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The photoinitiated thiol−ene addition reaction is a highly stereo‐ and regioselective, and environmentally friendly reaction proceeding under mild conditions, hence it is ideally suited for the synthesis of carbohydrate mimetics. A comprehensive study on UV‐light‐induced reactions of 2,3‐unsaturated O‐, C‐, S‐ and N‐glycosides with various thiols was performed. The effect of experimental parameters and structural variations of the alkenes and thiols on the efficacy and regio‐ and stereoselectivity of the reactions was systematically studied and optimized. The type of anomeric heteroatom was found to profoundly affect the reactivity of 2,3‐unsaturated sugars in the thiol−ene couplings. Hydrothiolation of 2,3‐dideoxy O‐glycosyl enosides efficiently produced the axially C2‐S‐substituted addition products with high to complete regioselectivity. Moderate efficacy and varying regio‐ and stereoselectivity were observed with 2,3‐unsaturated N‐glycosides and no addition occurred onto the endocyclic double bond of C‐glycosides. Upon hydrothiolation of 2,3‐unsaturated S‐glycosides, the addition of thiyl radicals was followed by elimination of the thiyl aglycone resulting in 3‐S‐substituted glycals., A comprehensive study on radical‐mediated hydrothiolation of 2,3‐unsaturated O‐, C‐, S‐ and N‐glycosides with a broad range of thiols was performed to gain a deeper insight into how experimental parameters and structural variations of the alkenes and thiols influence the efficacy and regio‐ and stereoselectivity of the reactions.

This work presents a H2S selective resistive gas sensor design based on a chemical field effect transistor (ChemFET) with open gate formed by hundreds of high temperature chemical vapour deposition (CVD) grown zinc oxide nanowires (ZnO NW). The sensing ability of pristine ZnO NWs and surface functionalized ZnO NWs for H2S is analysed systematically. ZnO NWs are functionalized by deposition of discontinuous gold (Au) nanoparticle films of different thicknesses of catalyst layer ranging from 1 to 10 nm and are compared in their gas sensing properties. All experiments were performed in a temperature stabilized small volume compartment with adjustable gas mixture at room temperature. The results allow for a well-founded understanding of signal-to-noise ratio, enhanced response, and improved limit of detection due to the Au functionalisation. Comprehension and controlled application of the beneficial effects of Au catalyst on ZnO NWs allow for the detection of very low H2S concentrations down to 10 ppb, and a theoretically estimated 500 ppt in synthetic air at room temperature., publishedVersion

Unsere Atemluft ist t��glichen Schwankungen ausgesetzt und die Marktnachfrage nach Sensoren, die die Luftqualit��t messen k��nnen, steigt rapide an. Ein gro��er Teil dieser Nachfrage kann mit Metall-Oxid Gas Sensoren bedient werden. Diese Art von Gassensoren hat jedoch einige Nachteile im Bezug auf Genauigkeit, Langzeitstabilit��t, Leistungsaufnahme und Selektivit��t. Auch fehlen gro��volumige Anwendungsbeispiele auf dem Markt, die Metall-Oxid (MOX) Gassensoren einsetzen und dabei alle Systemanforderungen erf��llen. Diese Arbeit stellt die neueste Entwicklung der "KArlsruhe MIkro NAse", einer im Rahmen der EU Horizon 2020 Initiative namens SMOKESENSE entwickelten elektrischen Nase, vor und vergleicht diese mit dem aktuellen Stand der Technik f��r Metalloxid-Gassensoren. Es wird gezeigt, dass durch UV-Anregung der $SnO_{2}$-Nanodr��hte ein geringerer Stromverbrauch sowie eine minimierte Siloxan-Kontaminierung im Vergleich zu klassischen MOX-Sensoren erzielt wird. Zudem l��sst sich mittels Aerosol-Jet-Druck eine vereinfachte und kosteng��nstigere Herstellung der Sensoren realisieren. Um die Massenproduktionstauglichkeit f��r eine Anwendung als intelligenter Feuersensor sicherzustellen, wird der Wachstumsprozess der Nanodr��hte optimiert. Au��erdem wird ein neuartiges chemisches FET-��hnliches Sensorkonzept namens Chem-FET vorgestellt, das im Vergleich zu UV-KAMINA ein verbessertes Signal-Rausch-Verh��ltnis und eine schnellere Reaktionszeit bietet. Eine ��berwachte Lernmethode des Maschinellen Lernens basierend auf einer linearen Diskriminanzfunktion wird verwendet, um verschiedene Zielger��che zu klassifizieren. In einer Anwendung als Feuersensor erwiesen sich die entwickelten Sensorprototypen als konkurrenzf��hig. Zus��tzlich werden M��glichkeiten aufgezeigt, das Sensorprinzip als Plattform f��r andere Anwendungsarten verwenden zu k��nnen. W��hrend mit den vorgestellten Methoden die Leistung des Gesamtsystems optimiert werden konne, bleibt als Ausblick Verbesserungsbedarf in Bereichen, wie z. B. der Charakterisierung von Ger��chen und der Testmethodik f��r die Anwendung in hohen St��ckzahlen.

