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Diagnosis

Diagnosis: The process of identifying a disease, condition, or injury from its signs and symptoms.
Effective diagnosis is crucial for making informed decisions and optimizing treatment outcomes.
PubCompare.ai, the leading AI-powered research platform, can enhance diagnosis accuracy by helping you easily locate the best protocols from literature, preprints, and patents using advanced AI comparisons.
Leverage the power of PubCompare.ai to make data-driven decisions for your diagnosis needs and improve patient outcomes.

Most cited protocols related to «Diagnosis»

1
Diagnostic and Differential Expression Analysis Plots
Figure 3 shows example diagnostic plots. Panel (A) shows RNA-seq data from Pickrell et al. (9 (link)) that has been analysed as described by Law et al. (10 (link)). Panels (B) and (C) display the two-colour microarray quality control data set presented by Ritchie et al. (11 (link)). Panel (B) displays background corrected but non-normalized intensities from one typical array. Panel (C) was generated from a subset of 30 of the control arrays after print-tip loess normalization (12 ).
Figure 4 shows example DE summary plots. Panels (A) and (B) were generated using the two-colour microarray data from GEO series GSE2593. Intensities were background corrected and normalized as previously described (13 (link)). Panel (A) shows a volcano plot for the comparison of samples with RUNX1 over-expressed versus wild-type samples, while panel (B) shows a Venn diagram of differentially expressed probes for each of the three over-expressed genes versus wild-type. Probes with false discovery rate less than 0.05 were considered to be differentially expressed. Panel (C) uses RNA-seq data from GEO series GSE52870. The data were analysed as described in Figure 5 of Liu et al. (7 (link)).
Ritchie M.E., Phipson B., Wu D., Hu Y., Law C.W., Shi W, & Smyth G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47.
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Publication 2015
Diagnosis Genes Microarray Analysis RNA-Seq RUNX1 protein, human
2
NHANES: Estimating CKD Prevalence in U.S.
The National Health and Nutrition Examination Survey (NHANES) is a cross-sectional, multistage, stratified, clustered probability samples of the civilian, non-institutionalized population of the U.S. conducted by the National Center of Health Statistics and appropriate for estimates of prevalence of chronic conditions in the U.S. Data were analyzed from 1999-2000, 2001-2002, 2003-2004, and 2005-2006 surveys. The study population for this analysis was limited to 16,032 participants (3,754 in 1999-2000, 4,297 in 2001-2002, 4,017 in 2003-2004, and 3,964 in 2005-2006), who were 20 years and older, had completed the examination in the mobile examination center, were not pregnant or menstruating, and were not missing serum creatinine measurements and did not have an estimated GFR below 15 ml/min/1.73 m2. Methods are similar to previous reports and are summarized briefly here (7 (link)).
GFR was not measured in NHANES. Serum creatinine was measured using a kinetic rate Jaffe method and re-calibrated to standardized creatinine measurements obtained in at the Cleveland Clinic Research Laboratory (Cleveland, OH) (33 (link)). GFR was estimated using the MDRD Study and the newly developed CKD-EPI equation. Estimates that exceeded 200 mL/min/1.73 m2 were truncated at that level. Methods for collection, analysis, and reporting for albuminuria have been described (7 (link), 34 (link)). Albuminuria was defined as albumin-to-creatinine ratio ≥30 mg/g. Repeated measurements, obtained in a subset of 1,241 NHANES 1988-1994 participants approximately 2 weeks after the original examination were used to estimate the persistence of albuminuria (34 (link)). NHANES does not have accurate diagnoses of causes of kidney disease. CKD was defined as persistent albuminuria or estimated GFR <60 ml/min/1.73 m2 (1 (link)). CKD was classified according to estimated GFR stages as defined above. Distributions of estimated GFR, estimated GFR stages and prevalence of CKD were compared for both equations.
Analyses were performed incorporating the sampling weights to obtain unbiased estimates from the complex NHANES sampling design using Stata (Version 10.0, StataCorp, College Station, TX). Standard errors for all estimates were obtained using the Taylor series (linearization) method following NHANES recommended procedures and weights (35 -37 ). Confidence intervals for prevalence estimates for CKD stages incorporating persistence data on of albuminuria were made using bootstrap methods implemented in Stata. Prevalence estimates were applied to the 2000 U.S. Census to obtain estimates of the number of individuals with CKD in the U.S.
Levey A.S., Stevens L.A., Schmid C.H., Zhang Y.(., Castro AF I.I.I., Feldman H.I., Kusek J.W., Eggers P., Van Lente F., Greene T, & Coresh J. (2009). A New Equation to Estimate Glomerular Filtration Rate. Annals of internal medicine, 150(9), 604-612.
Publication 2009
Albumins Chronic Condition Creatinine Diagnosis Kidney Diseases Kinetics Serum
3
ClustVis: Comprehensive Data Analysis Tool
ClustVis includes multiple popular public data sets for testing purposes: NKI breast cancer data set (14 (link),15 ), Wisconsin diagnostic breast cancer data set (16 ) and Fisher's Iris data set (17 ).
