GUIDELINES FOR THE DIAGNOSTICS AND TREATMENT OF HIDRADENITIS SUPPURATIVA

Hrvatsko dermatovenerološko društvo i Hrvatsko društvo za plastičnu, rekonstrukcijsku i estetsku kirurgiju ­Hrvatskoga liječničkog zbora ustrojili su radnu skupinu liječnika s kliničkim iskustvom u dijagnostici i liječenju gnojnog hidradenitisa (hidradenitis suppurativa). Skupina je kritički proučila mjerodavnu znanstvenu literaturu te je donijela smjernice za dijagnostiku i liječenje.Croatian dermatovenerologic society and Croatian society for plastic, reconstructive and esthetic surgery of the Croatian Medical Association formed the working group which consists of physicians with experience in diagnostics and treatment of hidradentitis suppurativa. After a critical analysis of relevant scientific papers, the working group has developed practice guidelines for the diagnosis and treatment

Smjernice za dijagnostiku i liječenje gnojnog hidradenitisa (hidradenitis suppurativa) [Guidelines for the diagnostics and treatment of hidradenitis suppurativa]

Croatian dermatovenerologic society and Croatian society for plastic, reconstructive and esthetic surgery of the Croatian Medical Association formed the working group which consists of physicians with experience in diagnostics and treatment of hidradentitis suppurativa. After a critical analysis of relevant scientific papers, the working group has developed practice guidelines for the diagnosis and treatment

Development and application of a soft sensor at a propylene production plant

U ovome radu prikazan je razvoj modela softverskih senzora za kontinuirano praćenje sadržaja propilena u rafinerijskom postrojenju propan/propilen splitter. Dobiveni podaci ulaznih varijabli usklađeni su vremenski sa izlaznom varijablom te su obrađeni različitim metodama predobrade. U programskom jeziku Python izrađeni su modeli softverskih senzora primjenom neuronskih mreža s višeslojnim perceptonom (engl. Multi-layer percepton, MLP) i neuronskih mreža s dugotrajnim kratkoročnim pamćenjem (engl. long short-term memory, LSTM). Pri razvoju MLP modela analizirani su hiperparametri modela kao što su: broj neurona u skrivenom sloju i utjecaj vrste prijenosne funkcije (sigmoid, tanh, ReLU i ELU) na kvalitetu razvijenih neuronskih mreža. Pri razvoju LSTM modela se dodatno ispitivao i broj vremenskih koraka u prošlost te broj LSTM jedinica. Oba modela dala su visoke i slične vrijednosti koeficijenata korelacije te niske pogreške između modeliranog sadržaja propilena i realnih podataka sa postrojenja te se mogu primijeniti u rafinerijskom informacijskom sustavu. Analiza trenda i histograma pogreške razvijenih modela dodatno ukazuje na zadovoljavajuću kvalitetu razvijenih modela. Primjenom softverskih senzora smanjuje se potreba za korištenjem skupe mjerne analitičke opreme i opreme koja se često kvari, te je u konačnici moguće potpuno zamijeniti postojeće analizatore u procesu. Primjenom softverskih senzora na postrojenju unaprijedit će se automatsko vođenje procesa što će utjecati na stabilnije odvijanje procesa i veću kvalitetu konačnog proizvoda.This paper presents the development of soft sensor models for continuous monitoring of propylene content in rafinery propane/propylene splitter. The goal of this paper is to develop soft sensor models based on given experimental data about refinery process of propane and propylene separation, validate and compare developed models. The development of models began with the preprocessing of the collected measurement data. Soft sensor models were created in the Python programming language using the methods of multilayer percepton networks and long short-term memory networks. During development of multilayer percepton networks model it was tested how hyperparameters such as number of neurons in hidden layers and activation function (sigmoid, tanh, ReLU i ELU) affect on quality of the developed neural networks. During the development of LSTM models, additional testing of the number of time steps into the past and the number of LSTM units on model quality were examined. A statistical analysis of the results was carried out, which shows that both types of models give high correlation values of model data with real propylene content data, so both types of models can be applied in the refinery information system. Error trend analysis and error histogram of the developed models additionally indicate the satisfactory quality of the developed models. By applying the developed soft sensors it is possible to decrease the need for high cost measurement equipment and equipment that breaks down frequently, and ultimately completely replace the existing analyzers in the process and improve the automatic management of the process. Application of software sensors will improve automatic process control, which will result in more stable process management at the plant and higher quality of the final product

