SEARCH · PubMed Health

Results for “algorithms”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4Linked to original sources

PubMed2026

Meniscal preservation in the age of biologics: toward a quantitative decision algorithm for personalized repair.

BACKGROUND: Despite advances in arthroscopic repair and biologic augmentation, surgical indication for meniscal tears remains heterogeneous. No standardized framework currently integrates biomechanical, clinical, and biological determinants to guide repair versus resection. PURPOSE: To develop a quantitative decision model-the Meniscal Preservation Score (MPS)-that unifies biomechanical and biological evidence to stratify reparability potential and standardize treatment selection in meniscal surgery. METHODS: A systematic evidence synthesis conducted in accordance with PRISMA 2020 reporting standards of studies published from 2000 to 2025 in PubMed, Embase, and Scopus identified key determinants of meniscal healing. Five consistent predictors-patient age, vascularity, tear morphology, associated pathology, and activity profile-were weighted through a two-round modified Delphi consensus among ten experienced knee surgeons. The resulting 0-9-point MPS was incorporated into a stepwise decision tree linking lesion morphology, biological context, and surgical strategy. Conceptual validation used 50 simulated cases and a retrospective cohort of 45 patients to test agreement between algorithm recommendations and expert surgical decisions. RESULTS: The MPS achieved 86% concordance with expert judgment in simulation and 84% agreement in clinical validation. In this retrospective exploratory cohort, cases in which surgical management was concordant with MPS recommendations demonstrated higher mean IKDC scores at 24 months and lower observed reoperation rates. These findings should be interpreted as associative rather than causal, as treatment allocation was not controlled and discordant cases may have represented inherently more complex pathology. CONCLUSION: The MPS represents an evidence-informed decision-support framework designed to systematize reparability assessment. While exploratory analyses suggest structural coherence with expert reasoning, prospective implementation and external validation are required before clinical adoption as a predictive tool. LEVEL OF EVIDENCE: conceptual model with exploratory validation.

Humans↗

PubMed1992

An algorithm for ordering pretreatment orthodontic radiographs.

A study was conducted to identify selection criteria for ordering pretreatment orthodontic radiographs. Thirty-nine orthodontists evaluated six test cases. They provided information on the rationale for ordering each specific radiograph and the impact of the radiograph on the diagnosis and treatment plan. Skeletal relationship of the jaws was the most common indication for a radiograph request, followed by root formation/length and molar position or development. Of the radiographs, 16% produced a change in diagnosis, and 20% produced a change in treatment plans. The criteria specified for the radiographs, their impact, and relevant information in the literature were used to develop an algorithm or set of decision rules that, when tested on the six test cases, resulted in a 36% reduction in the total number of radiographs.

Airway Obstruction↗

PubMed2024

Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms.

Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, "singletCode," to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.

Algorithms↗

PubMed2026

A de novo algorithm for allele reconstruction from Oxford nanopore amplicon reads, with application to CYP2D6.

MOTIVATION: The Oxford Nanopore Technologies' sequencing platform offers a path towards bedside genomics, producing long reads that can completely cover a gene of interest, and detect any known or novel variant the gene contains. However, the analysis of these long reads to identify actionable genotypes remains challenging and typically requires customization depending on the target gene. RESULTS: Here, we describe a generic algorithm to accurately reconstruct allele sequences derived from long-reads of amplicon-based data. Rather than calling variants directly from these long-reads, our method takes a "sequence-first" approach, performing an unbiased reconstruction of the underlying amplicon sequences to generate high-confidence reconstructed allele sequences. This is done without user input of the target gene, allowing for any source amplicon to be reconstructed. These high-confidence reconstructed allele sequences are then compared to the genomic reference sequence of the gene to infer the specific diplotype present in the sample. This approach is agnostic towards the number of genes and alleles present and readily detects novel variants. We demonstrate our approach using three independent data sets for CYP2D6, a diverse and complex gene with over 175 known alleles of clinical significance. We show how our approach can accurately recover validated CYP2D6 diplotypes from 20 Coriell samples covering 14 distinct alleles, using different amplicons, flow cell versions, and depths. This includes inferring occurrences of allele duplication events from relative abundances of each allele, a critical factor for ascribing functional effects to a diplotype. Further, we demonstrate our approach's utility for other genomic regions, including HLA. AVAILABILITY: Custom code is available at the following GitHub repository, along with instructions for use and test data: https://github.com/scottdbrown/allele-reconstruction-long-read-amplicon-data. A snapshot of the code at the time of publication is available on Zenodo.org; doi 10.5281/zenodo.19716004. Raw .fastq sequence data for our three sequencing runs is available at the SRA under Bioproject PRJNA1357883 (https://www.ncbi.nlm.nih.gov/bioproject/1357883).

