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Factor X

Factor X is a key component of the blood coagulation cascade, playing a crucial role in the conversion of prothrombin to thrombin.
It is activated by the intrinsic and extrinsic pathways, and its activation is a critical step in the formation of a fibrin clot.
Factor X deficiency can lead to impaired hemostasis and an increased risk of bleeding.
Researcheres can use PubCompare.ai's AI-driven protocol optimization to streamline their investigations into Factor X and related coagulation factors, locating relevant protocols from literature, pre-prints, and paents, and using intelligent comparisons to identify the best protocols and products for their research.

Most cited protocols related to «Factor X»

1
Carcinogenic Risk Assessment of PM2.5-bound PAHs
Cited 411 times
Carcinogenic and mutagenic risk assessments15 (link),60 (link)–63 (link),67 (link)–69 (link) induced by inhalation of PM2.5-bound enriched with selected nitro-PAHs (1-NPYR, 2-NPYR, 2-NFLT, 3-NFLT, 2-NBA, and 3-NBA) and PAHs (PYR, FLT, BaP, and BaA) were estimated in the bus station and coastal site samples according to calculations done by Wang et al.60 (link), Nascimento et al.61 (link), and Schneider et al.67 (link) PAH and PAH derivatives risk assessment is done in terms of BaP toxicity, which is well established67 (link)–73 (link). The daily inhalation levels (EI) were calculated as: EI=BaPeq×IR=(∑Ci×TEFi)×IR where EI (ng person−1 day−1) is the daily inhalation exposure, IR (m³ d−1) is the inhalation rate (m³ d−1), BaPeq is the equivalent of benzo[a]pyrene (BaPeq = Σ Ci × TEFi) (in ng m−3), Ci is the PM2.5 concentration level for a target compound i, and TEFi is the toxic equivalent factor of the compound i. TEF values were considered those from Tomaz et al.15 (link), Nisbet and LaGoy69 (link), OEHHA72 , Durant et al.73 (link), and references therein. EI in terms of mutagenicity was calculated using equation (1), just replacing the TEF data by the mutagenic potency factors (MEFs) data, published by Durant et al.73 (link). Individual TEFs and MEFs values and other data used in this study are described in SI, Table S4.
The incremental lifetime cancer risk (ILCR) was used to assess the inhalation risk for the population in the Greater Salvador, where the bus station and the coastal site are located. ILCR is calculated as: ILCR=(EI×SF×ED×cf×EF)/(AT×BW) where SF is the cancer slope factor of BaP, which was 3.14 (mg kg−1 d−1)−1 for inhalation exposure60 (link), EF (day year−1) represents the exposure frequency (365 days year−1), ED (year) represents exposure duration to air particles (year), cf is a conversion factor (1 × 10−6), AT (days) means the lifespan of carcinogens in 70 years (70 × 365 = 25,550 days)70 ,72 , and BW (kg) is the body weight of a subject in a target population71 .
The risk assessment was performed considering four different target groups in the population: adults (>21 years), adolescents (11–16 years), children (1–11 years), and infants (<1 year). The IR for adults, adolescents, children, and infants were 16.4, 21.9, 13.3, 6.8 m3 day−1, respectively. The BW was considered 80 kg for adults, 56.8 kg for adolescents, 26.5 kg for children and 6.8 kg for infants70 .
Santos A.G., da Rocha G.O, & de Andrade J.B. (2019). Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles. Scientific Reports, 9, 1.
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Publication 2019
Adolescent Adult Benzo(a)pyrene Body Weight Carcinogens Child derivatives Factor X Fibrinogen fluoromethyl 2,2-difluoro-1-(trifluoromethyl)vinyl ether Health Risk Assessment Infant Inhalation Inhalation Exposure Malignant Neoplasms Mutagens Polycyclic Hydrocarbons, Aromatic Population at Risk Population Group Respiratory Rate
2
Optimized Particle Picking in RELION
Cited 116 times
RELION also implements a template-based particle selection procedure, which calculates a probability measure (the R-value) for each pixel in the micrograph to signify the likelihood that it is the location of any of the provided templates (Scheres, 2015 (link)). The R-value map of a micrograph considers all possible rotations of each template, and is subsequently used in a peak-search algorithm that locates particles within the original micrograph (Figure 3). These calculations are performed in Fourier space, where they are highly efficient (Roseman, 2003 (link)). In fact, they are so fast that their execution time becomes negligible compared to the time spent performing FFTs to transform image objects between real and Fourier spaces. Consequently, even though reference templates are also treated as independent tasks to increase parallelism in the GPU version, a much larger gain is found at the level of template rotations, through parallel execution of FFTs. For example, when using 5-degree incremental template rotations, 72 such inverse FFTs are now performed concurrently on the GPU through the cufft cuda library. The size of these FFTs is now also padded automatically, since substantial performance penalties can occur if the transform size includes any large prime factors.10.7554/eLife.18722.004Semi-automated particle picking in RELION-2.

