model-based optimization
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Diet, stress, genetics, and a sedentary lifestyle may all contribute to heart disease rates. Although recent studies propose comprehensive automated diagnostic systems, these systems tend to focus on one aspect, such as feature selection, prioritization, or predictive accuracy. A more complete approach that considers all of these factors can improve the efficiency of a cardiac prediction system. This study uses an appropriate strategy to overcome potential network design problems, design challenges, overfitting, and lack of robustness that can interfere with system performance. The research introduces an ideally designed deep trust network called ID-DTN to improve system performance. The Ruzzo-Tompa method is used to eliminate noncontributory features. The Seagull Optimization Algorithm (SOA) is introduced to optimize the trust depth network to achieve optimal network design. The study scrutinizes the deep trust network (ID-DTN) and the restricted Boltzmann machine (RBM) and sheds light on the system's operation. This proposal can optimize both network architecture and feature selection, which is the main novelty. The proposed method is analyzed using the below-mentioned metrics: Matthew's correlation coefficient, F1 score, accuracy, sensitivity, specificity, and accuracy. ID-DTN performs well compared to other state-of-the-art methods. The validation results confirm that the proposed method improves the prediction accuracy to 97.11% and provides reliable recommendations for patients with cardiovascular disease.
- MeSH
- algoritmy * MeSH
- lidé MeSH
- nemoci srdce * diagnóza MeSH
- neuronové sítě MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
554 s.
- Klíčová slova
- struktury chemické-aktivita biologická,
- Konspekt
- Biochemie. Molekulární biologie. Biofyzika
- NLK Obory
- chemie, klinická chemie
- biochemie
Míru spokojenosti klientů ovlivňuje kvalita ošetřovatelských služeb s hlavním ukazatelem pečovatelského chování. Tato studie si klade za cíl analyzovat vliv kulturně založeného modelu péče na pracovní život a kvalitu pečovatelských služeb. U kontrolní skupiny byl použit kvaziexperimentální pre-post design. Této studie se zúčastnilo 60 zdravotních sester, které pracovaly v nemocnicích. Proměnnými v tomto šetření byly kulturně založený model péče, kvalita ošetřovatelské péče a také kvalita pracovního života ošetřovatelů. Výsledky různých post-testových skóre ve dvou skupinách ukázaly významný rozdíl ve všech ukazatelích kvality pracovního života sester (QNWL) a kvality ošetřovatelských služeb. Aplikace kulturně založeného modelu péče prakticky přispívá ke zkvalitnění služeb ošetřovatelské péče, a to poskytováním referencí sestrám při zlepšování pečovatelského chování tak, aby pacienti pociťovali kvalitu ošetřovatelských služeb optimálně.
The level of client satisfaction is influenced by the quality of nursing services with the main indicator of caring behavior. This study aims to analyze the effect of the culture-based caring model on work life and the quality of nursing services. A quasi-experimental pre-post-test design was used with the control group. This study involved 60 nurses who worked in hospitals. Culture-based care model, quality of nursing care, as well as the quality of nursing work life, were variables in this investigation. The results of the different post-test scores in the two groups showed a significant difference in all indicators of the quality of nursing work life (QNWL) and the quality of nursing services. The application of a culture-based caring model makes a practical contribution to the improvement of nursing care services, namely by providing references to nurses in improving caring behavior so that patients can feel the quality of nursing services optimal.
In silico methods like molecular docking and pharmacophore modeling are established strategies in lead identification. Their successful application for finding new active molecules for a target is reported by a plethora of studies. However, once a potential lead is identified, lead optimization, with the focus on improving potency, selectivity, or pharmacokinetic parameters of a parent compound, is a much more complex task. Even though in silico molecular modeling methods could contribute a lot of time and cost-saving by rationally filtering synthetic optimization options, they are employed less widely in this stage of research. In this review, we highlight studies that have successfully used computer-aided SAR analysis in lead optimization and want to showcase sound methodology and easily accessible in silico tools for this purpose.