Alleles↗

PubMed2025

Proteomics-enabled learning machine algorithms enhance the prediction of cardiovascular diseases in patients with type 2 diabetes mellitus.

BACKGROUND AND AIMS: Estimating the risk of cardiovascular disease (CVD) complications in type 2 diabetes mellitus (T2DM) patients is critical in the medical decision-making process. This study aimed to use a machine learning technique combined with proteomics to develop personalized models for predicting CVD in patients with T2DM. METHODS AND RESULTS: In total, 874 patients with T2DM and 2,920 Olink proteins obtained from the UK Biobank were used in this study. Proteins were screened using Cox regression and LASSO regression. A basic model containing clinical features and a full model combining proteome and clinical features were constructed using the random survival forest algorithm. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the predictive performance of the models and compare them with other CVD predictive models. Compared with the basic model, the full model performed better in predicting CVD, with time-dependent AUCs of 0.81 (3 years), 0.74 (5 years) and 0.74 (10 years) (0.77, 0.69 and 0.67). We calculated the risk scores of the Framingham, ASCVD and Score2-Diabetes models. The results revealed that the prediction performance of the full model was also better than that of the abovementioned models. In terms of differentiation accuracy, the results of the net reclassification improvement index and integrated discrimination improvement index showed that the full model can identify high-risk individuals more accurately (accuracy rate: 79% vs. 69%). CONCLUSIONS: Proteomics can be used to predict cardiovascular complications in diabetic patients. It is also necessary to consider the applicability of the model due to the limitations of the sample size and the constraints of proteomics in clinical applications.

Humans↗

PubMed2024

Identification of key immune-related genes and potential therapeutic drugs in diabetic nephropathy based on machine learning algorithms.

BACKGROUND: Diabetic nephropathy (DN) is a major contributor to chronic kidney disease. This study aims to identify immune biomarkers and potential therapeutic drugs in DN. METHODS: We analyzed two DN microarray datasets (GSE96804 and GSE30528) for differentially expressed genes (DEGs) using the Limma package, overlapping them with immune-related genes from ImmPort and InnateDB. LASSO regression, SVM-RFE, and random forest analysis identified four hub genes (EGF, PLTP, RGS2, PTGDS) as proficient predictors of DN. The model achieved an AUC of 0.995 and was validated on GSE142025. Single-cell RNA data (GSE183276) revealed increased hub gene expression in epithelial cells. CIBERSORT analysis showed differences in immune cell proportions between DN patients and controls, with the hub genes correlating positively with neutrophil infiltration. Molecular docking identified potential drugs: cysteamine, eltrombopag, and DMSO. And qPCR and western blot assays were used to confirm the expressions of the four hub genes. RESULTS: Analysis found 95 and 88 distinctively expressed immune genes in the two DN datasets, with 14 consistently differentially expressed immune-related genes. After machine learning algorithms, EGF, PLTP, RGS2, PTGDS were identified as the immune-related hub genes associated with DN. In addition, the mRNA and protein levels of them were obviously elevated in HK-2 cells treated with glucose for 24 h, as well as their mRNA expressions in kidney tissues of mice with DN. CONCLUSION: This study identified 4 hub immune-related genes (EGF, PLTP, RGS2, PTGDS), as well as their expression profiles and the correlation with immune cell infiltration in DN.

Diabetic Nephropathies↗

PubMed2025

G4SNVHunter: An R/Bioconductor Package for Evaluating SNV-Induced Disruption of G-Quadruplex Structures Leveraging the G4Hunter Algorithm.

G-quadruplexes (G4s) are nucleic acid secondary structures with important regulatory functions. Single-nucleotide variants (SNVs), one of the most common forms of genetic variation, can potentially impact the formation of G4 structures if they occur within G4 regions. However, there is currently a lack of software tools specifically designed to assess such effects. Here, we present an R/Bioconductor package named G4SNVHunter, which enables rapid detection of variants that may disrupt G4 structures. This tool, based on the core principles of the G4Hunter algorithm, can provide precise quantitative assessment of the propensity for G4 formation within genomic sequences. Specialized experimental methods can then be designed based on the results provided by G4SNVHunter to further verify the specific functions of the affected G4 structures, facilitating deeper insights into the biological impacts of genetic variants from the perspective of G4 structures. To showcase the functionality of the G4SNVHunter package, we analyzed the Neandertal and Denisovan archaic introgressed variants detected by the Sprime software, and identified approximately 5,800 variants located within G4 regions, among which around 230 may impair G4 structure formation propensity. The source code for the G4SNVHunter package has been publicly released under the MIT license at https://github.com/rongxinzh/G4SNVHunter and https://bioconductor.org/packages/devel/bioc/html/G4SNVHunter.html.