Breath analysis using eNose technology can be used to discriminate between asthma and COPD patients, but it remains unclear whether results are influenced by smoking status. We aim to study whether eNose can discriminate between ever- vs. never-smokers and smoking &lt, 24 vs. &gt, 24 h before the exhaled breath, and if smoking can be considered a confounder that influences eNose results. We performed a cross-sectional analysis in adults with asthma or chronic obstructive pulmonary disease (COPD), and healthy controls. Ever-smokers were defined as patients with current or past smoking habits. eNose measurements were performed by using the SpiroNose. The principal component (PC) described the eNose signals, and linear discriminant analysis determined if PCs classified ever-smokers vs. never-smokers and smoking &lt, 24 h. The area under the receiver–operator characteristic curve (AUC) assessed the accuracy of the models. We selected 593 ever-smokers (167 smoked &lt, 24 h before measurement) and 303 never-smokers and measured the exhaled breath profiles of discriminated ever- and never-smokers (AUC: 0.74, 95% CI: 0.66–0.81), and no cigarette consumption &lt, 24h (AUC 0.54, 95% CI: 0.43–0.65). In healthy controls, the eNose did not discriminate between ever or never-smokers (AUC 0.54, 95% CI: 0.49–0.60) and recent cigarette consumption (AUC 0.60, 95% CI: 0.50–0.69). The eNose could distinguish between ever and never-smokers in asthma and COPD patients, but not recent smokers. Recent smoking is not a confounding factor of eNose breath profiles.

Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class., publishedVersion

In this work, we investigate the catalytic effects of gold (Au) and platinum (Pt) nanoparticle layer deposition on highly sensitive zinc oxide (ZnO) nanowires (NWs) used for selective H2S detection in the sub-ppm region. Optimum quality pristine ZnO NWs were grown by high temperature chemical vapor deposition (CVD) in the vapor liquid solid growth (VLS) mode on silicon with a thin Au layer acting as a growth catalyst. The surface of pristine ZnO NWs was modified by systematic magnetron sputtering of discontinuous Au and Pt layers of 0���5 nm thickness. Resistive gas sensors based on the gas sensing mechanism of a chemical field effect transistor (ChemFET) with open gate, which is formed by hundreds of parallel aligned pristine Aumodified or Pt-modified ZnO NWs, were measured toward H2S diluted in dry nitrogen (N2) or in dry synthetic air at room temperature. Gas sensing results showed a largely improved response due to the catalytic effects of metal deposition on the ZnO NW surface. Controlled application of ZnO NW growth under optimized conditions and metal catalyst deposition showed a clear response enhancement toward 1 ppm H2S from the initial 20% achieved with pristine ZnO to over 5000% with ZnO NWs covered by 5 nm of Au, and, hence, significantly lower than the limit of detection., publishedVersion

Aim: Rapid identification of bacteria would facilitate timely initiation of therapy and improve cost–effectiveness of treatment. Traditional methods (culture, PCR) require reagents, consumables and hours to days to complete the identification. In this study, we examined whether differential mobility spectrometry could classify most common bacterial species, genera and between Gram status within minutes. Materials & methods: Cultured bacterial sample gaseous headspaces were measured with differential mobility spectrometry and data analyzed using k-nearest-neighbor and leave-one-out cross-validation. Results: Differential mobility spectrometry achieved a correct classification rate 70.7% for all bacterial species. For bacterial genera, the rate was 77.6% and between Gram status, 89.1%. Conclusion: Largest difficulties arose in distinguishing bacteria of the same genus. Future improvement of the sensor characteristics may improve the classification accuracy.