Development and application of a soft sensor at a propylene production plant

U ovome radu prikazan je razvoj modela softverskih senzora za kontinuirano praćenje sadržaja propilena u rafinerijskom postrojenju propan/propilen splitter. Dobiveni podaci ulaznih varijabli usklađeni su vremenski sa izlaznom varijablom te su obrađeni različitim metodama predobrade. U programskom jeziku Python izrađeni su modeli softverskih senzora primjenom neuronskih mreža s višeslojnim perceptonom (engl. Multi-layer percepton, MLP) i neuronskih mreža s dugotrajnim kratkoročnim pamćenjem (engl. long short-term memory, LSTM). Pri razvoju MLP modela analizirani su hiperparametri modela kao što su: broj neurona u skrivenom sloju i utjecaj vrste prijenosne funkcije (sigmoid, tanh, ReLU i ELU) na kvalitetu razvijenih neuronskih mreža. Pri razvoju LSTM modela se dodatno ispitivao i broj vremenskih koraka u prošlost te broj LSTM jedinica. Oba modela dala su visoke i slične vrijednosti koeficijenata korelacije te niske pogreške između modeliranog sadržaja propilena i realnih podataka sa postrojenja te se mogu primijeniti u rafinerijskom informacijskom sustavu. Analiza trenda i histograma pogreške razvijenih modela dodatno ukazuje na zadovoljavajuću kvalitetu razvijenih modela. Primjenom softverskih senzora smanjuje se potreba za korištenjem skupe mjerne analitičke opreme i opreme koja se često kvari, te je u konačnici moguće potpuno zamijeniti postojeće analizatore u procesu. Primjenom softverskih senzora na postrojenju unaprijedit će se automatsko vođenje procesa što će utjecati na stabilnije odvijanje procesa i veću kvalitetu konačnog proizvoda.This paper presents the development of soft sensor models for continuous monitoring of propylene content in rafinery propane/propylene splitter. The goal of this paper is to develop soft sensor models based on given experimental data about refinery process of propane and propylene separation, validate and compare developed models. The development of models began with the preprocessing of the collected measurement data. Soft sensor models were created in the Python programming language using the methods of multilayer percepton networks and long short-term memory networks. During development of multilayer percepton networks model it was tested how hyperparameters such as number of neurons in hidden layers and activation function (sigmoid, tanh, ReLU i ELU) affect on quality of the developed neural networks. During the development of LSTM models, additional testing of the number of time steps into the past and the number of LSTM units on model quality were examined. A statistical analysis of the results was carried out, which shows that both types of models give high correlation values of model data with real propylene content data, so both types of models can be applied in the refinery information system. Error trend analysis and error histogram of the developed models additionally indicate the satisfactory quality of the developed models. By applying the developed soft sensors it is possible to decrease the need for high cost measurement equipment and equipment that breaks down frequently, and ultimately completely replace the existing analyzers in the process and improve the automatic management of the process. Application of software sensors will improve automatic process control, which will result in more stable process management at the plant and higher quality of the final product