G-Quadruplexes↗

PubMed2024

Machine learning algorithm-based biomarker exploration and validation of mitochondria-related diagnostic genes in osteoarthritis.

The role of mitochondria in the pathogenesis of osteoarthritis (OA) is significant. In this study, we aimed to identify diagnostic signature genes associated with OA from a set of mitochondria-related genes (MRGs). First, the gene expression profiles of OA cartilage GSE114007 and GSE57218 were obtained from the Gene Expression Omnibus. And the limma method was used to detect differentially expressed genes (DEGs). Second, the biological functions of the DEGs in OA were investigated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Wayne plots were employed to visualize the differentially expressed mitochondrial genes (MDEGs) in OA. Subsequently, the LASSO and SVM-RFE algorithms were employed to elucidate potential OA signature genes within the set of MDEGs. As a result, GRPEL and MTFP1 were identified as signature genes. Notably, GRPEL1 exhibited low expression levels in OA samples from both experimental and test group datasets, demonstrating high diagnostic efficacy. Furthermore, RT-qPCR analysis confirmed the reduced expression of Grpel1 in an in vitro OA model. Lastly, ssGSEA analysis revealed alterations in the infiltration abundance of several immune cells in OA cartilage tissue, which exhibited correlation with GRPEL1 expression. Altogether, this study has revealed that GRPEL1 functions as a novel and significant diagnostic indicator for OA by employing two machine learning methodologies. Furthermore, these findings provide fresh perspectives on potential targeted therapeutic interventions in the future.

Humans↗

PubMedDate not supplied

An algorithm for the surgical management of chronic abdominal aortic occlusion and occluded aortofemoral grafts.

An algorithm for the surgical management of chronic abdominal aortic occlusion is presented based upon experience of treating 60 consecutive patients. Of 33 patients with juxtarenal aortic occlusion, 17 underwent aortofemoral bypass (AFB), 10 descending thoracic aortofemoral (DTAF), 5 axillofemoral (AxF) bypass, and 1 ascending thoracic aortofemoral bypass. Of 11 patients with mid or distal aortic occlusion, 8 underwent AFB, 2 DTAF and 1 AxF. Of 16 patients with aortic graft occlusion, 1 underwent AFB, 10 DTAF and 5 AxF. Acceptable risk patients were selected for AFB (26). DTAF (22) was frequently preferred for patients with occluded aortic grafts or other hazardous intraabdominal pathology. AxF (11) was used for patients with severe cardiopulmonary risk, limited life expectancy from malignancy, or when emergency procedures were required for salvage of severely ischemic limbs in debilitated patients with chronic aortic occlusion. In the AFB, DTAF and AxF groups the perioperative mortality was 8%, 5% and 36% respectively, the late mortality was 15%, 36% and 45%, and the 5-year primary cumulative graft patency was 92%, 89% and 15%.

Aged↗

PubMedDate not supplied

[Algorithms of x-ray and ultrasonic diagnoses in urology].

Roentgenourologic methods and ultrasonic scanning (USS) should be combined in the radiologic diagnosis of urologic diseases. USS should be the first stage of examinations of urologic patients, and its results should be taken into account when planning and carrying out excretory urography. USS can be repeated before more sophisticated roentgenourologic examinations in order to single out the "zones of interest"; special programmes are possible for the purpose-pharmacoechography, dopplerography, etc. Development of tentative algorithms of x-ray and ultrasonic diagnosis of the major urologic diseases will help optimize the diagnostic process.

Algorithms↗

PubMedDate not supplied

[Formulas for estimating the RD parameter in the algorithm of statistical regulation of glycemia with a biostator].

An analytical approach to the estimation of the RD parameter for this algorithm has been developed; up to the latest time this parameter was chosen by the investigators by the 'try and miss' technique, based on the scientist's intuition. Three interactive formulae for the calculation of the RD parameter were derived, differing by the degree of the parameter modulation in the iterative procedure of the search for the precise value. The method for estimation of glucose utilization rate was adapted for the clamp method results obtained with a biostator; this method was for the first time used in the semiautomated glycemia regulation, that may improve the accuracy of glucose tolerance estimation.

Algorithms↗

PubMed1992

[Rapid micromethod for CH50 determination using an algorithm].

The titration of haemolytic complement in biological liquid cause inconvenient in calculating concentration, it was long and fastidious. We report a fast technic based on measure of hemolysis in microplate++ method, which exploitation of results, it does with an appropriate algorithm.

Algorithms↗

PubMedDate not supplied

[The diagnostic algorithm in neonatal infections].