Thioglycosides and C‐glycosides represent pharmacologically useful classes of glycomimetics that possess a high degree of biological stability. One emerging tool for the stereoselective synthesis of thioglycosides is the photoinitiated addition of thiols to unsaturated sugars. Moreover, thiyl radical‐mediated reactions of exo‐glycals and 1‐substituted endo‐glycals offer facile routes to β‐C‐glycosidic structures. This Concept article summarizes the thiol‐ene coupling strategies developed recently by our group and Somsák's group for the synthesis of several kinds of glycomimetics which are difficult to synthesize by conventional methods. One unusual characteristic of the thiol‐ene reactions of endo‐glycals is that heating inhibits, whereas cooling promotes the reaction. This unique temperature dependence as well as the effects of the enose structures and thiol configurations on the efficacy and stereoselectivity of the reactions are also discussed., UV‐light induced radical‐mediated addition of thiols onto endo‐ or exo‐cyclic double bonds of unsaturated sugars offers an easy and stereoselective route to a broad range of stable glycomimetics including 1,2‐cis‐α‐S‐glycosides and new types of β‐C‐glycosides.

Die idiopathische pulmonale Fibrose (IPF) ist eine schwere chronisch progrediente Lungenerkrankung mit einer mittleren Überlebenszeit von 3 bis 5 Jahren ab dem Zeitpunkt der Erstdiagnose. Das Bronchialkarzinom gehört zu den häufigsten malignomassoziierten Todesursachen weltweit sowohl bei Frauen als auch bei Männern. Beide Erkrankungen haben gemeinsam, dass eine frühestmögliche Diagnosestellung die Prognose deutlich positiv beeinflussen kann, jedoch häufig die Diagnosestellung erst spät erfolgt, da es bisher keine zuverlässigen Screening-Möglichkeiten gibt.In dieser Arbeit kam die Technologie der elektronischen Nase zur Anwendung, ein sich in den letzten Jahrzehnten rasch entwickelndes Feld der nicht-invasiven Diagnostik. Ziel der Arbeit war es im Rahmen einer Studie mit dem Gerät Aeonose® 174 Patienten mit interstitieller Lungenerkrankung und 120 Patienten mit einem Bronchialkarzinom sowie 33 Gesunden und 23 COPD-Patienten zu untersuchen und herauszuarbeiten, ob die Aeonose® in der Lage ist, die erkrankten Patienten mit hoher Spezifität und Sensitivität von gesunden Kontrollpersonen zu unterscheiden. Zudem wurde gefragt, ob die Aeonose® darüber hinaus die Erkrankungen in Querschnittsanalysen voneinander unterscheiden kann.Der in dieser Studie entwickelte Algorithmus zur Differenzierung von Bronchialkarzinom-Patienten bzw. Patienten mit interstitiellen Lungenerkrankungen von der Kontrollgruppe erbrachte Ergebnisse mit guter Sensitivität und Spezifität bei einem Fall-Kontroll-Ansatz. Jedoch scheiterte die Aeonose® größtenteils bei der sicheren Identifizierung der COPD-Kohorte und der Bronchialkarzinom-Kohorten in partieller oder kompletter Remission als Karzinom-frei im Blindversuch. Unsere Daten legen nahe, dass basierend auf der Vorgehensweise immer nur zwei Konditionen in der Trainingsphase miteinander zu vergleichen relevante VOC-Signale, die von Komorbiditäten wie der COPD herrührten, fälschlicherweise zur Unterscheidung von gesunden und kranken Personen geführt