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Assessment of Intra-cranial Pressure After Severe Traumatic Brain Injury by Transcranial Doppler Ultrasonography
No full textPRIMARY OBJECTIVE: To investigate the potential of transcranial Doppler ultrasonography in estimating post-traumatic intra-cranial pressure early after severe traumatic brain injury.
RESEARCH DESIGN: The group of 24 patients was analysed for the observation of an early post-traumatic cerebral haemodynamic by middle cerebral artery blood velocity measuring.
METHODS AND PROCEDURES: The standard method of measuring the mean blood middle cerebral artery velocity by transcranial Doppler ultrasonic device was performed.
MAIN OUTCOMES AND RESULTS: The increased duration of intra-cranial hypertension correlated to the middle cerebral artery low blood velocity (p = 0.042; r = -0.498) (n = 17) and to elevated pulsatility indices (p = 0.007; r = 0.753) (n = 11) significantly. The increased duration of lowered cerebral perfusion pressure correlated to the middle cerebral artery low blood velocity significantly (p = 0.001; r = -0.619) (n = 24).
CONCLUSIONS: The significance of transcranial Doppler ultrasonography as a method to estimate an early post-traumatic intra-cranial pressure after severe brain injury was confirmed. This simple and non-invasive technique could be easily used in daily clinical practice and precede intra-cranial pressure monitoring in selected patients
Computer-assisted Surgery and Computer-assisted Telesurgery in Otorhinolaryngology
No full textSurgical preparation is enhanced by the availability of computer-generated three-dimensional models that allow surgeons to explore the surgical field in various projections prior to an actual operation. In fact, with adequate computed tomography images, an entire operation can be simulated beforehand so that surgeons can plan the safest and most effective approach and be prepared to avoid or overcome obstacles during the actual procedure. Also, computer technology allows surgeons to conduct remote consultations and to even perform telesurgery--that is, to operate on a patient from a great distance. In this article, we describe our experience with computer-assisted local and remote endoscopic sinus surgery in Croatia
Use of Information Theory and Numerical Taxonomy Methods for Evaluating the Quality of Thin-layer Chromatographic Separations of Flavonoids and Phenolic Acids of Rhamni Cathartici Fructus
No full textA rational selection of a restricted set from fifteen available chromatographic systems for the separation of flavonoids and phenolic acids identified in the methanolic extract of Rhamni cathartici fructus is discussed. Series of mathematical techniques
for the evaluation of solvents and solvent combinations in thinlayer chromatography of flavonoids and phenolic acids have been investigated. The chromatographic systems are classified according to their mutual resemblance by numerical taxonomy
techniques. The selection criterion in the groups, obtained by numerical taxonomy classification, is the information content or
discriminating power. The numerical taxonomic and information theoretical selection procedures are compared and their respective advantages and disadvantages discussed
Heterotopic Trigeminal Pregnancy in Infertile Women after Ovulation Stimulation and Embolisation of a Uterine Myoma
No full textHeterotopic pregnancy is a simultaneous occurrence of intra-uterine pregnancy and ectopic pregnancy. The incidence of ectopic pregnancy has increased as a consequence of assisted reproduction and ovulation stimulation agents. In this report, we describe the case of a 34-year-old nulliparous woman who became pregnant after ovulation induction with clomiphene. According to her gynaecologic history, she had embolisation of a uterine myoma. The report presents a case of ectopic and twin intra-uterine pregnancy. After total laparoscopic salpingectomy, she had normal intra-uterine pregnancy
Bridging the gap Between Microarray Technology and Routine Clinical Diagnostics: a Random Forest Approach to the Gene Expression Profile Dimensionality Reduction
No full textUvod: Analiza genske ekspresije zasnovana na mikropostrojima je tijekom proteklog desetljeća prepoznata kao koristan alat od strane znanstvene zajednice, ali nije ušla u rutinsku dijagnostičku primjenu. Kako je skupa i podložna značajnim eksperimentalnim varijacijama, na trenutnom tehnološkom stupnju razvoja ta tehnologija nije prikladna za rutinske kliničko-dijagnostičke primjene. U svrhu premošćivanja jaza između mogućnosti navedene tehnologije i potreba kliničke dijagnostike razvijeni su različiti računalni alati za smanjenje dimenzionalnosti. Njihova osnovna svrha je odabir malog skupa kandidata za biomarkere iz ogromnog skupa sadržanog u profilima genske ekspresije prikladnog za rutinsko postavljanje dijagnoze.
Cilj: Slučajna šuma (engl. Random Forest, RF) se nametnula kao pouzdan pretkazatelj. Ipak, njene su mogućnosti u odabiru relevantnih gena privukle manje pažnje. Cilj ove studije je evaluacija prikladnosti na RF-u zasnovanoga odabira biomarkera iz skupova genskih profila. Tri takva skupa, preuzeta iz literature, prikupljena tijekom manjih kliničkih pokusa izabrana su u navedenu svrhu.