The low-pass filter applied to micrographs is a novel feature in RELION, aimed at reducing the size and execution time of the highlighted inverse FFTs, which accounts for most of the computational work. In addition to the inverse FFTs, all template- and rotation-dependent parallel steps have also been accelerated on GPUs.

DOI:http://dx.doi.org/10.7554/eLife.18722.004

Kimanius D., Forsberg B.O., Scheres S.H, & Lindahl E. (2016). Accelerated cryo-EM structure determination with parallelisation using GPUs in RELION-2. eLife, 5, e18722.
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Publication 2016
DNA Library Factor X
3
Psychometric Validation of the Perceived Stress Scale
Cited 37 times
Descriptive statistics were calculated for a detailed sample description. Age was categorized by decades. Reliability was assessed by Cronbach’s alpha. Construct validity was determined by Pearson correlations between perceived stress and depression, anxiety, fatigue, procrastination, respectively quality of life. Basing on previous findings suggesting a two-factor solution of the PSS-10, a confirmatory factor analysis (CFA) was calculated. The positively worded items determined the first factor, the negatively worded items the second factor. The model was estimated with the maximum likelihood method approach. Model fit was evaluated by using following model fit indices [34 (link)]: Chi-square statistic; the comparative-fit-index (CFI) and the Tucker-Lewis Index (TLI) to describe incremental fit; the root mean square error of approximation (RMSEA) was used as an absolute measure of fit. Values of TLI and CFI close to .95 or higher indicate a better fit. RMSEA should be 0.08 or smaller. Sex differences and interaction effects with demographic variables were tested by means of a two-factorial ANOVA with Scheffé test (total PSS-10 score as the dependent variable; sex and one demographic variable as independent variables). Effect sizes were calculated for t-tests (Cohen’s d) and for ANOVA (η2). According to Cohen [35 ] effect sizes were considered as small (d ≥ .2; η2 = .01), moderate (d ≥ .5; η2 = .06) or large (d ≥ .8; η2 = .14). We performed a forward stepwise linear regression defining the sum score of the PSS-10 as outcome variable. We included sex, age, anxiety, depression, procrastination, fatigue and life satisfaction as predictor variables. In order to provide differentiated norm values, mean sum-scores, standard deviations, and percentiles of each factor of the PSS-10 were analyzed separately for sex and age. Because of the large sample size, p-values should be interpreted with caution and in connection with effect estimates. We performed calculations by SPSS Version 21.0 and AMOS© 21.0.
Klein E.M., Brähler E., Dreier M., Reinecke L., Müller K.W., Schmutzer G., Wölfling K, & Beutel M.E. (2016). The German version of the Perceived Stress Scale – psychometric characteristics in a representative German community sample. BMC Psychiatry, 16, 159.
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Publication 2016
A-factor (Streptomyces) Anxiety factor A Factor X Fatigue neuro-oncological ventral antigen 2, human Plant Roots Procrastination Satisfaction
4
Convolutional DNN for Arrhythmia Detection
Cited 27 times
We developed a convolutional DNN to detect arrhythmias (Extended Data Figure 1) which takes as input the raw ECG data (sampled at 200Hz, or 200 samples per second) and outputs one prediction every 256 samples (or every 1.28 seconds), which we call the output interval. The network takes as input only the raw ECG samples and no other patient or ECG related features. The network architecture has 34 layers, and in order to make the optimization of such a network tractable, we employed shortcut connections in a manner similar to the Residual Network architecture41 . The network consists of 16 residual blocks with two convolutional layers per block. The convolutional layers have a filter width of 16 and 32*2k filters, where k starts at zero and is incremented by one every fourth residual block. Every alternate residual block subsamples its inputs by a factor of two. Before each convolutional layer we applied Batch Normalization42 and a rectified linear activation, adopting the pre-activation block design43 . The first and last layers of the network are special-cased due to this pre-activation block structure. We also applied Dropout44 between the convolutional layers and after the non-linearity with a probability of 0.2. The final fully-connected softmax layer produces a distribution over the 12 output classes.
The network was trained de-novo with random initialization of the weights as described by He et al.9 We used the Adam optimizer45 with the default parameters (β1=0.9andβ2=0.999) and a minibatch size of 128. We initialized the learning rate to 1e-3 and reduced it by a factor of ten when the development-set loss stopped improving for two consecutive epochs. We chose the model that achieved the lowest error on the development dataset.
In general, the hyper-parameters of the network architecture and optimization algorithm were chosen via a combination of grid-search and manual tuning. For the architecture we searched primarily over the number of convolutional layers, the size and number of the convolutional filters as well as the use of residual connections. We found the residual connections useful once the depth of the model exceeded 8 layers. We also experimented with recurrent layers including long short-term memory cells46 and bi-directional recurrence but found no improvement in accuracy and a substantial increase in runtime, thus we abandoned this class of models. We manually tuned the learning rate to achieve fastest convergence.
Hannun A.Y., Rajpurkar P., Haghpanahi M., Tison G.H., Bourn C., Turakhia M.P, & Ng A.Y. (2019). Cardiologist-Level Arrhythmia Detection and Classification in Ambulatory Electrocardiograms Using a Deep Neural Network. Nature medicine, 25(1), 65-69.
Publication 2019
Cardiac Arrhythmia EPOCH protocol factor A Factor X Memory, Long-Term Neoplasm Metastasis Patients Postoperative Residual Curarization Recurrence
5
Lung Organoid Culture and Passaging
Cited 26 times