haben. Trotz dieser etwas unzufriedenstellenden Ergebnisse ist weiterhin davon auszugehen, dass VOC-Signaturen, wenn sie adäquat gebündelt und dem vorliegenden pulmonalen Phänotyp zugeordnet wären, für die rasche und frühe Erkennung von Bronchialkarzinomen und interstitiellen Lungenerkrankungen genutzt werden und Screeningprogramme für Bronchialkarzinome, die auf low dose CT basieren, vervollständigen könnten. Studien mit einem weitaus größeren Datensatz an Patienten, welche auf Analysen durch künstliche Intelligenzen oder Hauptkomponentenanalysen (principle compound analysis) basieren, könnten weitaus besser in der Lage sein diese Geräte zu trainieren und schlussendlich die sichere Differenzierung innerhalb der interstitiellen Lungenerkrankungen sowie von Malignomen und Nicht-Malignomen ermöglichen., The idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with a poor mean survival rate of about 3 - 5 years after definite diagnosis. Lung cancer accounts for more deaths than any other cancer in both men and women. Both diseases, IPF and lung cancer, have in common that an early diagnosis can have massive positive impact on the prognosis. Nevertheless, the definite diagnoses in most cases are delayed to a late stage of the disease, as there are no uncomplicated valid screening programs yet.This study investigates the electronic nose technology, which has been of great scientific interest over the last few years and is a rapidly emerging field of medical diagnostics. In this study, we used a device called Aeonose® by the dutch company The eNose company.174 patients with ILD and 91 lung cancer patients were measured by Aeonose®. Aim of this study was to investigate whether the Aeonose® is able to distinguish the diseased patients from healthy controls with a high sensitivity and specifcity and furthermore to study if the Aeonose® could distinguish the diseases in cross-sectional analyses.The algorithm developed in this study to separate LC from controls using the Aeonose® resulted in good sensitivity and specificity in separating these two conditions in a case-control approach, but largely failed to safely identify cohorts of COPD and LC in partial or complete remission as being cancer-free in a blinded approach. The Aeonose® also showed potential in distinguishing ILD from HC in direct comparison. Our data suggest that - based on the herein applied approach of dichotomous comparison of conditions for training - relevant VOC signals originating from comorbidities such as COPD may have erroneously led to separation between LC or ILD and healthy control. Notwithstanding these somewhat disappointing results, we nevertheless believe that VOC signatures, once being adequately clustered and annotated to the underlying pulmonary phenotype, may be used for rapid and safe detection of lung cancer and ILDs and to accomplish lung cancer screening programs based on low dose CT. We suggest that artificial intelligence or principle compound analysis - based studies of a much broader data set of patients with interstitial lung diseases may be much better suitable to train these devices and ultimately to allow safe differentiation within ILDs as well as cancer and non cancer.