Rezultati: Dobiveni rezultati ukazuju da RF može lako identificirati dobre uni-varijatne klasifikatore, tj. pojedinačne biomarkere kada je složenost skupa mala. Za nešto složenije probleme pouzdani dvodimenzionalni klasifikator može se također pronaći. Ipak, ako je odnos između dijagnoze/prognoze i profila genske ekspresije vrlo složen ili ako je skup premalen, na RF-u zasnovano smanjenje dimenzionalnosti ne omogućava odabir pouzdanog skupa kandidata za biomarkere.
Zaključci: Unutar ograničenja zadanih složenošću skupa RF predstavlja prikladan alat za izbor kandidata za biomarkere.Introduction: Although recognized as a valuable tool by scientific community, microarray based gene expression profiling has not accessed routine diagnostic application during the last decade. Since this approach is expensive and prone to substantial experimental variation, it is not suited for routine clinical diagnostic purposes at the current state of technology. In order to bridge that gap, different computational dimensionality reduction tools have been developed. The principle of their application is selection of a limited set of biomarker candidates from huge gene expression profiles appropriate for routine diagnostic assessment.
Aim: Random forest (RF) has been established as a reliable predictor. However, its relevant gene selection capabilities gained less attention. The aim of this study was to evaluate suitability of RF for biomarker selection from gene expression profile datasets. Three datasets taken from literature, obtained during small-scale clinical experiments, were chosen for that purpose.
Results: The results obtained show that RF could easily identify good uni-variate classifiers, i.e. single biomarkers when the problem at hand is of low complexity. For more complex problem a reliable two-dimensional classifier candidate could be also found by this approach. However, when the relationship between diagnosis/prognosis and gene expression profiling results are highly complex or the dataset is too small, RF-based dimensionality reduction fails to select a reliable set of biomarker candidates.
Conclusions: Within dataset complexity limitations, RF represents an appropriate tool for biomarker candidate selection
Novel Approach to Evolutionary Neural Network Based Descriptor Selection and QSAR Model Development
No full textCapability of evolutionary neural network (ENN) based QSAR approach to direct the descriptor selection process towards stable descriptor subset (DS) composition characterized by acceptable generalization, as well as the influence of description stability on QSAR model interpretation have been examined. In order to analyze the DS stability and QSAR model generalization properties multiple random dataset partitions into training and test set were made. Acceptability criteria proposed by Golbraikh et al. [J. Comput.-Aided Mol. Des., 17 (2003) 241] have been chosen for selection of highly predictive QSAR models from a set of all models produced by ENN for each dataset splitting. All QSAR models that pass Golbraikh's filter generated by ENN for each dataset partition were collected. Two final DS forming principles were compared. Standard principle is based on selection of descriptors characterized by highest frequencies among all descriptors that appear in the pool [J. Chem. Inf. Comput. Sci., 43 (2003) 949]. Search across the model pool for DS that are stable against multiple dataset subsampling i.e. universal DS solutions is the basis of novel approach. Based on described principles benzodiazepine QSAR has been proposed and evaluated against results reported by others in terms of final DS composition and model predictive performance
Interobserver varijabilnost u citološkoj subklasifikaciji skvamoznih intraepitelnih lezija
No full textThe aim of the study was to compare interobserver variability for The Bethesda System (TBS) and World Health Organization (WHO) classification of cervical squamous intraepithelial lesions. A total of 1,000 conventional Papanicolaou smears (156 positive and 884 negative) were examined »blindly« by three cytologists and one cytotechnician. The degree of observer agreement was expressed by kappa statistics using a program for the calculation of interobserver variation and association »Agree« (Svanholm and Jergensen, 1989). Kappa (x) was determined for each cytologic diagnosis within a particular classification and total for either classification. The association with and separation from other diagnoses was determined for each cytologic diagnosis in the form of conditional probability (Pj). In WHO classification, the diagnoses of dysplasia media and dysplasia gravis showed poor reproducibility (x=0.114 and x= 0.259, respectively), the diagnosis of dysplasia levis good reproducibility (x=0.639), and the diagnosis of carcinoma in situ excellent reproducibility (x=0.762). WHO classification yielded pool x of 0.741. In TBS classification, the diagnosis of LSIL showed good, and HSIL excellent reproducibility (x=0.542 and x=0.763, respectively). TBS classification yielded pool x of 0.699. Dysplasia media (Pj=0.121) and dysplasia gravis (Pj=0.274) were found to be morphologically poorly defined, and carcinoma in situ (Pj=0.777) and dysplasia levis (Pj=0.651) well defined diagnoses. LSIL was morphologically moderately defined (Pj=0.587) and HSIL well defined (Pj=0.789) diagnosis. Accordingly, TBS does not substantially improve