Lung cell pellets were resuspended in 10 mg ml−1 cold Cultrex growth factor reduced BME type 2 (Trevigen‐3533‐010‐02), and 40 μl drops of BME‐cell suspension were allowed to solidify on pre‐warmed 24‐well suspension culture plates (Greiner‐M9312) at 37°C for 10–20 min.

Upon completed gelation, 400 μl of AO medium was added to each well and plates transferred to humidified 37°C/5% CO2 incubators at ambient O2.

Medium was changed every 4 days and organoids were passaged every 2 weeks: Cystic organoids were resuspended in 2 ml cold AdDF+++ and mechanically sheared through flamed glass Pasteur pipettes. Dense (organoids were dissociated by resuspension in 2 ml TrypLE Express (Invitrogen‐12605036), incubation for 1–5 min at room temperature, and mechanical shearing through flamed glass Pasteur pipettes.

Following the addition of 10 ml AdDF+++ and centrifugation at 300 or 400 rcf respectively, organoid fragments were resuspended in cold BME and reseeded as above at ratios (1:1–1:6) allowing the formation of new organoids. Single‐cell suspensions were initially seeded at high density and reseeded at a lower density after ˜1 week. Success rate was determined by dividing the number of successfully established, expanded, and cryopreserved AO lines by the number of attempts.

NSCLC organoids could be distinguished from normal regular cystic organoids by morphology (size, irregular shape, thick organoid walls, dense) as well as histology.

Separation from normal AOs was achieved by manual separation and in case of TP53 mutations by the addition of 5 μM Nutlin‐3a (Cayman Chemicals‐10004372) to the culture medium. For the R‐spondin withdrawal assay, established organoid lines were trypsinized to single cells and grown in AO medium ± R‐spondin until organoids were depleted. Intestinal organoids were cultured as previously described (Sato et al, 2011).