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
AlphaFold is an artificial intelligence approach for predicting the three-dimensional (3D) structures of proteins with atomic accuracy. One challenge that limits the use of AlphaFold models for drug discovery is the correct prediction of folding in the absence of ligands and cofactors, which compromises their direct use. We have previously described the optimization and use of the histone deacetylase 11 (HDAC11) AlphaFold model for the docking of selective inhibitors such as FT895 and SIS17. Based on the predicted binding mode of FT895 in the optimized HDAC11 AlphaFold model, a new scaffold for HDAC11 inhibitors was designed, and the resulting compounds were tested in vitro against various HDAC isoforms. Compound 5a proved to be the most active compound with an IC50 of 365 nM and was able to selectively inhibit HDAC11. Furthermore, docking of 5a showed a binding mode comparable to FT895 but could not adopt any reasonable poses in other HDAC isoforms. We further supported the docking results with molecular dynamics simulations that confirmed the predicted binding mode. 5a also showed promising activity with an EC50 of 3.6 μM on neuroblastoma cells.
- MeSH
- histondeacetylasy * metabolismus MeSH
- inhibitory histondeacetylas * farmakologie chemie chemická syntéza MeSH
- lidé MeSH
- molekulární struktura MeSH
- nádorové buněčné linie MeSH
- neuroblastom * farmakoterapie patologie MeSH
- protinádorové látky * farmakologie chemie chemická syntéza MeSH
- racionální návrh léčiv * MeSH
- simulace molekulární dynamiky MeSH
- simulace molekulového dockingu MeSH
- umělá inteligence MeSH
- vztah mezi dávkou a účinkem léčiva MeSH
- vztahy mezi strukturou a aktivitou MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Východisko. Potenciální výhody a rizika nových operačních technik se mohou u jednotlivých pacientů projevit rozdílně. Cílem práce bylo na základě spolehlivé predikce pooperačních komplikací ověřit možnost vytvoření modelu pro optimalizaci operační techniky u souboru pacientů operovaných pro karcinom rekta. Materiál a metody. Do studie byli zařazeni pacienti operováni v průběhu pěti let pro karcinom konečníku laparoskopickou nebo otevřenou technikou. Matematické modely predikce pooperačních komplikací jednotlivých operačních technik byly odvozeny od skórovacího systému Physiological and Operative Severity Score for enUmeration of Mortality and morbidity (POSSUM). Spolehlivost predikce pro danou techniku byla otestována a pacienti souboru byli analyzováni s ohledem na „vhodnost“ použité operační techniky. Výsledky. Do studie bylo zařazeno 91 pacientů operovaných otevřenou technikou a 67 pacientů operovaných laparoskopicky s výskytem 45 %, resp. 39 % pooperačních komplikací. Z testovaných proměnných byly pro vznik pooperačních komplikací statisticky významné „kardiální příznaky“ a „závažnost operačního výkonu “ pro otevřené operace, „leukocyty“ a „závažnost operačního výkonu“ pro laparoskopické výkony. Modely predikce postavené na těchto proměnných vykázaly statisticky vysokou spolehlivost. V celém souboru by při ideální volbě operační techniky komplikace teoreticky poklesly o 36 %. Závěr. Spolehlivá predikce pooperačních komplikací může být efektivním nástrojem přizpůsobení chirurgické léčby individuálnímu pacientovi.
Background. Potential benefits and risks of new operation techniques can show up differently in the individual patients. The aim of this pilot study was to verify on the basis of reliable prediction of postoperative complications the possibility to create model for optimization of the operation technique in the cohort of patients operated for rectal carcinoma. Material and methods. The study involved patients operated in the course of five years for rectal carcinoma by means of laparoscopic or open technique. The mathematical models of prediction of postoperative complications of individual operative techniques were derived from the Physiological and Operative Severity Score for enUmeration of Mortality and Morbidity scoring system (POSSUM). The reliability of prediction for the given technique was tested and the patients of the cohort were analyzed with regard to the “suitability” of the operation technique used. Results. The study involved 91 patients operated using open technique and 67 patients operated laparoscopically with the occurrence of 45% and 39% of the postoperative complications respectively. The statistically relevant variables tested for the occurrence of postoperative complications were the “cardiac symptoms” and “severity of the surgery” for open operations, and “leukocytes” and “severity of the surgery” for the laparoscopic operations. The prediction models based on these variables showed statistically high reliability. The complications in the entire cohort would in case of ideal selection of surgical technique drop by 36%. Conclusion. Reliable prediction of the postoperative complications can be potentially effective tool to optimize surgical treatment for an individual patient.