Diseases of prematurity such as neonatal infections and bronchopulmonary dysplasia (BPD) are frequent in preterm neonates, resulting from incomplete organ development. Early and late onset neonatal infections are a primary reason for mortality in preterms during the neonatal period. BPD is caused by an inflammatory process that prevents further development of the premature lung, resulting in a chronic pulmonary disease with prolonged need for supplementary oxygen. Early prediction markers for BPD are still lacking. Likewise, valid diagnosis of invasive infection in preterm and term neonates is difficult due to unspecific clinical symptoms. In this project BPD and neonatal infections were investigated by analysing tracheal aspirates of 33 preterm and term neonates, which were collected during routine suctioning of the intubated patients. IL-6 and IFN-γ cytokine concentrations in the tracheal aspirates were measured by immunoassay. Conventional microbiological analysis consisting of gram staining, microscopy and bacterial culture was performed, as well as molecular analysis, which included PCR of 16S-rDNA and separation of the PCR- products by chromatography. Additionally, an electronic nose, a device that senses volatile organic compounds (VOCs) through 32 biopolymer sensors, was utilized. Smellprints were generated according to the detected VOCs. Preterms born before completion of 32 weeks that developed BPD later on did not show higher levels of IL-6 and IFN-γ in their tracheal aspirates when compared to preterms that did not develop BPD. Tracheal aspirate cytokines were also not elevated in patients with signs of systemic infection or detection of bacteria in the tracheal aspirate. Comparison of the different microbiological detection methods showed a high rate of mismatches. In patients that developed BPD, germs were detected significantly more often in the tracheal aspirate when compared to patients that did not develope BPD. The smellprint linear discriminant analysis showed promising results: smellprints from tracheal aspirates of patients with and without subsequent development of BPD differed significantly. Furthermore, smellprints from tracheal aspirates of neonates with laboratory-confirmed bloodstream infection differed significantly from those without infection, regardless of a positive or negative microbiological test result. The application of an electronic nose may prove useful in the prediction of BPD development and diagnosis of neonatal infections. Larger numbers of samples will be needed for further validation of the respective smellprint patterns., Eine Frühgeburt birgt aufgrund der Unreife der Organsysteme des Neugeborenen erhöhte Krankheitsrisiken. Dazu zählen unter anderem Infektionserkrankungen und die Bronchopulmonale Dysplasie (BPD). Early- oder Late-Onset Infektionen durch diverse Erreger sind bei Frühgeborenen eine der Hauptursachen für die Mortalität in der Neonatalperiode. Bei der BPD führen inflammatorische Prozesse in der unreifen Lunge zu einer chronischen Lungenschädigung mit persistierendem Sauerstoffbedarf im Verlauf. Hierfür fehlen valide Frühvorhersagemodelle. Auch was Infektionserkrankungen betrifft, ergeben sich bei Früh- und Neugeborenen wegen der unspezifischen Symptomatik diagnostische Schwierigkeiten. Im Hinblick auf die Prädiktion der BPD und die Diagnostik von systemischen Infektionen erfolgten in der vorliegenden Arbeit Untersuchungen am Trachealaspirat von 33 intubierten Früh- und Reifgeborenen. Das Trachealaspirat wurde beim routinemäßigen Absaugen der Patienten gewonnen. Die Konzentrationen der Zytokine IL-6 und IFN-γ im Trachealaspirat wurden mittels Immunassay bestimmt. Zudem wurden die Trachealaspiratproben mit zwei verschiedenen mikrobiologischen Untersuchungsverfahren auf das Vorhandensein von Bakterien untersucht: Der konventionelle Untersuchungsansatz umfasste Färbung und Mikroskopie sowie kulturelle Anzucht, der molekulare Ansatz bestand aus einer PCR der im Trachealaspirat vorhandenen 16S-rDNA sowie chromatographischer Auftrennung und Sequenzierung der Amplifikate. Des Weiteren kam eine Elektronische Nase zum Einsatz, ein tragbares Gerät, das volatile organische Substanzen (VOCs) detektiert. So wurde ein Smellprint, also ein Profil der emittierten Substanzen, für jede Trachealaspiratprobe – ähnlich einem individuellen Fingerabdruck – erstellt. Die Untersuchung zeigte keine signifikanten Unterschiede der Konzentrationen der Zytokine IL-6 und IFN-γ im Trachealaspirat von Frühgeborenen mit einem Gestationsalter < 32 SSW, die später eine BPD entwickelten und denen, die keine BPD entwickelten. Die Zytokinkonzentrationen im Trachealsekret korrelierten ebenfalls nicht mit dem Vorliegen einer systemischen Infektion oder dem Bakteriennachweis im Trachealaspirat. Der Vergleich des konventionellen und genomischen Erregernachweises im Trachealaspirat zeigte eine erhebliche Diskrepanz. Bei Patienten, die später eine BPD entwickelten, zeigten sich signifikant häufiger Bakterien im Trachealaspirat. Die erstmalig bei Neugeborenen durchgeführte Analyse der durch die Elektronische Nase erstellten Smellprints erbrachte erfolgversprechende Ergebnisse: Die Smellprints der Trachealaspirate von Frühgeborenen, die später eine BPD entwickelten und Frühgeborenen, die keine BPD entwickelten, unterschieden sich in der Diskriminanzanalyse signifikant. Ebenso unterschieden sich die Smellprints von Patienten mit einer laborchemisch bestätigten Infektion signifikant von Patienten ohne Infektionsparameter, unabhängig davon, ob Letztere einen positiven Keimnachweis im Trachealaspirat hatten oder nicht. Der Einsatz der Elektronischen Nase in der Neugeborenenmedizin ist also vielversprechend hinsichtlich der Prädiktion der BPD sowie der Diagnostik von systemischen Infektionen. Es sind jedoch weitere Studien zur Validierung dieser Methode notwendig.