diagnostic reproducibility of the cytologic diagnoses of squamous intraepithelial lesions, while providing considerably less information to the clinician than the four-grade dysplasia/CIS terminology, thus eliminating the opportunity of choosing a different procedure for the diagnosis of dysplasia media, which is of utmost importance in the population of young nulliparae.Cilj rada je bio usporediti interobserver varijabilnost za The Bethesda System (TBS) i World Health Organization (WHO) klasifikaciju skvamoznih intraepitelnih lezija cerviksa uterusa. Set od 1000 konvencionalnih Papa razmaza (156 pozitivnih i 884 negativnih) »na slijepo« su pregledala 3 citologa i jedan citotehničar. Stupanj slaganja je izražen kappa statistikom pomoću programa za računanje interobserver varijacija i asocijacija »Agree« (Svanholm i Jergensen, 1989.). Weighted je određen za svaku citološku dijagnozu unutar klasifikacije, kao i za klasifikacije u cijelosti. Za svaku citološku dijagnozu je određena povezanost, odnosno razgraničenost s drugim dijagnozama u obliku uvjetne vjerojatnosti (Pj). Kod WHO klasifikacije su slabo reproducibilne dijagnoze dysplasia media (=0,114) i dysplasia gravis (=0,259), prilično dobro je reproducibilna dijagnoza dysplasia levis (=0,639), a odlično je reproducibilna dijagnoza carcinoma in situ (=0,762). Za klasifikaciju u cijelosti je 0,741. Kod TBS klasifikacije LSIL je prilično dobro reproducibilna dijagnoza (=0,542), dok je HSIL odlično reproducibilna dijagnoza (=0,763). Za klasifikaciju u cijelosti je 0,699. Dysplasia media (P=0,121) i dysplasia gravis (Pj=0,274) su morfološki slabo definirane dijagnoze, carcinoma in situ (Pj=0,777) i dysplasia levis (Pj=0,651) su dobro definirane dijagnoze. LSIL (Pj=0,587) je morfološki srednje definirana dijagnoza, dok je HSIL (Pj=0,789) dobro definirana dijagnoza.TBS ne popravlja bitno dijagnostičku reproducibilnost citoloških dijagnoza za skvamozne intraepitelne lezije, a kliničaru daje znatno manje informacija nego četverodijelna dysplasia / CIS terminologija i time oduzima mogućnost različitog postupka za dijagnozu dysplasia media što je osobito važno za populaciju mladih nulipara i trudnica
Transcranial Doppler Ultrasonography as an Early Outcome Forecaster Following Severe Brain Injury
No full textKnowledge of post-traumatic cerebral haemodynamic disturbances might be beneficial for predicting the management outcome when measuring the basal cerebral arteries blood flow velocity by ultrasonic transcranial Doppler device immediately after severe head injury. Thirty patients who sustained severe brain injury underwent an early blood velocity measuring by transcranial Doppler ultrasonography during a 1-year period of study. The standard technique of measuring the mean blood flow velocity in the middle cerebral artery was applied. The outcome was assessed at 6-month follow-up by the Glasgow Outcome Score. The middle cerebral artery low blood flow velocity, and the increased values of the pulsatility index significantly correlated to an unfavourable outcome. Transcranial Doppler ultrasonography for measuring the middle cerebral artery blood flow velocity has been proved worthy as a possible predictor of severe head injury management outcome. This non-invasive and simple procedure could be engaged in the daily management of severely brain-injured patients
Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks
No full textAbstract: The aim of this work is comparison of the prediction power of multiple linear regression and artificial neural networks retention models for inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) in suppressed ion chromatography with isocratic elution. Relations between ion chromatographic parameters (eluent flow rate and concentration of OH2 in eluent) and retention time of particular anion are described with unique mathematical function obtained by multiple linear regression and with a three-layers feed-forward artificial neural network. The artificial neural network was trained with a Levenberg-Marquardt batch error back propagation algorithm. It is shown that the multiple linear regression retention model has lower, but still very satisfactory, predictive ability. Due to its complexity, the artificial neural network must still be regarded as a more complicated technique. That indicates multiple linear regression as a method of choice for retention modeling in the case of ion chromatographic analysis with isocratic elution
Development of an Ion Chromatographic Method for Monitoring Fertilizer Industry Wastewater Quality
No full textThe aim of this work is to develop an ion chromatographic method for monitoring of fluoride, chloride, nitrite, sulfate, nitrate, and phosphate in fertilizer industry wastewater. A developed method was optimized and better separation within a reasonable analysis time was obtained. Optimization was performed by using retention models obtained with artificial neural networks in combination with several criteria functions for evaluation of separation, resulting with a fast and accurate optimization procedure. By performing a validation procedure and number of statistical tests, it is shown that the developed ion chromatographic method has superior performance characteristics: linearity R2 0.998, recovery ¼ 99.49 – 100.12%, repeatability RSD 1.14%. This result proves that the proposed method can be used for monitoring of fertilizer industry wastewater