In addition to small data sets, we used the last version of Multi Experiment Matrix—MEM (18 (link)). MEM contains a very large collection of public gene expression matrices from ArrayExpress (5 (link)), together with annotation tracks where available. Genetic pathways were downloaded from g:Profiler web tool (12 (link)). From Gene Ontology, only biological processes were included. Microarray platforms and genetic pathways cover currently 17 species.
Metsalu T, & Vilo J. (2015). ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Research, 43(Web Server issue), W566-W570.
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Publication 2015
Biological Processes Diagnosis Gene Expression Iris Plant Malignant Neoplasm of Breast Microarray Analysis Reproduction
4
Quantification of Wnt and Cilia Genes
Total RNAs were isolated using Isogen (Nippon Gene) and reverse-transcribed using PrimeScript RT reagent kit (TAKARA Bio). Real-time PCR was performed on the LightCycler 480 system (Roche Diagnostics) using LightCycler 480 SYBR Green I Master mix (Roche Diagnostics). The amount of mRNA was normalized relative to that of 18S ribosomal RNA. The following primers were used: Ror2, 5′-CAATTCCACTGGTCATCGCT-3′ (forward) and 5′-TGAGGGGCATTTCCATGTC-3′ (reverse); Wnt5a, 5′-TAAGCCCAGGAGTTGCTTTG-3′ (forward) and 5′-GCAGAGAGGCTGTGCTCCTA-3′ (reverse); IFT20, 5′-CAGAACTCCTCTAGGGAACCTG-3′ (forward) and 5′-GCTCTATGGTCTGCTGGGTAA-3′ (reverse); 18S ribosomal RNA, 5′-ATGGCCGTTCTTAGTTGGTG-3′ (forward) and 5′-CGCTGAGCCAGTCAGTGTAG-3′ (reverse).
Nishita M., Park S.Y., Nishio T., Kamizaki K., Wang Z., Tamada K., Takumi T., Hashimoto R., Otani H., Pazour G.J., Hsu V.W, & Minami Y. (2017). Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness. Scientific Reports, 7, 1.
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Publication 2017
Diagnosis Genes Oligonucleotide Primers Real-Time Polymerase Chain Reaction RNA RNA, Messenger RNA, Ribosomal, 18S ROR2 protein, human SYBR Green I WNT5A protein, human
5
Comparing Propensity Score Estimation Methods
Several studies have demonstrated that propensity score matching eliminates a greater proportion of the systematic differences in baseline characteristics between treated and untreated subjects than does stratification on the propensity score or covariate adjustment using the propensity score (Austin, 2009a ; Austin, Grootendorst, & Anderson, 2007; (link) Austin & Mamdani, 2006 (link)). In some settings propensity score matching and IPTW removed systematic differences between treated and untreated subjects to a comparable degree; however, in some settings, propensity score matching removed modestly more imbalance than did IPTW (Austin, 2009a ). Lunceford and Davidian (2004) (link) demonstrated that stratification results in estimates of average treatment effects with greater bias than does a variety of weighted estimators.
Propensity score matching, stratification on the propensity score, and IPTW differ from covariate adjustment using the propensity score in that the three former methods separate the design of the study from the analysis of the study; this separation does not occur when covariate adjustment using the propensity score is used. Appropriate diagnostics exist for each of the four propensity score methods to assess whether the propensity score model has been adequately specified. However, with propensity score matching, stratification on the propensity score, and IPTW, once one is satisfied with the specification of the propensity score model, one can directly estimate the effect of treatment on outcomes in the matched, stratified, or weighted sample. Specification of a regression model relating the outcome to treatment is not necessary. In contrast, when using covariate adjustment using the propensity score, once one is satisfied that the propensity score model has been adequately specified, one must fit a regression model relating the outcome to an indicator variable denoting treatment status and to the propensity score. In specifying the regression model, one must correctly model the relationship between the propensity score and the outcome (e.g., specifying whether the relationship is linear or nonlinear). In doing so, the outcome is always in sight because the outcome model contains both the propensity score and the outcome. As Rubin (2001) notes, when using regression modeling, the temptation to work toward the desired or anticipated result is always present. Another difference between the four propensity score approaches is that covariate adjustment using the propensity score and IPTW may be more sensitive to whether the propensity score has been accurately estimated (Rubin, 2004 ).
The reader is referred elsewhere to empirical studies comparing the results of analyses using the different propensity score methods on the same data set (Austin & Mamdani, 2006 (link); Kurth et al., 2006 (link)). Prior Monte Carlo studies have compared the relative performance of the different propensity score methods for estimating risk differences, relative risks, and marginal and conditional odds ratios (Austin, 2007b (link), 2008c (link), 2010 (link); Austin, Grootendorst, Normand, & Anderson, 2007 (link)). It is important to note that two of these studies found that stratification, matching, and covariate adjustment using the propensity score resulted in biased estimation of both conditional and marginal odds ratios.
, & Austin P.C. (2011). An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behavioral Research, 46(3), 399-424.
Publication 2011
austin Diagnosis Vision

