of wildcard symbols, as the residues associated with functional sites are usually largely separated in sequences. Discovering such patterns is time-consuming because abundant combinations exist when long gaps (a gap consists of one or more successive wildcards) are considered. Mining algorithms often employ constraints to narrow down the search space in order to increase efficiency. However, improper constraint models might degrade the sensitivity and specificity of the motifs discovered by computational methods. We previously proposed a new constraint model to handle large wildcard regions for discovering functional motifs of proteins. The patterns that satisfy the proposed constraint model are called W-patterns. A W-pattern is a structured motif that groups motif symbols into pattern blocks interleaved with large irregular gaps. Considering large gaps reflects the fact that functional residues are not always from a single region of protein sequences, and restricting motif symbols into clusters corresponds to the observation that short motifs are frequently present within protein families. To efficiently discover W-patterns for large-scale sequence annotation and function prediction, this paper first formally introduces the problem to solve and proposes an algorithm named WildSpan (sequential pattern mining across large wildcard regions) that incorporates several pruning strategies to largely reduce the mining cost. Results: WildSpan is shown to efficiently find W-patterns containing conserved residues that are far separated in sequences. We conducted experiments with two mining strategies, protein-based and family-based mining, to evaluate the usefulness of W-patterns and performance of WildSpan. The protein-based mining mode of WildSpan is developed for discovering functional regions of a single protein by referring to a set of related sequences (e.g. its homologues). The discovered W-patterns are used to characterize the protein sequence and the results are compared with the conserved positions identified by multiple sequence alignment (MSA). The family-based mining mode of WildSpan is developed for extracting sequence signatures for a group of related proteins (e.g. a protein family) for protein function classification. In this situation, the discovered W-patterns are compared with PROSITE patterns as well as the patterns generated by three existing methods performing the similar task. Finally, analysis on execution time of running WildSpan reveals that the proposed pruning strategy is effective in improving the scalability of the proposed algorithm. Conclusions: The mining results conducted in this study reveal that WildSpan is efficient and effective in discovering functional signatures of proteins directly from sequences. The proposed pruning strategy is effective in improving the scalability of WildSpan. It is demonstrated in this study that the W-patterns discovered by WildSpan provides useful information in characterizing protein sequences. The WildSpan executable and open source codes are available on the web (http://biominer.csie.cyu.edu.tw/wildspan). [ABSTRACT FROM AUTHOR]