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PubMed1989

[Reducing chloramines in drinking water].

Monochloramine is produced when drinking water containing ammonium is chlorinated. It has long been known that activated charcoal destroys monochloramine. However, exact data for the dimensioning of a dechloramination plant were lacking. Four different commercially available activated charcoals were characterized (size of particle, iodine number) and examined for their effectiveness in removing monochloramines. The degradation of monochloramine by active charcoal is based on a chemical reaction of first order between carbon and monochloramine. The types of activated charcoal considerably differ in terms of reaction velocity, due in part to the mean granular size. The final concentration of the monochloramines is influenced only by their length of stay in the activated carbon filter, the temperature and the inflow concentration. A mathematical model describes the dependence of the degradation rate of the monochloramines on various factors. With its aid a nomogram can be established with which, simply and quickly, the activated charcoal needed in a concrete case can be determined.

Charcoal↗
PubMedDate not supplied

CT scanning phantom for normalization of infant brain attenuation.

The x-ray attenuation values of brain studied with computed tomography (CT) are strikingly affected by the ages of the subjects. Premature neonates, for example, may have brain attenuation values 20-30 H below adult values. These lower attenuation values for developing compared with adult brain can be ascribed partly to machine-related effects (beam-hardening, adult algorithms, scanning geometry, etc.). A scanning phantom made from aluminum was developed that can be used to develop a nomogram for any particular scanner from which normalized brain attenuation may be derived for any small head size. Using this nomogram, predicted neonatal attenuations are still 10-15 H higher than those actually observed in scanning neonates. The model predicts that, at the most, 3-4 H of this discrepancy can be accounted for by less beam-hardening from the lower bone attenuation of the thinner developing skull. Presumably, the rest is from a lower brain density in neonates (higher water content). By normalizing to cerebrospinal fluid (water) with special care to avoid partial-volume artifacts, one can predict attenuation values for developing brain more accurately.

Aluminum↗
PubMed1975

A mathematical single-pool model for short time haemodialysis.

The objective was to reduce treatment time while the average concentration of all molecular sizes was maintained equal to that in a standard treatment. A nomogram based on the one-pool patient-dialyser system has been devised. The patient variables are presented in dimensionless forms, thus making on diagram suffice for all patients and all molecular sizes. Seven patients were first observed during a standard programme and later during an individually calculated short-time programme. The clinical parameters indicated equal treatment. The method was practical enough for clinical use.

Aged↗
PubMed2003

[Misdiagnosis of urinalysis due to in vivo formation of urinary stones].

The occurrence of biochemically unaltered urinalyses in patients with severe recurrent stone formation is not a rare observation in practice. The possible reasons for that phenomenon are manifold. We show that stone growth-related urinary depletion of lithogenic constituents caused by acute growth of urinary calculi in vivo can be an important reason for the observed phenomenon. The described process which can strongly influence the urinary composition occurs in any stone-bearing patient. Thus, it is strongly recommended that stone-related alterations be taken into account when interpreting the urinalyses of these patients. Based on simplified model assumptions, the extent of the expected chemical depletion effect can be calculated for any stone patient's urine sample. In two easy-to-use nomograms, we have combined the key parameters which govern the process, allowing the user a fast and easy estimation.

Adult↗
PubMed2004

Preoperative neural network using combined magnetic resonance imaging variables, prostate specific antigen, and Gleason score to predict prostate cancer recurrence after radical prostatectomy.