Preparation of filaments for 3D printing of pharmaceutical dosage forms

Cilj ovog rada je priprava filamenata koji će sadržavati dronedaron-hidroklorid kao djelatnu tvar postupkom ekstrudiranja taljenjem pri što nižoj temperaturi zbog toplinske osjetljivosti djelatne tvari. Također, iz dobivenih filamenata potrebno je proizvesti kocke tehnikom 3D-tiska te provesti karakterizaciju. Korištene su mješavine dronedaron-hidroklorida, polikaprolaktona, polietilen glikola i Soluplusa u različitim udjelima iz kojih su priređena tri filamenta: dronedaron-polikaprolakton (F), dronedaron-polikaprolakton-polietilen glikol (FP) i dronedaron-polikaprolakton-Soluplus (FS). Filamenti su ekstrudirani pri 100 ℃, izmjeren im je promjer digitalnom pomičnom mjerkom. Iz dobivenih filamenata, pomoću 3D-printera, pripravljene su kocke koje su podvrgnute testu bubrenja u tri otopine: destilirana voda, fosfatni pufer i klorovodična kiselina. Sadržaj dronedarona u filamentima i kockama određen je UV/Vis spektrofotometrijom. Rezultati pokazuju da filament FP ima promjer najbliži ciljanom od 1,75 mm koji je potreban za 3D-printer te da su odstupanja u promjeru uzduž filamenta najmanja. Najveći udio dronedarona postignut je u filamentu FS. Od istiskanih kocki najpravilnija je kocka FS dok je iz kocke FP oslobođeno više DNR-a. Spektrofotometrijski određen sadržaj dronedarona u filamentima i kockama znatno je manji od stvarnog. Mogući uzrok tome je slaba topljivost polikaprolaktona u acetonu što je imalo za posljedicu nepotpuno otapanje dronedarona. Rezultati testa bubrenja pokazali su neznatno povećanje mase nakon tri dana u svim otopinama. Unatoč nižim temperaturama obrade postignutim upotrebom polikaprolaktona, njegov nedostatak je mali intenzitet bubrenja, a time i vrlo sporo oslobađanje djelatne tvari.The aim of this work is to prepare filaments containing dronedarone hydrochloride as an active pharmaceutical ingredient by hot melt extrusion at the lowest possible temperature due to the thermal sensitivity of the active pharmaceutical ingredient. In addition, the filaments were used for the fabrication of cubes by 3D printing process, which were subsequently characterized. Mixtures of dronedarone hydrochloride, polycaprolactone, polyethylene glycol and Soluplus in different proportions were used in the preparation of the filaments, from which three filaments were prepared: Dronedarone-polycaprolactone (F), Dronedarone-polycaprolactonepolyethylene glycol (FP) and Dronedarone-polycaprolactone-Soluplus (FS). The filaments were extruded at 100 ℃, and their diameter was measured using a digital moving gauge. The obtained filaments were used to make cubes with a 3D printer, which were subjected to a swelling test in three solutions: distilled water, phosphate buffer and hydrochloric acid. The content of dronedarone in the filaments and cubes was determined by UV/Vis spectrophotometry. The results show that the filament FP has the diameter closest to the target value of 1.75 mm required for a 3D printer and that the diameter variations along the filament are the smallest. The highest percentage of dronedarone was achieved with the FS filament. Of the printed cubes, the FS cube is the most visually correct, while more DNR is released from the FP cube. The spectrophotometrically determined content of dronedarone in filaments and cubes is significantly lower than the actual content. One possible reason for this is the poor solubility of polycaprolactone in acetone, which resulted in incomplete dissolution of dronedarone. The results of the swelling test showed a slight increase in mass after three days in all solutions. Despite the lower processing temperatures achieved when using polycaprolactone, the disadvantage is the low swelling intensity and thus the very slow release of the active ingredient

Development and application of a soft sensor at a propylene production plant

U ovome radu prikazan je razvoj modela softverskih senzora za kontinuirano praćenje sadržaja propilena u rafinerijskom postrojenju propan/propilen splitter. Dobiveni podaci ulaznih varijabli usklađeni su vremenski sa izlaznom varijablom te su obrađeni različitim metodama predobrade. U programskom jeziku Python izrađeni su modeli softverskih senzora primjenom neuronskih mreža s višeslojnim perceptonom (engl. Multi-layer percepton, MLP) i neuronskih mreža s dugotrajnim kratkoročnim pamćenjem (engl. long short-term memory, LSTM). Pri razvoju MLP modela analizirani su hiperparametri modela kao što su: broj neurona u skrivenom sloju i utjecaj vrste prijenosne funkcije (sigmoid, tanh, ReLU i ELU) na kvalitetu razvijenih neuronskih mreža. Pri razvoju LSTM modela se dodatno ispitivao i broj vremenskih koraka u prošlost te broj LSTM jedinica. Oba modela dala su visoke i slične vrijednosti koeficijenata korelacije te niske pogreške između modeliranog sadržaja propilena i realnih podataka sa postrojenja te se mogu primijeniti u rafinerijskom informacijskom sustavu. Analiza trenda i histograma pogreške razvijenih modela dodatno ukazuje na zadovoljavajuću kvalitetu razvijenih modela. Primjenom softverskih senzora smanjuje se potreba za korištenjem skupe mjerne analitičke opreme i opreme koja se često kvari, te je u konačnici moguće potpuno zamijeniti postojeće analizatore u procesu. Primjenom softverskih senzora na postrojenju unaprijedit će se automatsko vođenje procesa što će utjecati na stabilnije odvijanje procesa i veću kvalitetu konačnog proizvoda.This paper presents the development of soft sensor models for continuous monitoring of propylene content in rafinery propane/propylene splitter. The goal of this paper is to develop soft sensor models based on given experimental data about refinery process of propane and propylene separation, validate and compare developed models. The development of models began with the preprocessing of the collected measurement data. Soft sensor models were created in the Python programming language using the methods of multilayer percepton networks and long short-term memory networks. During development of multilayer percepton networks model it was tested how hyperparameters such as number of neurons in hidden layers and activation function (sigmoid, tanh, ReLU i ELU) affect on quality of the developed neural networks. During the development of LSTM models, additional testing of the number of time steps into the past and the number of LSTM units on model quality were examined. A statistical analysis of the results was carried out, which shows that both types of models give high correlation values of model data with real propylene content data, so both types of models can be applied in the refinery information system. Error trend analysis and error histogram of the developed models additionally indicate the satisfactory quality of the developed models. By applying the developed soft sensors it is possible to decrease the need for high cost measurement equipment and equipment that breaks down frequently, and ultimately completely replace the existing analyzers in the process and improve the automatic management of the process. Application of software sensors will improve automatic process control, which will result in more stable process management at the plant and higher quality of the final product