In the present work, a novel, portable and innovative eNose composed of a surface acoustic wave (SAW) sensor array based on zeolitic imidazolate frameworks, ZIF-8 and ZIF-67 nanocrystals (pure and combined with gold nanoparticles), as sensitive layers has been tested as a non-invasive system to detect different disease markers, such as acetone, ethanol and ammonia, related to the diagnosis and control of diabetes mellitus through exhaled breath. The sensors have been prepared by spin coating, achieving continuous sensitive layers at the surface of the SAW device. Low concentrations (5 ppm, 10 ppm and 25 ppm) of the marker analytes were measured, obtaining high sensitivities, good reproducibility, short time response and fast signal recovery., This work has been supported by Universidad Nacional Autónoma de México via Grants DGAPA-UNAM-PAPIIT TA100118 and DGAPA-UNAM-PAPIIT IT100518, the Fundación General CSIC via Programa ComFuturo, and the Spanish Ministry of Science and Innovation via Grant TEC2016-79898-C6-(AEI/FEDER,EU). This research has used the Spanish ICTS Network MICRONANOFABS (partially funded by MINECO), We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)

Fecal volatile organic compound (VOC) analysis has shown great potential as a noninvasive diagnostic biomarker for a variety of diseases. Before clinical implementation, the factors influencing the outcome of VOC analysis need to be assessed. Recent studies found that the sampling conditions can influence the outcome of VOC analysis. However, the dietary influences remains unknown, especially in (preterm) infants. Therefore, we assessed the effects of feeding composition on fecal VOC patterns of preterm infants (born at &lt, 30 weeks gestation). Two subgroups were defined: (1) daily intake &gt, 75% breastmilk (BM) feeding and (2) daily intake &gt, 75% formula milk (FM) feeding. Fecal samples, which were collected at 7, 14 and 21 days postnatally, were analyzed by an electronic nose device (Cyranose 320&reg, ). In total, 30 preterm infants were included (15 FM, 15 BM). No differences in the fecal VOC patterns were observed at the three predefined time-points. Combining the fecal VOC profiles of these time-points resulted in a statistically significant difference between the two subgroups although this discriminative accuracy was only modest (AUC [95% CI], p-value, sensitivity, and specificity of 0.64 [0.51&ndash, 0.77], 0.04, 68%, and 51%, respectively). Our results suggest that the influence of enteral feeding on the outcome of fecal VOC analysis cannot be ignored in this population. Furthermore, in both subgroups, the fecal VOC patterns showed a stable longitudinal course within the first month of life.

In recent years pollution due to hydrocarbon spills has increased markedly as a result of the numerous advances in technologies and industrial processes. Anthropogenic activities (accidental or illegal) are responsible for most of these incidents. In some cases, the spills are not detected at the moment they occur and the contaminants are subjected to different degradation phenomena that may change the chemical composition of the hydrocarbon over time. An incorrect or ineffective identification of the spill could lead to significant consequences, bearing in mind that most spills are hazardous to the environment. In the present work the capacity of the analytical technique based on the Electronic Nose (eNose) combined with chemometrics in the identification and discrimination of different weathered petroleum-derived products (PDPs) was studied. Different volumes (40 &mu, L and 80 &mu, L) of PDPs (gasoline, diesel, and paraffin) were poured onto different supports (wood, cork, paper, and cotton sheet) and subjected to a natural weathering process by evaporation for one month. The porosity of the support was also studied. The application of linear discriminant analysis allowed the full discrimination of the samples according to the presence/absence of PDP and a 97.7% of correct discrimination of the different PDPs regardless of the weathering time, support or volume used. The results show that the system is capable of detecting and discriminating the presence of petroleum-derived products in any of the situations studied.

Acute rhinosinusitis (ARS) is a sudden, symptomatic inflammation of the nasal and paranasal mucosa. It is usually caused by respiratory virus infection, but bacteria complicate for a small number of ARS patients. The differential diagnostics between viral and bacterial pathogens is difficult and currently no rapid methodology exists, so antibiotics are overprescribed. The electronic nose (eNose) has shown the ability to detect diseases from gas mixtures. Differential mobility spectrometry (DMS) is a next-generation device that can separate ions based on their different mobility in high and low electric fields. Five common rhinosinusitis bacteria (Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis, Staphylococcus aureus, and Pseudomonas aeruginosa) were analysed in vitro with DMS. Classification was done using linear discriminant analysis (LDA) and k-nearest neighbour (KNN). The results were validated using leave-one-out cross-validation and separate train and test sets. With the latter, 77% of the bacteria were classified correctly with LDA. The comparative figure with KNN was 79%. In one train-test set, P. aeruginosa was excluded and the four most common ARS bacteria were analysed with LDA and KNN; the correct classification rate was 83 and 85%, respectively. DMS has shown its potential in detecting rhinosinusitis bacteria in vitro. The applicability of DMS needs to be studied with rhinosinusitis patients.