Most recents protocols related to «Diagnosis»

1
Preparation of Cell-free Lactobacillus Fractions
Not available on PMC !

Example 1

Cell-free fractions were prepared as previously described (25). Briefly, Lactobacillus acidophilus strain La-5 was grown overnight in modified DeMann, Rogosa and Sharpe medium. (mMRS; 10 g peptone from casein, 8 g meat extract, 4 g yeast extract, 8 g D(+)-glucose, 2 g dipotassium hydrogen phosphate, 2 g di-ammonium hydrogen citrate, 5 g sodium acetate, 0.2 g magnesium sulfate, 0.04 g manganese sulfate in 1 L distilled water) (MRS; BD Diagnostic Systems, Sparks, MD). The overnight culture was diluted 1:100 in fresh medium. When the culture grew to an optical density at 600 nm (OD600) of 1.6 (1.2×108 cells/ml), the cells were harvested by centrifugation at 6,000×g for 10 min at 4° C. The supernatant was sterilized by filtering through a 0.2-μm-pore-size filter (Millipore, Bioscience Division, Mississauga, ON, Canada) and will be referred to as cell-free spent medium (CFSM). Two litres of L. acidophilus La-5 CFSM was collected and freeze-dried (Unitop 600 SL, VirTis Co., Inc. Gardiner, NY., USA). The freeze-dried CFSM was reconstituted with 200 ml of 18-Ω water. The total protein content of the reconstituted CFSM was quantified using the BioRad DC protein assay kit II (Bio-Rad Laboratories Ltd., Mississauga, ON, Canada). Freeze-dried CFSM was stored at −20° C. prior to the assays.

US11857581B2. Antiviral methods and compositions comprising probiotic bacterial molecules (2024-01-02). MicroSintesis Inc. [CA]. Inventors: Mansel Griffiths [CA].
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Patent 2024
ammonium citrate Biological Assay casein peptone Cells Centrifugation Diagnosis Freezing Glucose Hydrogen Lactobacillus acidophilus L Cells manganese sulfate Meat potassium phosphate, dibasic Proteins Sodium Acetate Sulfate, Magnesium Unitop Yeast, Dried
2
Benchmarking MCA-Miner against FP-Growth in BRL
Not available on PMC !

Example 1

The MCA-miner method disclosed herein in FIGS. 2A-2C, when used together with BRL, offers the power of rule list interpretability while maintaining the predictive capabilities of already established machine learning methods.

The performance and computational efficiency of the new MCA-miner is benchmarked against the “Titanic” dataset, as well as the following five (5) datasets available in the UCI Machine Learning Repository: “Adult,” “Autism Screening Adult,” “Breast Cancer Wisconsin (Diagnostic),” “Heart Disease,” and “HIV-1 protease cleavage,” which are designated as Adult, ASD, Cancer, Heart, and HIV, respectively. These datasets represent a wide variety of real-world experiments and observations, thus enabling the improvements described herein to be compared against the original BRL implementation using the FP-Growth miner.