Unless specified, airway organoids were analyzed after at least 7 days post‐splitting at the indicated passage.

Sachs N., Papaspyropoulos A., Zomer‐van Ommen D.D., Heo I., Böttinger L., Klay D., Weeber F., Huelsz‐Prince G., Iakobachvili N., Amatngalim G.D., de Ligt J., van Hoeck A., Proost N., Viveen M.C., Lyubimova A., Teeven L., Derakhshan S., Korving J., Begthel H., Dekkers J.F., Kumawat K., Ramos E., van Oosterhout M.F., Offerhaus G.J., Wiener D.J., Olimpio E.P., Dijkstra K.K., Smit E.F., van der Linden M., Jaksani S., van de Ven M., Jonkers J., Rios A.C., Voest E.E., van Moorsel C.H., van der Ent C.K., Cuppen E., van Oudenaarden A., Coenjaerts F.E., Meyaard L., Bont L.J., Peters P.J., Tans S.J., van Zon J.S., Boj S.F., Vries R.G., Beekman J.M, & Clevers H. (2019). Long‐term expanding human airway organoids for disease modeling. The EMBO Journal, 38(4), e100300.
Publication 2019
2-aza-2-desamino-5,8-dideazafolic acid Biological Assay Caimans Cells Centrifugation Cold Temperature Cyst Factor X Intestines Lung Mutation Non-Small Cell Lung Carcinoma nutlin-3A Organoids Pellets, Drug TP53 protein, human

Most recents protocols related to «Factor X»

1
Carcinogenicity Study of Esketamine in Rats
Not available on PMC !

Example 4

A 104 Week carcinogenicity study of esketamine administered via oral gavage to Sprague Dawley Rats is performed to evaluate the carcinogenic potential and determine the toxicokinetics of esketamine.

As based on the International Conference on Harmonization (ICH) S1 Guidelines S1A, Guideline on the Need for Carcinogenicity Studies of Pharmaceuticals; SIB, Testing for Carcinogenicity of Pharmaceuticals; and S1C(R2), Dose Selection for Carcinogenicity Studies of Pharmaceuticals, 236 male and 236 female Sprague Dawley Rats are administered esketamine over 104 weeks at the doses of 0 (vehicle control), 6, 10 or 30 mg/kg/day for the male rats and 0 (vehicle control), 2, 10 or 20 mg/kg/day for the female rats.

The study end-points include clinical observations, body weight changes, food consumption, bioanalytical toxicokinetic analysis, and anatomic macroscopic and microscopic pathology findings.

It can thus be demonstrated that the genotoxic changes as shown in Examples 1 and 2 were not identified after 28 days administration at point of departure doses and at reduced doses, which factor in an at least 10 fold safety margin after 730 days, thereby providing a minimal safe window for chronic esketamine administration.

US11865088B2. Method of treating major depressive disorder (2024-01-09). Clexio Biosciences Ltd. [IL]. Inventors: Elena Kagan [IL], Gina Pastino [US].
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Patent 2024
Carcinogens Conferences Esketamine Factor X Females Food Males Microscopy Pharmaceutical Preparations Rats, Sprague-Dawley Rattus Safety Tube Feeding
2
Subgrouping Hemophilia Patients by Factor Consumption