OBJECTIVE: An artificial neural network analysis (ANNA) was developed to predict the biochemical recurrence more effectively than regression models based on the combined use of pelvic coil magnetic resonance imaging (pMRI), prostate specific antigen (PSA) and biopsy Gleason score in patients with clinically organ-confined prostate cancer after radical prostatectomy (RP). METHODS: Two-hundred-and-ten patients undergoing retropubic RP with pelvic lymphadenectomy were evaluated. Predictive study variables included clinical TNM classification, preoperative serum PSA, biopsy Gleason score, transrectal ultrasound (TRUS) findings, and pMRI findings. The predicted result was a biochemical failure (PSA >or=0.1 ng/ml). Using a five-way cross-validation method, the predicted ability of ANNA for a validation set of 200 randomly selected patients was compared with those of Cox regression analysis and "Kattan nomogram" by area under the receiver operating characteristic curve (AUC) analysis. RESULTS: Seventy-three patients (35%) failed at median follow-up of 61 (mean: 60, range: 2-94) months. Using similar input variables, the AUC of ANNA (0.765, 95% Confidence Interval [CI]: 0.704-0.825) was comparable (p > 0.05) to those for Cox regression (0.738, 95%CI: 0.691-0.819) and Kattan nomogram (0.728, 95%CI: 0.644-0.819). Contrarily, adding the pMRI findings, the ANNA is significantly (p < 0.05) superior to any other predictive model (0.897, 95%CI: 0.841-0.977). The Gleason score represented the most influential predictor (relative weight: 2.4) of PSA recurrence, followed by pMRI (2.2), and PSA (2.0). CONCLUSION: ANNA is superior to regression models to predict accurately biochemical recurrence. The relative importance of pMRI and the utility of ANNA to predict the PSA failure in patients referred for RP must be confirmed in further trials.

Biomarkers, Tumor↗
PubMed2005

Time-dependent pharmacokinetics of cyclosporine (Neoral) in de novo renal transplant patients.

PURPOSE: A model for the large scale temporal trend in the oral bioavailability of microemulsion cyclosporine (Neoral) (CsA) is established, with dependence on post-(renal) transplantation day (PTD). METHODS: Twenty de novo adult renal transplant recipients were monitored for CsA administered orally q12 h. A model development group (11 patients, 315 blood concentration samples) was screened at 2 h (C(2); n = 92), 3 h (C(3); n = 56) and at predose troughs (C(min); n = 167) over periods of up to 75 days. The final model was tested in nine patients with C(min) (n = 580) monitored across 4-5 years. The doses varied between 100 and 538 mg with an apparent hyperbolic trend in C(2)/dose vs. PTD. A nonlinear mixed effects modelling (NONMEM) approach was used to obtain population and individual patient one-compartment pharmacokinetic (PK) parameters for oral CsA, which carry implicit the bioavailability (F). RESULTS: In the final PK model (PK-f) the F was modelled via a simple function for the temporal (days) trend of the bioavailability after transplantation as, F(f) = 1-alpha * exp(-lambda * PTD) resulting in a 28% reduction in the unexplained intra-individual variability. The population PK-f parameters were, for apparent clearance [mean, 95% confidence interval (interindividual CV%)] Cl/F(f) = 17.0 (13.8-20.2) L/h (27%), apparent central compartment volume of distribution, V/F(f) = 134 L (108-160) (28%), and lambda = 0.037/day (0.005-0.069) (120%). The absorption rate k(a) and the parameter alpha were approximated iteratively as 4/h and 0.62 respectively. The PK-f was structurally superior to the base model in explaining part of the within subject (occasion) variability and predicting the exposure surrogates C(2) and C(3). Also, the PK-f was better than the base model with Bayesian fitting of individual profiles in that group. CONCLUSION: The PTD-dependent relative bioavailability model provides a rational means of steering dose titration of CsA in de novo renal transplantation patients by removing the large scale PK adjustment signal, either through nomograms or as a Bayesian prior.

Adult↗
PubMed2025

Identification and external validation of a prognostic signature based on myeloid-derived suppressor cells-related LncRNAs to evaluate survival prognosis and treatment efficacy in invasive breast carcinoma.