Objectives: This study is aimed to achieve the rapid optimization of the input feature subset that satisfies the expert's point of view and enhance the prediction performance of the early prediction model for fatty liver disease (FLD). Methods: We explore a large-scale and high-dimension dataset coming from a northern Taipei Health Screening Center in Taiwan, and the dataset includes data of 12,707 male and 10,601 female patients processed from around 500,000 records from year 2009 to 2016. We propose three eigenvector-based feature selections taking the Intersection of Union (IoU) and the Coverage to determine the sub-optimal subset of features with the highest IoU and the Coverage automatically, use various long short-term memory (LSTM) related classifiers for FLD prediction, and evaluate the model performance by the test accuracy and the Area Under the Receiver Operating Characteristic Curve (AUROC). Results: Our eigenvector-based feature selection EFS- TW has the highest IOU and the Coverage and the shortest total computing time. For comparison, the highest IOU, the Coverage, and computing time are 30.56%, 45.83% and 260 seconds for female, and that of a benchmark, sequential forward selection (SFS), are 9.09%, 16.67% and 380,350 seconds. The AUROC with LSTM, biLSTM, Gated Recurrent Unit (GRU), Stack-LSTM, Stack-biLSTM are 0.85, 0.86, 0.86, 0.86 and 0.87 for male, and all 0.9 for female, respectively. Conclusion: Our method explores a large-scale and high-dimension FLD dataset, implements three efficient and automatic eigenvector-based feature selections, and develops the model for early prediction of FLD efficiently.
BACKGROUND: Particularly in the pediatric clinical pharmacology field, data-sharing offers the possibility of making the most of all available data. In this study, we utilize previously collected therapeutic drug monitoring (TDM) data of term and preterm newborns to develop a population pharmacokinetic model for phenobarbital. We externally validate the model using prospective phenobarbital data from an ongoing pharmacokinetic study in preterm neonates. METHODS: TDM data from 53 neonates (gestational age (GA): 37 (24-42) weeks, bodyweight: 2.7 (0.45-4.5) kg; postnatal age (PNA): 4.5 (0-22) days) contained information on dosage histories, concentration and covariate data (including birth weight, actual weight, post-natal age (PNA), postmenstrual age, GA, sex, liver and kidney function, APGAR-score). Model development was carried out using NONMEM® 7.3. After assessment of model fit, the model was validated using data of 17 neonates included in the DINO (Drug dosage Improvement in NeOnates)-study. RESULTS: Modelling of 229 plasma concentrations, ranging from 3.2 to 75.2mg/L, resulted in a one compartment model for phenobarbital. Clearance (CL) and volume (Vd) for a child with a birthweight of 2.6kg at PNA day 4.5 was 0.0091L/h (9%) and 2.38L (5%), respectively. Birthweight and PNA were the best predictors for CL maturation, increasing CL by 36.7% per kg birthweight and 5.3% per postnatal day of living, respectively. The best predictor for the increase in Vd was actual bodyweight (0.31L/kg). External validation showed that the model can adequately predict the pharmacokinetics in a prospective study. CONCLUSION: Data-sharing can help to successfully develop and validate population pharmacokinetic models in neonates. From the results it seems that both PNA and bodyweight are required to guide dosing of phenobarbital in term and preterm neonates.
- MeSH
- fenobarbital aplikace a dávkování MeSH
- kojenec MeSH
- lidé MeSH
- monitorování léčiv metody MeSH
- novorozenec nedonošený MeSH
- novorozenec MeSH
- prospektivní studie MeSH
- šíření informací metody MeSH
- vztah mezi dávkou a účinkem léčiva MeSH
- Check Tag
- kojenec MeSH
- lidé MeSH
- mužské pohlaví MeSH
- novorozenec MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
This study aimed to develop a vancomycin population pharmacokinetic model in obese adult patients treated with intermittent haemodialysis and propose a model-based loading dose strategy ensuring attainment of newly recommended AUC-based PK/PD target. Retrospective cross-sectional analysis was performed among obese haemodialysis dependent adult patients treated with intravenous vancomycin. A pharmacokinetic population model was developed using a nonlinear mixed-effects modelling approach and Monte Carlo simulations were used to identify the optimal loading dose for PK/PD target attainment during the first 48 h of treatment. Therapeutic drug monitoring data from 27 patients with a BMI of 30.2-52.9 kg/m2 were analysed. Among all tested variables, only LBM as a covariate of vancomycin Vd significantly improved the model, while vancomycin CL did not correlate with any of the tested variables. The median (IQR) value from the conditional mean of individual estimates of Vd and CL was 68.4 (56.6-84.2) L and 0.86 (0.79-0.90) L/h, respectively. To ensure optimal vancomycin exposure during the first 48 h of therapy, the vancomycin loading dose of 1500, 1750, 2000, 2250, 2500 and 2750 mg should be administered to obese patients with a lean body mass of ˂50, 50-60, 60-70, 70-80, 80-85 and >85 kg, respectively.