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2023
Condoms factor A Factor X Genetic Heterogeneity Hemophilia A Hemophilia B Patients Phenotype Prescriptions Woman
3
Modeling Diabetes Progression Factors
The culprits of diabetes may vary for different subgroups of diabetic patients, which implies the distinction of possible interference factors to the glucose regulation system. Nevertheless, the underlying mechanisms through which the factors lead to dysglycemia are common. Numerous studies indicate that glycemia is primarily attributed to excess hepatic glucose output and abnormal insulin secretion and utilization [35 (link)]. Of note, beta-cell function is regulated by various mechanisms, not limited to glucose utilization [12 (link)]. Thus, confining the model for beta-cell function only with the variables of glucose and insulin may impede the study of beta-cell dysfunction. We aim to test through an in-silico approach how the T2D progression is affected by certain pathological factors. Here we propose a general form of diabetes progression model with a pathological factor X that is to be specified:
dGdt=Gin+p1(X)-f2(G)-C(I)GI,
dIdt=f1(G)p2(X)β-kI,
dβdt=(f3(I)+p3(X))β,
where X is a bounded variable with a real value; all the variables in the system are in the time scale of days: p1(X) is incorporated into Eq (1) to stand for the increased hepatic glucose production caused by the pathological factor; p2(X) integrated into Eq (2) symbolizes the impact of the factor on the insulin secretion rate; p3(X) is incorporated to Eq (3) to describe the abnormal response of beta-cells to a hostile environment that develops in a slow time scale. The exact forms of the influence functions pi(X) (i = 1, 2, 3) will be determined with X being an obesity-related factor in Section. We assume that p1(X) = 0, p2(X) = 1, and p3(X) = 0 when X = 0 so the model is in accordance with the undisturbed glucose-insulin regulatory model when no diabetogenic factors exist in normal subjects.
Yang B., Li J., Haller M.J., Schatz D.A, & Rong L. (2023). Modeling the progression of Type 2 diabetes with underlying obesity. PLOS Computational Biology, 19(2), e1010914.
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Publication 2023
Diabetes Mellitus Disease Progression Factor X Glucose Hostility Insulin Insulin Secretion LEP protein, human Pancreatic beta Cells Patients Physiology, Cell
4
Factors Influencing COVID-19 Admission Patterns
Participant characteristics were summarized descriptively. Comparisons between patients discharged home, admitted to the medical ward, or admitted directly to the ICU were made with Wilcoxson rank sum and Pearson chi-square tests for continuous and categorical variables, respectively. Impact of timing during the pandemic was assessed as days since data collection started (March 8, 2020).
All tests were 2-sided and a P value < .05 was considered statistically significant. All variables were initially assessed for significance using univariable analysis comparing: Patients discharged home versus admitted to the medical ward and; Patients admitted to the medical ward versus ICU (see Tables S1 and S2, Supplemental Digital Content, http://links.lww.com/MD/I601, which shows the results of univariable analysis). A Multivariable logistic regression was fitted separately comparing: Patients discharged home versus admitted to the medical ward and; Patients admitted to the medical ward versus ICU. We opted for 2 logistic regression models to reflect the distinct clinical decision making processes in the ED (i.e., “discharge home” vs “admit to medical ward,” and “admit to medical ward” vs “admit to ICU”).”
Our key associations of interest were race, ethnicity, ADI, English as a primary language, homelessness, and illicit substance use (opiates, cocaine, methamphetamine); variables also included age, gender, and clinical comorbidities, including body mass index (mg/kg2) and clinical severity. We evaluated disease severity using clinical severity scores (sequential organ failure assessment, Charlson comorbidity index) and laboratory markers found in other risk severity scores,[27 (link),28 ] specifically, C-reactive protein (mg/L), ferritin (ug/L), D-dimer (ng/mL), creatine kinase (U/L), troponin (ng/L), procalcitonin (ng/mL), absolute lymphocyte count (K/mL), and blood urea nitrogen (mg/dL). Timing of admission was calculated as days after the first date of data collection (March 8, 2020). In our regression, we controlled for timing of admission and included the square of timing of admission to evaluate how the effect changed over time. To build our regression models, we first included a priori variables based on clinical understanding (i.e., age, sex, sequential organ failure assessment, C-reactive protein, ferritin, and troponin), and then added variables that were significant on univariable analysis.” Variables were excluded if they showed significant co-linearity (variance inflation factors over 10). We used stepwise, backward selection for our logistic regression model, using a P value of over 0.2 as a cutoff to remove variables. Potential interaction between significant variables was explored.