BACKGROUND: Originating in the hematopoietic tissue, myeloid-derived suppressor cells (MDSCs) significantly contribute to tumor-related immunological processes. However, their relationship with long noncoding RNAs (lncRNAs) and breast cancer remains incompletely understood. In this study, we introduced MDSCs-associated lncRNAs as novel prognostic biomarkers to assess outcomes in patients with invasive breast carcinoma (BRCA). METHODS: Information regarding BRCA cases, including clinical and genomic details, was obtained from the TCGA repository. Predictive indicators were discovered, and their reliability underwent thorough verification. A clinically useful nomogram was developed following application-based validation. Additional investigations encompassed functional analysis, TMB assessment, TME profiling, immunotherapy efficacy forecasting, and drug sensitivity testing along with target identification. Long non-coding RNA expression was measured using reverse transcription quantitative PCR. RESULTS: A risk stratification model incorporating eight MDSCs-related lncRNAs effectively predicted patient outcomes. Kaplan-Meier (K-M) survival analysis clearly indicated a much worse prognosis among patients classified as high-risk (p&#xa0;<&#xa0;0.001). The nomogram accurately forecasted overall survival (OS). Analysis of functional enrichment revealed that pathways associated with epithelial cells showed activity among patients at higher risk. Characterization of the tumor microenvironment showed increased immune cell presence in those classified as low-risk. Conversely, individuals with greater risk displayed higher tumor mutational burden. TIDE and IPS analyses indicated superior immunotherapy responsiveness in the low-risk BRCA subgroup. Among 47 drugs with notable IC50 variations, Ribociclib, PD173074, KU-55933, NU7441, and nutlin-3a exhibited lower IC50 values within the low-risk group, whereas Lapatinib demonstrated greater efficacy among the high-risk group. Moreover, 10 potential therapeutic agents and their targets were predicted for high-risk patients. RT-qPCR validation confirmed the robustness of the model. CONCLUSIONS: We successfully verified a new model of molecular markers of MDSCs-related lncRNAs, offering critical insights for predicting outcomes and guiding therapeutic decisions in BRCA cases.

Bioinformatics↗
PubMed2006

Development and validation of nomograms for predicting residual tumor size and the probability of successful conservative surgery with neoadjuvant chemotherapy for breast cancer.

BACKGROUND: Neoadjuvant chemotherapy (NACT) increases the likelihood that breast conservation therapy for breast cancer patients will be successful. There is no available nomogram to predict breast conservation after NACT. The aim of the current study was to develop and validate nomograms for predicting residual tumor size and probability of a patient becoming eligible for breast conservation surgery after NACT. METHODS: A total of 1147 patients treated at M. D. Anderson Cancer Center (Houston, TX) and the Institut Gustave Roussy (Villejuif, France) who received anthracycline with or without paclitaxel NACT were included in the analysis. Clinicopathologic data from 1 series were used to construct logistic regression models for breast conservation and residual tumor size < 3 cm after NACT and were validated on an independent series. RESULTS: The discrimination and the calibration of the nomogram for predicting the probability of residual tumor size < 3 cm after anthracycline-based NACT were good when applied to the validation set (concordance index = 0.79; U-index = 10(-3)). The discrimination of the nomogram for predicting eligibility for breast conservation therapy was also good (concordance index = 0.67). However, the calibration had to be adjusted to take into account global rates of breast conservation surgery. A second nomogram adapted to preoperative chemotherapy regimens containing paclitaxel was established. The concordance index of the nomogram for predicting breast conservation was 0.71 (P < 10(-6)) for the independent dataset and the calibration was also good. The confrontation of both nomograms showed that predictions were highly correlated (r = 0.97), suggesting that eligibility for breast conservation therapy was independent of the preoperative chemotherapy regimen used. CONCLUSIONS: Nomograms were developed for breast cancer patients who received NACT to predict residual tumor size and whether the patient would thus become eligible for breast conservation therapy. These tools may be useful when counseling patients about treatment options, and a web-based interface is now available to help guide patients and physicians in these decisions.