Authors review the principles of diagnosis in neonatal bacterial infections (local and systemic), in congenital, peri- and postnatal viral infections and also in Candida spp. and other mycotic infections of the neonatal period. They try to delineate the clinical and epidemiological criteria of suspicion and modalities of confirmation of the neonatal infections by specific paraclinical methods. Attention is focused on modern diagnostic methods (such as immunofluorescent techniques, counterimmunoelectrophoresis and so on), which are important for the early etiological diagnosis and for thr rapid initiation of specific therapy. Authors made a practical diagnostic algorithm for the most frequent encountered neonatal infections. They also focused on the recent changes in the etiology of neonatal infections and their therapeutic significance.

Algorithms↗

PubMedDate not supplied

The Pennsylvania Plan. An algorithm for the management of lumbar degenerative disc disease.

An algorithm for the sequential management of the patient with low-back pain has been formulated from evaluation of treatment outcomes. Patients presenting with back pain complaints and cauda equina syndrome are evaluated with immediate myelography. Without this complication, back pain patients are treated with 6 weeks of conservative therapy. Those who fail to respond are evaluated with progressively more complex techniques. When sciatica predominates, treatment may ultimately include laminectomy. When back pain predominates, medical and psychosocial appraisal are recommended. Some with normal medical and psychosocial evaluations may become candidates for spine fusion. The remaining are treated according to the findings of such appraisals. Rigorous screening is mandatory prior to any surgery.

Algorithms↗

PubMed1979

Kinetic parameter estimation by numerical algorithms and multiple linear regression: application to pharmacokinetics.

Two numerical examples are presented to illustrate the application of the proposed method of parameter estimation in pharmacokinetics. Results for a system exemplifying first-order kinetics indicate that parameters estimated by the proposed procedure compare favorably with those estimated by a nonlinear regression method. In a simulated example characterized by Michaelis-Menten elimination kinetics, the accuracy of the estimated parameters was comparable to that expected, verifying the validity of the method. The importance of the numerical approximation algorithms was demonstrated also.

Kinetics↗

PubMed1977

A computer algorithm for triple radionuclide subtraction studies applied for preoperative localization of enlarged parathyroid glands.

This paper presents a computer algorithm for triple radionuclide subtraction studies and its application to preoperative localization of enlarged parathyroid glands. In the clinical examination procedure, 131I-toluidine blue was used as the principal radionuclide, 99mTcO4 and 113mIn were used to obtain additional images of the thyroid and the blood distribution respectively. A scintillation camera with a pinhole collimator and connected to a digital data aquisition system was used to record the images. Subtraction of uniform background, thyroid and blood contributions to the principal image is done automatically in the computer program. The results from a clinical study is used to illustrate the method.

Computers↗

PubMed2026

Multimodal features and prognostic risk assessment in locally advanced gastric cancer patients following neoadjuvant therapy based on machine learning algorithms: a multicenter study.

BACKGROUND: Neoadjuvant therapy (NAT) is recommended for locally advanced gastric cancer (LAGC), but some patients respond poorly. We aimed to construct a multimodal model integrating CT images, transcriptomic sequencing, and clinicopathological data to assess prognosis in LAGC patients receiving NAT. MATERIALS AND METHODS: This multicenter study included 505 LAGC patients who underwent NAT. Radiomic features were extracted from preoperative CT images of 505 patients. RNA-seq was performed on 277 post-NAT specimens, with additional data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (n = 804). Patients were divided into training (168 cases), internal validation (72 cases), and external validation cohorts. Machine learning algorithms identified key radiomic, molecular, and clinical features associated with NAT response, which were then integrated into a multimodal model to predict overall survival (OS) and disease-free survival (DFS). RESULTS: Six radiomic and three molecular features significantly associated with NAT response were selected. Radiomic risk (hazard ratio [HR]: 4.0, P < 0.001) and molecular risk (HR: 7.1, P < 0.001) were independent prognostic factors. By integrating radiomic risk, molecular risk, and clinical characteristics, a multimodal model (MuMo) was constructed.The C-index results (OS, C-index = 0.855; DFS, C-index = 0.786) demonstrated that MuMo outperformed the single-modality models and ypTNM staging.Mechanistic analysis suggested that the efficacy of neoadjuvant therapy was significantly enriched in immune-inflammatory pathways. CONCLUSIONS: MuMo can effectively predict postoperative survival risk in LAGC patients receiving NAT, serving as a powerful tool for optimizing prognostic assessment.

Humans↗

PubMed1979

An iterative algorithm for analysis of variance.

In this paper, an iterative algorithm is proposed for computing estimates of parameters and sums of squares in non-orthogonal multivariate analysis of variance, without inverting any matrix. It is useful in the case of a large design matrix for it saves memory and computation time. It was first proposed by Stevens (1948) for 3 factors and is here generalised to any number of factors and interactions of any order. Convergence properties are studied. The more orthogonal is the design, the faster is the convergence. Several examples are provided.

Analysis of Variance↗