In this work, a new generation of eNose systems particularly suited for exhaled breath gas analysis is presented. The developed analyzer system comprises a compact modular, low volume, temperature controlled sensing chamber explicitly tested for the detection of acetone, isoprene, pentane and isopropanol. The eNose system sensing chamber consists of three compartments, each of which can contain 8 analog Metal Oxide (MOX) sensors or 10 digital MOX sensors. Additional sensors within the digital compartment allow for pressure, humidity and temperature measurements. The presented eNose system contains a sensor array with up to 30 physical sensors and provides the ability to discriminate between low VOC concentrations under dry and humid conditions. The MOX sensor signals were analyzed by pattern recognition methods.

In a fire investigation the rapid detection of the presence of ignitable liquids like gasoline is of great importance as it allows appropriate treatment of the remains, the identification of prevention methods and detects the possible presence of an arsonist. In some cases, analysts cannot access the fire scene in the first few hours due to the dangers involved in the situation and, as a consequence, phenomena such as weathering start. Ignitable liquid weathering is an evaporation process that results in an increase in the abundance of non-volatile compounds relative to volatile compounds, and this process changes the chemical composition. In the present work, the weathering of samples of gasoline at different times (from 0 h to a month) has been studied using an electronic nose (eNose). The influence of the volume used (40 µL and 80 µL) and the type of support (cork, wood, paper and cotton sheet) has been studied. Chemometric tools have been used with the aim of ascertaining the weathering time for which the developed method is capable of detecting the presence of gasoline. The eNose was able to discriminate samples of weathered gasoline. The support used for the samples did not seem to have an influence on the detection and the system.

To obtain a better understanding of the complex clinical conditions and disease mechanisms of severe asthma, the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) consortium was initiated. By generating multi-molecular patient profiles and revealing distinct phenotypes, the consortium aims for more accurate prediction of therapeutic efficacy, and identification of novel targets and mechanistic pathways in both adult and pediatric patients. Noninvasive metabolomics of exhaled air (breathomics) through gas-sensor driven electronic nose (eNose) technology and gas chromatography–mass spectrometry (GC-MS) were two of the many applied omics approaches during this project. Challenging study aspects such as the incorporation of multiple eNose devices and a pan-European multicenter setting led to the establishment of the U-BIOPRED eNose platform, and the development of new sampling and storage strategies of exhaled breath samples. Based on a description of adapted technologies, and by virtue of a proof of concept analysis, this chapter provides a first glimpse into the outcomes of the U-BIOPRED eNose and GC-TOF-MS datasets, combined with information about new insights and future directions for the field of breathomics.

Laktaatti on maitohapon anioni. Maitohappoasidoosiksi kutsutaan tilaa, jossa veren laktaattiarvot ovat jatkuvasti yli 5 mmol/l ja elimistössä vallitsee metabolinen asidoosi. Veren laktaattipitoisuudet kohoavat esimerkiksi rasituksessa, vakavissa infektioissa ja sydänpysähdyksen jälkeen. Laktaattipitoisuuden määrittämistä käytetään erityisesti arvioitaessa sairauden vakavuutta ja hoidon vastetta. Perinteisesti laktaattipitoisuus on määritelty verinäytteen perusteella. Ionimobiliteettispektrometri erottelee molekyylejä niiden varauksen, muodon ja massan mukaan. DMS:ssä (differential mobility spectrometry) on lisäksi muuttuva sähkökenttä. Sovelluksia näillä laitteilla on turvallisuusviranomaisilla ja lisääntyvästi myös terveydenhuollossa. Laadimme tutkimusasetelman, jossa nuoret perusterveet koehenkilöt rasittivat itseään polkupyöräergometrilla nousevin vastuksin. Rasituskoeprotokollan aikana määritettiin subjektiivinen rasitusaste ja otettiin hengitysilmanäytteitä sekä kapillaarinäytteitä laktaattipitoisuuden määrittämiseksi verestä. Hengitysilmanäyte analysoitiin sen jälkeen DMS-laitteella. Tutkimushypoteesinamme oli, että veren laktaattipitoisuudet korreloivat hengitysilmasta mitattavissa olevan laktaatin kanssa. Tutkimuksessamme laktaattipitoisuuden korrelaatio pyöräilyvastuksen kanssa oli 0.62 ja subjektiivisen rasitustason kanssa 0.81. Laktaatin havaitseminen hengitysilmasta DMS:ää käyttäen vaikuttaa mahdolliselta. Tulevaisuudessa DMS:ää voisi hyödyntää kenttäolosuhteissa nopeaan ja noninvasiiviseen laktaatin määrittämiseen. Tämän opinnäytteen alkuperäisyys on tarkastettu Turnitin OriginalityCheck-ohjelmalla Tampereen yliopiston laatujärjestelmän mukaisesti.