Anthracyclines↗
PubMed2004

Pretreatment probability model for predicting outcome after intraarterial chemoradiation for advanced head and neck carcinoma.

BACKGROUND: Concurrent chemoradiation is being used increasingly to treat patients with advanced-stage head and neck carcinoma. In the current study, a clinical nomogram was developed to predict local control and overall survival rates for individual patients who will undergo chemoradiation. METHODS: Ninety-two consecutive patients with UICC TNM Stage III/IV squamous cell carcinoma of the oral cavity, oropharynx, hypopharynx, and supraglottic larynx were treated with selective-targeted chemoradiation (acronym: RADPLAT). All living patients had a minimum follow-up of 2 years. In addition to general factors, the following parameters were analyzed in a multivariable analysis: primary tumor volume, lymph node tumor volume, total tumor volume, lowest involved neck level, comorbidity, pretreatment hemoglobin level, pretreatment weight loss, and unilateral/bilateral intraarterial infusion. Relevant factors for local control and survival were analyzed using the Cox proportional hazards model. RESULTS: At 5 years, the local control and overall survival rates for the whole group were 60% and 38%, respectively. Primary tumor volume (hazard ratio [HR], 1.03; P = 0.01) and unilateral infusion (HR, 5.05; P = 0.004) were found to influence local control significantly. Using tumor volume as a continuous variable, an adjusted risk ratio of 1.026 was found, indicating that each 1-cm(3) increase in volume was associated with a 2.6% decrease in probability of local control. Primary tumor volume (HR, 1.01; P = 0.003), comorbidity (American Society of Anesthesiologists [ASA] physical status 1 vs. > 1; HR, 2.47; P = 0.01), lowest involved neck level (HR, 3.45; P = 0.007), and pretreatment weight loss > 10% (HR, 2.04; P = 0.02) were found to be significant predictors of worse overall survival. Variables from the multivariable analysis were used to develop a nomogram capable of predicting local control and overall survival. CONCLUSIONS: Tumor volume was found to play a significant role in predicting local control and overall survival in patients with advanced-stage head and neck carcinoma who were treated with targeted chemoradiation. The nomograms may be useful for pretreatment selection of patients with advanced-stage head and neck carcinoma.

Adult↗
PubMed1998

Amino acid signatures in the normal cat retina.

PURPOSE: To establish a nomogram of amino acid signatures in normal neurons, glia, and retinal pigment epithelium (RPE) of the cat retina, guided by the premise that micromolecular signatures reflect cellular identity and metabolic integrity. The long-range objective was to provide techniques to detect subtle aberrations in cellular metabolism engendered by model interventions such as focal retinal detachment. METHODS: High-performance immunochemical mapping, image registration, and quantitative pattern recognition were combined to analyze the amino acid contents of virtually all cell types in serial 200-nm sections of normal cat retina. RESULTS: The cellular cohorts of the cat retina formed 14 separable biochemical theme classes. The photoreceptor --> bipolar cell --> ganglion cell pathway was composed of six classes, each possessing a characteristic glutamate signature. Amacrine cells could be grouped into two glycine- and three gamma-aminobutyric acid (GABA)-dominated populations. Horizontal cells possessed a distinctive GABA-rich signature completely separate from that of amacrine cells. A stable taurine-glutamine signature defined Müller cells, and a broad-spectrum aspartate-glutamate-taurine-glutamine signature was present in the normal RPE. CONCLUSIONS: In this study, basic micromolecular signatures were established for cat retina, and multiple metabolic subtypes were identified for each neurochemical class. It was shown that virtually all neuronal space can be accounted for by cells bearing characteristic glutamate, GABA, or glycine signatures. The resultant signature matrix constitutes a nomogram for assessing cellular responses to experimental challenges in disease models.

Alanine↗
PubMed2024

Construction and Analysis of a Mitochondrial Metabolism-Related Prognostic Model for Breast Cancer to Evaluate Survival and Immunotherapy.