Preparation of filaments for 3D printing of pharmaceutical dosage forms

Cilj ovog rada je priprava filamenata koji će sadržavati dronedaron-hidroklorid kao djelatnu tvar postupkom ekstrudiranja taljenjem pri što nižoj temperaturi zbog toplinske osjetljivosti djelatne tvari. Također, iz dobivenih filamenata potrebno je proizvesti kocke tehnikom 3D-tiska te provesti karakterizaciju. Korištene su mješavine dronedaron-hidroklorida, polikaprolaktona, polietilen glikola i Soluplusa u različitim udjelima iz kojih su priređena tri filamenta: dronedaron-polikaprolakton (F), dronedaron-polikaprolakton-polietilen glikol (FP) i dronedaron-polikaprolakton-Soluplus (FS). Filamenti su ekstrudirani pri 100 ℃, izmjeren im je promjer digitalnom pomičnom mjerkom. Iz dobivenih filamenata, pomoću 3D-printera, pripravljene su kocke koje su podvrgnute testu bubrenja u tri otopine: destilirana voda, fosfatni pufer i klorovodična kiselina. Sadržaj dronedarona u filamentima i kockama određen je UV/Vis spektrofotometrijom. Rezultati pokazuju da filament FP ima promjer najbliži ciljanom od 1,75 mm koji je potreban za 3D-printer te da su odstupanja u promjeru uzduž filamenta najmanja. Najveći udio dronedarona postignut je u filamentu FS. Od istiskanih kocki najpravilnija je kocka FS dok je iz kocke FP oslobođeno više DNR-a. Spektrofotometrijski određen sadržaj dronedarona u filamentima i kockama znatno je manji od stvarnog. Mogući uzrok tome je slaba topljivost polikaprolaktona u acetonu što je imalo za posljedicu nepotpuno otapanje dronedarona. Rezultati testa bubrenja pokazali su neznatno povećanje mase nakon tri dana u svim otopinama. Unatoč nižim temperaturama obrade postignutim upotrebom polikaprolaktona, njegov nedostatak je mali intenzitet bubrenja, a time i vrlo sporo oslobađanje djelatne tvari.The aim of this work is to prepare filaments containing dronedarone hydrochloride as an active pharmaceutical ingredient by hot melt extrusion at the lowest possible temperature due to the thermal sensitivity of the active pharmaceutical ingredient. In addition, the filaments were used for the fabrication of cubes by 3D printing process, which were subsequently characterized. Mixtures of dronedarone hydrochloride, polycaprolactone, polyethylene glycol and Soluplus in different proportions were used in the preparation of the filaments, from which three filaments were prepared: Dronedarone-polycaprolactone (F), Dronedarone-polycaprolactonepolyethylene glycol (FP) and Dronedarone-polycaprolactone-Soluplus (FS). The filaments were extruded at 100 ℃, and their diameter was measured using a digital moving gauge. The obtained filaments were used to make cubes with a 3D printer, which were subjected to a swelling test in three solutions: distilled water, phosphate buffer and hydrochloric acid. The content of dronedarone in the filaments and cubes was determined by UV/Vis spectrophotometry. The results show that the filament FP has the diameter closest to the target value of 1.75 mm required for a 3D printer and that the diameter variations along the filament are the smallest. The highest percentage of dronedarone was achieved with the FS filament. Of the printed cubes, the FS cube is the most visually correct, while more DNR is released from the FP cube. The spectrophotometrically determined content of dronedarone in filaments and cubes is significantly lower than the actual content. One possible reason for this is the poor solubility of polycaprolactone in acetone, which resulted in incomplete dissolution of dronedarone. The results of the swelling test showed a slight increase in mass after three days in all solutions. Despite the lower processing temperatures achieved when using polycaprolactone, the disadvantage is the low swelling intensity and thus the very slow release of the active ingredient