- MeSH
- antibakteriální látky * farmakokinetika aplikace a dávkování MeSH
- biologické modely MeSH
- dialýza ledvin * MeSH
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- metoda Monte Carlo MeSH
- monitorování léčiv MeSH
- obezita * komplikace MeSH
- průřezové studie MeSH
- retrospektivní studie MeSH
- senioři MeSH
- vankomycin * farmakokinetika aplikace a dávkování MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis is a heterogenous autoimmune disease. While traditionally stratified into two conditions, granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA), the subclassification of ANCA-associated vasculitis is subject to continued debate. Here we aim to identify phenotypically distinct subgroups and develop a data-driven subclassification of ANCA-associated vasculitis, using a large real-world dataset. METHODS: In the collaborative data reuse project FAIRVASC (Findable, Accessible, Interoperable, Reusable, Vasculitis), registry records of patients with ANCA-associated vasculitis were retrieved from six European vasculitis registries: the Czech Registry of ANCA-associated vasculitis (Czech Republic), the French Vasculitis Study Group Registry (FVSG; France), the Joint Vasculitis Registry in German-speaking Countries (GeVas; Germany), the Polish Vasculitis Registry (POLVAS; Poland), the Irish Rare Kidney Disease Registry (RKD; Ireland), and the Skåne Vasculitis Cohort (Sweden). We performed model-based clustering of 17 mixed-type clinical variables using a parsimonious mixture of two latent Gaussian variable models. Clinical validation of the optimal cluster solution was made through summary statistics of the clusters' demography, phenotypic and serological characteristics, and outcome. The predictive value of models featuring the cluster affiliations were compared with classifications based on clinical diagnosis and ANCA specificity. People with lived experience were involved throughout the FAIRVASVC project. FINDINGS: A total of 3868 patients diagnosed with ANCA-associated vasculitis between Nov 1, 1966, and March 1, 2023, were included in the study across the six registries (Czech Registry n=371, FVSG n=1780, GeVas n=135, POLVAS n=792, RKD n=439, and Skåne Vasculitis Cohort n=351). There were 2434 (62·9%) patients with GPA and 1434 (37·1%) with MPA. Mean age at diagnosis was 57·2 years (SD 16·4); 2006 (51·9%) of 3867 patients were men and 1861 (48·1%) were women. We identified five clusters, with distinct phenotype, biochemical presentation, and disease outcome. Three clusters were characterised by kidney involvement: one severe kidney cluster (555 [14·3%] of 3868 patients) with high C-reactive protein (CRP) and serum creatinine concentrations, and variable ANCA specificity (SK cluster); one myeloperoxidase (MPO)-ANCA-positive kidney involvement cluster (782 [20·2%]) with limited extrarenal disease (MPO-K cluster); and one proteinase 3 (PR3)-ANCA-positive kidney involvement cluster (683 [17·7%]) with widespread extrarenal disease (PR3-K cluster). Two clusters were characterised by relative absence of kidney involvement: one was a predominantly PR3-ANCA-positive cluster (1202 [31·1%]) with inflammatory multisystem disease (IMS cluster), and one was a cluster (646 [16·7%]) with predominantly ear-nose-throat involvement and low CRP, with mainly younger patients (YR cluster). Compared with models fitted with clinical diagnosis or ANCA status, cluster-assigned models demonstrated improved predictive power with respect to both patient and kidney survival. INTERPRETATION: Our study reinforces the view that ANCA-associated vasculitis is not merely a binary construct. Data-driven subclassification of ANCA-associated vasculitis exhibits higher predictive value than current approaches for key outcomes. FUNDING: European Union's Horizon 2020 research and innovation programme under the European Joint Programme on Rare Diseases.
- MeSH
- ANCA-asociované vaskulitidy * klasifikace diagnóza epidemiologie krev imunologie MeSH
- dospělí MeSH
- kohortové studie MeSH
- lidé středního věku MeSH
- lidé MeSH
- mikroskopická polyangiitida klasifikace epidemiologie krev diagnóza imunologie MeSH
- registrace * statistika a číselné údaje MeSH
- senioři MeSH
- shluková analýza MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Evropa MeSH