All six benchmark datasets correspond to binary classification tasks. The experiments were conducted using the same set up in each of the benchmarks. First, the dataset is transformed into a format that is compatible with the disclosed BRL implementation. Second, all continuous attributes are quantized into either two (2) or three (3) categories, while keeping the original categories of all other variables. It is worth noting that depending on the dataset and how its data was originally collected, the existing taxonomy and expert domain knowledge are prioritized in some instances to generate the continuous variable quantization. A balanced quantization is generated when no other information was available. Third, a model is trained and tested using 5-fold cross-validations, reporting the average accuracy and Area Under the ROC Curve (AUC) as model performance measurements.

Table 1 presents the empirical result of comparing both implementations. The notation in the table follows the definitions above. To strive for a fair comparison between both implementations, the parameters rmax=2 and smin=0:3 are fixed for both methods, and in particular for MCA-miner μmin=0:5 and M=70 are also set. The multi-core implementations for both the new MCA-miner and BRL were executed on six parallel processes, and stopped when the Gelman & Rubin parameter satisfied {circumflex over (R)}≤1.05. All the experiments were run using a single AWS EC2 c5.18×large instance with 72 cores.

TABLE 1
Performance evaluation of FP-Growth against MCA-miner
when used with BRL on benchmark datasets. ttrain is the full training wall time.
FP-GROWTH + BRLMCA-MINER + BRL
DATASETnpΣt-1p|α1|ACCURACYAUCttrain[s]ACCURACYAUCttrain[s]
Adult45.222141110.810.855120.810.85115
ASD24821890.870.901980.870.9016
Cancer569321500.920.971680.920.9422
Heart30313490.820.861170.820.8615
HIV5.84081600.870.884490.870.8836
Titanic2.201380.790.761180.790.7510

It is clear from the experiments in Table 1 that the new MCA-miner matches the performance of FP-Growth in each case, while significantly reducing the computation time required to mine rules and train a BRL model.

US11857322B2. Systems and methods for screening, diagnosing, and stratifying patients (2024-01-02). BlackThorn Therapeutics, Inc. [US]. Inventors: Qingzhu Gao [US], Humberto Andres Gonzalez Cabezas [US], Parvez Ahammad [US], Yuelu Liu [US].
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Patent 2024
Adult Autistic Disorder Cytokinesis Diagnosis Figs Heart Heart Diseases HIV-2 Malignant Neoplasm of Breast Malignant Neoplasms p16 protease, Human immunodeficiency virus 1
3
Syphilis Diagnosis and Treatment
Not available on PMC !

Example 4

Syphilis is an STI that can cause long-term complications if not treated correctly. Symptoms in adults are divided into stages. These stages are primary, secondary, latent, and late syphilis. In pregnant women, having syphilis can lead to giving birth to a low birth weight baby. It can also lead to delivering the baby too early or stillborn (CDC fact sheet, 2015).

Although T. pallidum cannot be grown in culture, there are many tests for the direct and indirect diagnosis of syphilis. Still, there is no single optimal test. Direct diagnostic methods include the detection of T. pallidum by microscopic examination of fluid or smears from lesions, histological examination of tissues or nucleic acid amplification methods such as polymerase chain reaction (PCR). Indirect diagnosis is based on serological tests for the detection of antibodies (Ratnam S, Can J Infect Dis Med Microbiol 2005). Treatment includes a single dose of intramuscular administration of penicillin (2.4 Million units).

In some embodiments, the disclosed device can be used to detect syphilis infections from menstrual blood or cervicovaginal fluids.

US11864740B2. Sample collection and preservation devices, systems and methods (2024-01-09). NextGen Jane, Inc. [US]. Inventors: Ridhi Tariyal [US], Stephen K. Gire [US].
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Patent 2024
Adult Antibodies BLOOD Childbirth Diagnosis Globus Pallidus Infant Infection Medical Devices Menstruation Microscopy Nucleic Acid Amplification Techniques Penicillins Polymerase Chain Reaction Pregnant Women Syphilis Syphilis, tertiary Tests, Serologic Tissues
4
Diagnosis and Significance of Bacterial Vaginosis
Not available on PMC !