As one of the most prevalent malignancies among women, breast cancer (BC) is tightly linked to metabolic dysfunction. However, the correlation between mitochondrial metabolism-related genes (MMRGs) and BC remains unclear. The training and validation datasets for BC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. MMRG-related data were obtained from the Molecular Signatures Database. A risk score prognostic model incorporating MMRGs was established based on univariate, LASSO, and multivariate Cox regression analyses. Independent factors affecting BC prognosis were identified through regression analysis and presented in a nomogram. Single-sample gene set enrichment analysis was employed to assess the immune levels of high-risk (HR) and low-risk (LR) groups. The sensitivity of BC patients in the two groups to common anti-tumor drugs was evaluated by utilizing the Genomics of Drug Sensitivity in Cancer database. 12 MMRGs significantly associated with survival were selected from 1234 MMRGs. A 12-gene risk score prognostic model was built. In the multivariate regression analysis incorporating classical clinical factors, the MMRG-related risk score remained an independent prognostic factor. As revealed by tumor immune microenvironment analysis, the LR group with higher survival rates had elevated immune levels. The drug sensitivity results unmasked that the LR group demonstrated higher sensitivity to Irinotecan, Nilotinib, and Oxaliplatin, while the HR group demonstrated higher sensitivity to Lapatinib. The development of MMRG characteristics provides a comprehensive understanding of mitochondrial metabolism in BC, aiding in the prediction of prognosis and tumor microenvironment, and offering promising therapeutic choices for BC patients with different MMRG risk scores.

Humans↗
PubMed2002

Stature estimation from long bone lengths in Bulgarians.

The purpose of the present study is to develop a new regression procedure for predicting the stature from the length of the limb long bones taking into account sex- and age-related changes. The statures and lengths of humerus (H), tibia (T) and fibula (Fi) were measured in 416 forensic cases (286 male and 130 female adult Bulgarians). The measurements of the bones and the stature were made on cadavers before autopsy. Stature regression analysis is performed for each of the three bones, as well as for a combination of humerus and tibia. There is a possibility of applying five different procedures with regard of the effect of aging on stature decrease. Resulting models are tested for outliers and heteroskedasticity. Regression parameters, their standard deviations, standard error of the regression. Anova test for model adequacy and the covariance matrix of regression parameters are calculated. The confidence intervals of the error term are determined. Nomograms for a direct application of the results are constructed where it is convenient. The method provides better and more reliable results of stature estimation for the Bulgarian population than other formulae.

Adult↗
PubMed1987

[Predicting the dynamics of elevated arterial pressure in 12--13-year-old children of Kaunas and Berlin].

The increased arterial blood pressure (BP) variation between 12-13 and 15-16 years of age was examined in 119 schoolchildren of Kaunas and 169 schoolchildren of Berlin. The 90th percentile for systolic and/or diastolic BP was used as a criterion of increased arterial BP. The data were treated by multivariate logistical regression analysis. Independent samples were used to construct and test the model. The reproducibility of increased arterial BP was shown to be affected by physical developmental status as well as baseline systolic and diastolic BP, the influence being more pronounced in girls, as compared to boys. The derived formulas are presented as a nomogram to facilitate their practical application.

Adolescent↗
PubMed2002

Prediction of progression: nomograms of clinical utility.

It is difficult to determine the pathologic stage of a clinically localized prostate cancer by physical examination or imaging studies. Consequently, clinicians rely on predictive models that estimate the probability of lymph node metastases and other pathologic features from clinical factors such as the clinical T stage, the grade in the biopsy specimen, and the serum prostate-specific antigen level. These models do not, however, directly predict prognosis. In developing a tool for predicting the probability that prostate cancer might recur after treatment, we took a novel approach that focused on the risk for the individual patient. In particular, we chose to develop a tool that calculates a continuous probability of recurrence rather than placing the patient in a risk group. This represents a fundamental departure from the classical goal of staging; a departure we argue is long overdue. Clinically localized prostate cancer patients deserve the most accurate and tailored predictions available, which current staging systems do not provide. Such an individualized approach should add value in medical decision making whenever an accurate prediction of the outcome may guide treatment selection.