Additionally, we divided differences in number of admissions in 3 groups to visually evaluate changes in admission over time. Groups were created as general phases of the surge in SARS-CoV-2 admissions in our hospital, representing changes in comfort with diagnosis and clinical management of COVID-19. Changes in admission patterns over time were assessed using the Jonckheere–Terpstra test for trend. All data were analyzed using Stata Statistical Software (Release 16. College Station, TX: StataCorp LLC).
Olds P.K., Musinguzi N., Geisler B.P, & Haberer J.E. (2023). Evaluating disparities by social determinants in hospital admission decisions for patients with COVID-19 quaternary hospital early in the pandemic. Medicine, 102(10), e33178.
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Publication 2023
Cocaine COVID 19 C Reactive Protein Creatine Kinase Diagnosis Ethnicity Factor X Ferritin fibrin fragment D Index, Body Mass Lymphocyte Count Methamphetamine Opiate Alkaloids Pandemics Patients Procalcitonin SARS-CoV-2 Substance Use Troponin Urea Nitrogen, Blood
5
Postoperative Pain Trajectory Analysis
Statistical analysis was performed using IBM SPSS Statistics (Version 23.0, IBM Corp, New York, USA) and the R programming language (version 4.1.1). Data are expressed using means ± (standard deviation, SD), medians (interquartile range, IQR) or frequencies (percentages). Each patient had two distinct pain trajectories for worst pain scores and average pain scores. The R package kml (K-means for longitudinal data) was used to cluster each pain trajectory category. The Euclidean distance between values at each time point was measured for clustering. Calinski–Harabasz scores were used to evaluate intergroup distinctness and intragroup variation, and the optimal number of groups corresponded to the value of k that maximized the Calinski–Harabasz scores [16 (link)]. Patients were divided into different pain trajectory groups generated by the k-means algorithm according to pain scores over the first 2 weeks after surgery.
The median time to pain resolution and opioid cessation was analyzed using Kaplan–Meier survival analysis and log rank statistics. Cox regression analysis was used to assess the correlation of the pain trajectory group with long-term outcomes, adjusting for potential patient- and surgery-related factors. Factors with p < 0.10 in the univariate analysis were entered into the multivariable Cox regression analysis.
Logistic regression was used to examine preoperative factors associated with the high pain trajectory group. Factors were compared between clusters by Student’s t tests, Mann–Whitney U tests, and chi-squared or Fisher’s exact tests, as appropriate. Factors with p < 0.10 were considered for inclusion in the final model with pain trajectory group assignment as the outcome. A 2-sided p value less than 0.05 was considered statistically significant.
As a substudy of a multicenter study, the sample size was determined by available data from patients enrolled in the main study, and no statistical power was calculated before analysis.
Zhang Q., Wu Y., Hong S, & Feng Y. (2023). Trajectory of worst pain within the first two weeks following pelvic and sacral tumor surgery and long-term outcome: a pilot observational prospective cohort study. BMC Anesthesiology, 23, 73.
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Publication 2023
Factor X Operative Surgical Procedures Opioids Pain Patients Properdin Student

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More about "Factor X"

Factor X, also known as Stuart-Prower factor, is a critical component of the blood coagulation cascade.
It plays a pivotal role in the conversion of prothrombin to thrombin, a crucial step in the formation of a fibrin clot.
Factor X is activated by both the intrinsic and extrinsic pathways of the coagulation system.
Researchers can utilize PubCompare.ai's AI-driven protocol optimization to streamline their investigations into Factor X and related coagulation factors.
This intelligent platform allows them to locate relevant protocols from literature, pre-prints, and patents, and use sophisticated comparisons to identify the best protocols and products for their research.
Factor X deficiency can lead to impaired hemostasis and an increased risk of bleeding.
Researchers studying coagulation disorders may use techniques like SPSS software, Growth factor-reduced Matrigel, Phase contrast microscopy, Matrigel, SAS 9.4, Stata 14, Transwell chambers, SPSS version 22.0, FBS, and Diff-Quick kit to understand the underlying mechanisms and develop new treatments.
By leveraging PubCompare.ai's AI-driven protocol optimization, researchers can streamline their investigations, improve the accuracy of their findings, and accelerate the pace of their research into Factor X and related coagulation factors.