Preparation of filaments for 3D printing of pharmaceutical dosage forms

Cilj ovog rada je priprava filamenata koji će sadržavati dronedaron-hidroklorid kao djelatnu tvar postupkom ekstrudiranja taljenjem pri što nižoj temperaturi zbog toplinske osjetljivosti djelatne tvari. Također, iz dobivenih filamenata potrebno je proizvesti kocke tehnikom 3D-tiska te provesti karakterizaciju. Korištene su mješavine dronedaron-hidroklorida, polikaprolaktona, polietilen glikola i Soluplusa u različitim udjelima iz kojih su priređena tri filamenta: dronedaron-polikaprolakton (F), dronedaron-polikaprolakton-polietilen glikol (FP) i dronedaron-polikaprolakton-Soluplus (FS). Filamenti su ekstrudirani pri 100 ℃, izmjeren im je promjer digitalnom pomičnom mjerkom. Iz dobivenih filamenata, pomoću 3D-printera, pripravljene su kocke koje su podvrgnute testu bubrenja u tri otopine: destilirana voda, fosfatni pufer i klorovodična kiselina. Sadržaj dronedarona u filamentima i kockama određen je UV/Vis spektrofotometrijom. Rezultati pokazuju da filament FP ima promjer najbliži ciljanom od 1,75 mm koji je potreban za 3D-printer te da su odstupanja u promjeru uzduž filamenta najmanja. Najveći udio dronedarona postignut je u filamentu FS. Od istiskanih kocki najpravilnija je kocka FS dok je iz kocke FP oslobođeno više DNR-a. Spektrofotometrijski određen sadržaj dronedarona u filamentima i kockama znatno je manji od stvarnog. Mogući uzrok tome je slaba topljivost polikaprolaktona u acetonu što je imalo za posljedicu nepotpuno otapanje dronedarona. Rezultati testa bubrenja pokazali su neznatno povećanje mase nakon tri dana u svim otopinama. Unatoč nižim temperaturama obrade postignutim upotrebom polikaprolaktona, njegov nedostatak je mali intenzitet bubrenja, a time i vrlo sporo oslobađanje djelatne tvari.The aim of this work is to prepare filaments containing dronedarone hydrochloride as an active pharmaceutical ingredient by hot melt extrusion at the lowest possible temperature due to the thermal sensitivity of the active pharmaceutical ingredient. In addition, the filaments were used for the fabrication of cubes by 3D printing process, which were subsequently characterized. Mixtures of dronedarone hydrochloride, polycaprolactone, polyethylene glycol and Soluplus in different proportions were used in the preparation of the filaments, from which three filaments were prepared: Dronedarone-polycaprolactone (F), Dronedarone-polycaprolactonepolyethylene glycol (FP) and Dronedarone-polycaprolactone-Soluplus (FS). The filaments were extruded at 100 ℃, and their diameter was measured using a digital moving gauge. The obtained filaments were used to make cubes with a 3D printer, which were subjected to a swelling test in three solutions: distilled water, phosphate buffer and hydrochloric acid. The content of dronedarone in the filaments and cubes was determined by UV/Vis spectrophotometry. The results show that the filament FP has the diameter closest to the target value of 1.75 mm required for a 3D printer and that the diameter variations along the filament are the smallest. The highest percentage of dronedarone was achieved with the FS filament. Of the printed cubes, the FS cube is the most visually correct, while more DNR is released from the FP cube. The spectrophotometrically determined content of dronedarone in filaments and cubes is significantly lower than the actual content. One possible reason for this is the poor solubility of polycaprolactone in acetone, which resulted in incomplete dissolution of dronedarone. The results of the swelling test showed a slight increase in mass after three days in all solutions. Despite the lower processing temperatures achieved when using polycaprolactone, the disadvantage is the low swelling intensity and thus the very slow release of the active ingredient

Guidelines for the Diagnostics and Treatment of Hidradenitis Suppurativa

Hrvatsko dermatovenerološko društvo i Hrvatsko društvo za plastičnu, rekonstrukcijsku i estetsku kirurgiju Hrvatskoga liječničkog zbora ustrojili su radnu skupinu liječnika s kliničkim iskustvom u dijagnostici i liječenju gnojnog hidradenitisa (hidradenitis suppurativa). Skupina je kritički proučila mjerodavnu znanstvenu literaturu te je donijela smjernice za dijagnostiku i liječenje.Croatian dermatovenerologic society and Croatian society for plastic, reconstructive and esthetic surgery of the Croatian Medical Association formed the working group which consists of physicians with experience in diagnostics and treatment of hidradentitis suppurativa. After a critical analysis of relevant scientifi c papers, the working group has developed practice guidelines for the diagnosis and treatment