Aged↗
PubMedDate not supplied

Predialysis urea concentration is sufficient to characterize hemodialysis adequacy.

Mathematical description of urea kinetics for a week showed that, under steady state conditions (i.e., total removal equals total synthesis), any predialysis urea concentration is expressed as a linear function of specific urea generation (G/V) and of dialysis schedule timing and sessional Kt/V (product of clearance, K, and session time, t, divided by the urea distribution volume, V). It also predicts that TACurea is proportional to the predialysis concentrations. The ratio between the two depends linearly on delivered weekly dialysis dose ([wDD] = T(G/V)/TACurea, with T the number of hours in 1 week). These hypotheses have been tested by retrospectively analyzing urea kinetc modelling data that include all predialysis and post dialysis concentrations of 163 patient-weeks. All patients were anuric, and dialysis frequency was thrice weekly. Accuracy is assessed with regression analysis between database numbers and computed values. The theoretical ratio between midweek concentration and TACurea (1.43) is close to the computed ratio (1.46, r2 = 0.909). TACurea (slope = 1.002, r2 = 0.997), specific generation rate G/V as a precursor to PCRn (slope = 1.007, r2 = 0.985), and wDD (slope = 1.002, r2 = 0.909) are all accurately computed from predialysis concentrations. To aid in the determination of the ratio for the different predialysis, concentrations using wDD a nomogram is included.

Models, Theoretical↗
PubMed2026

Prognostic significance of DNA damage response-related markers in esophageal squamous cell carcinoma using machine learning approaches.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) lacks reliable prognostic biomarkers. Homologous recombination deficiency (HRD) has been implicated in genomic instability across multiple cancers, but its prognostic significance in ESCC remains unexplored. This study aimed to evaluate HRD score as a prognostic biomarker and develop a machine learning-based predictive model for ESCC. METHODS: Transcriptomic and clinical data from 78 ESCC patients were obtained from The Cancer Genome Atlas (TCGA) and randomly split into training (70%) and test (30%) cohorts. Prognostic models were constructed using 112 machine learning algorithm combinations based on DNA damage response (DDR)-related genes. Gene set enrichment analysis (GSEA), somatic mutation profiling, and immune cell infiltration estimation via CIBERSORT were performed to characterize HRD-associated molecular features. RESULTS: High HRD scores were significantly associated with poorer overall survival (P<0.05). Among 112 algorithm combinations, the survival support vector machine (Survival-SVM) model demonstrated optimal performance [training concordance index (C-index): 0.741; test C-index: 0.708], identifying six hub genes: PARP1, MBD4, TELO2, NSMCE3, SMUG1, and BABAM1. A nomogram incorporating risk score (RS) and clinical variables achieved strong predictive accuracy for 1- to 3-year survival [area under the curve (AUC) >0.7]. High-HRD tumors exhibited distinct mutational patterns (TP53 and TTN) and enriched glutathione metabolism and cytochrome P450 pathways. Immune infiltration analysis revealed significant differences in plasma cell and neutrophil infiltration between risk groups (P<0.05), suggesting HRD-associated immune microenvironment remodeling. CONCLUSIONS: We developed a novel HRD-based prognostic model incorporating six DDR-related genes that demonstrates robust predictive performance in ESCC. HRD score is identified as an independent prognostic factor associated with genomic instability, immune microenvironment alterations, and clinical outcomes. These findings provide a theoretical basis for personalized treatment strategies, including potential applications of PARP inhibitors and immunotherapy in ESCC.

Esophageal squamous cell carcinoma (ESCC)↗