Example 5

Bacterial Vaginosis (BV) is an infection caused when too much of certain bacteria change the normal balance of bacteria in the vagina. Bacterial vaginosis (BV) is one of the most common lower genital tract conditions, occurring in 35% of women attending sexually transmitted infection (STI) clinics, 15% to 20% of pregnant women, and 5% to 15% of women attending gynecology clinics (Eschenbach D A, Am J Obstet Gynecol 1993). Pregnant women with BV are more likely to have babies who are born premature (early) or with low birth weight than women who do not have BV while pregnant. Low birth weight means having a baby that weighs less than 5.5 pounds at birth (CDC fact sheet, 2015).

Diagnosis of BV is typically done through a vaginal swab to assess the presence and balance of certain bacteria within the vaginal flora through PCR. A wet mount, whiff test, or pH test can also be performed in order to diagnose a possible bacterial infection.

In some embodiments, the disclosed device can be used to detect bacterial vaginosis from menstrual blood or cervicovaginal fluids.

US11864740B2. Sample collection and preservation devices, systems and methods (2024-01-09). NextGen Jane, Inc. [US]. Inventors: Ridhi Tariyal [US], Stephen K. Gire [US].
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Patent 2024
Bacteria Bacterial Infections Bacterial Vaginosis Blood Childbirth Diagnosis Hereditary Diseases Infant Infection Medical Devices Menstruation Pregnant Women Premature Birth Sexually Transmitted Diseases Vagina Vaginal Diseases Woman
5
Thermal Transformations of Compound A Polymorphs
Not available on PMC !

Example 18

a) During heating at 90° C. of Compound A crystal Form I (crystallized from ethyl acetate) the characteristic peaks of Form I decreased (particularly noticeable in solid-state CP/MAS 13C NMR spectrum in the regions 14-15, 26-29, 44-46 and 63-66 ppm), whereas those of Compound A crystal Form II increased [diagnostic peaks (15.4, 14.7), (29.1, 25.9), (64.0, 65.7) ppm]. Compound A crystal Form I was completely converted to Compound A crystal Form II in 4 hrs.

b) Crystalline Form I/III Compound A, crystallized from water, was heated at 90° C. for 75 min. Solid-state CP/MAS 13C NMR of the product confirmed that crystalline Form I/III was transformed to crystalline Form II (re FIG. 16).

c) Crystalline Form II Compound A was heated at 70° C. for 10 h, then left at room temperature overnight. Solid-state CP/MAS 13C NMR of the product confirmed that crystalline Form II was unchanged.

US11866449B2. Crystalline polymorphs of a muscarinic acetylcholine receptor agonist (2024-01-09). NSC Therapeutics GmbH [AT]. Inventors: Abraham Fisher [IL], Nira Bar-Ner [IL], Manfred Windisch [AT].
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Patent 2024
Carbon-13 Magnetic Resonance Spectroscopy Diagnosis ethyl acetate

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TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
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TRIzol is a monophasic solution of phenol and guanidine isothiocyanate that is used for the isolation of total RNA from various biological samples. It is a reagent designed to facilitate the disruption of cells and the subsequent isolation of RNA.

More about "Diagnosis"

Diagnosis is the critical process of identifying a disease, condition, or injury based on its observable signs and symptoms.
Effective diagnosis is essential for making informed decisions and optimizing treatment outcomes.
Leveraging advanced AI-powered platforms like PubCompare.ai can enhance diagnosis accuracy by helping researchers and clinicians easily locate the best diagnostic protocols from literature, preprints, and patents using sophisticated AI comparisons.
Diagnosis encompasses a range of related terms and subtopics, including disease identification, condition assessment, injury recognition, symptom analysis, and clinical decision-making.
Accurate diagnosis relies on a thorough examination of the patient, consideration of medical history, and interpretation of diagnostic tests and imaging results.
Cutting-edge tools and techniques like TRIzol reagent, LightCycler 480, SAS version 9.4, Protease inhibitor cocktail, RNeasy Mini Kit, In Situ Cell Death Detection Kit, and Prism 8 can be utilized to support the diagnostic process and gather critical data.
By integrating these advanced methods with AI-powered platforms like PubCompare.ai, healthcare professionals can make more informed, data-driven decisions to improve patient outcomes.
Whether you're a researcher, clinician, or healthcare provider, leveraging the power of PubCompare.ai can be a game-changer in enhancing your diagnosis accuracy and effectiveness.
Explore the capabilities of this leading AI platform to optimize your diagnostic workflows and deliver the best possible care for your patients.