Tausta. Iho- ja pehmytkudosinfektiot, kuten leikkaushaavojen infektiot, ovat suhteellisen yleisiä ja aiheuttavat yhteiskunnalle merkittävää kuormitusta. Nopeaa ja kustannustehokasta menetelmää infektion aiheuttajan tunnistamiseen ei ole kehitetty. Bakteeriviljely on edullinen menetelmä, mutta tulosten valmistuminen kestää usein päiviä. Nopeampien menetelmien, kuten PCR:n ongelmana on niiden vaatimat kalliit laitteistot ja se, että ne vaativat erikoiskoulutettua henkilökuntaa. Nopean ja edullisen menetelmän puuttuessa antibioottihoito joudutaan usein aloittamaan ilman tarkempaa tietoa infektion aiheuttajasta. Menetelmät. Tutkimuksessa käytettiin elektronista nenää (eNose) yleisimpien haavainfektioita aiheuttavien bakteerien tunnistamisessa. Tutkittavat bakteerit viljeltiin petrimaljoille, ja niiden erittämää kaasuseosta analysoitiin eNosella. Tulokset. Enose kykeni erottelemaan Staphylococcus aureuksen, Streptococcus pyogeneksen, Escherichia colin, Pseudomonas aeruginosan ja Clostridium perfringensin 78 % täsmällisyydellä minuuteissa ilman edeltävää näytteiden valmistelua. Laite erotti myös metisilliiniresistentin ja metisilliinisensitiivisen S. aureuksen 83 %:n herkkyydellä ja 100 %:n tarkkuudella. Johtopäätös. Tulokset osoittavat, että eNosea voidaan käyttää bakteerien nopeaan tunnistamiseen viljelmistä. Lisäksi laite kykeni erottelemaan MRSA:n ja MSSA:n toisistaan, joten sitä voisi käyttää myös antibioottiherkkyyden määrittämiseen.

Respiratory infections, viral or bacterial, are a common cause of acute exacerbations of chronic obstructive pulmonary disease (AECOPD). A rapid, point-of-care, and easy-to-use tool distinguishing viral and bacterial from other causes would be valuable in routine clinical care. An electronic nose (e-nose) could fit this profile but has never been tested in this setting before. In a single-center registered trial (NTR 4601) patients admitted with AECOPD were tested with the Aeonose (R) electronic nose, and a diagnosis of viral or bacterial infection was obtained by bacterial culture on sputa and viral PCR on nose swabs. A neural network with leave-10%-out cross-validation was used to assess the e-nose data. Forty three patients were included. In the bacterial infection model, 22 positive cases were tested versus the negatives; and similarly 18 positive cases were tested in the viral infection model. The Aeonose was able to distinguish between COPD-subjects suffering from a viral infection and COPD patients without infection, showing an area under the curve (AUC) of 0.74. Similarly, for bacterial infections, an AUC of 0.72 was obtained. The Aeonose e-nose yields promising results in 'smelling' the presence or absence of a viral or bacterial respiratory infection during an acute exacerbation of COPD. Validation of these results using a new and large cohort is required before introduction into clinical practice.

[EN] Nowadays, new technologies tend to evolve to a concept known as the cloud. The cloud is a concept that gives name to both the provided apps and services, and the hardware and software on the datacenters that provide those services. The main goal of the following thesis is to explain the theoretical foundations and the procedures followed for the development of a web application that will be used for data processing on an eNose. This eNose is based on another electronic nose provided with thirtytwo gas sensors. The original device uses a National Instruments data acquisition card and a computer provided with LabVIEW programmed software for the data monitoring and storage. The main concern of the contemporary engineering is energy saving and resources optimization. From this point, it is proposed to design an eNose with only four gas sensors that can be acquirable through a low consuming embedded prototype. Another important principle is the usage of free open tools, such as GNU Linux systems, and developed software through languages like Python or Java, [ES] Actualmente, las nuevas tecnologías tienden a evolucionar hacia un concepto llamado la nube. La nube es un concepto que se refiere tanto a las aplicaciones entregadas como servicios en Internet como al hardware y sistemas de software en los centros de datos que prestan estos servicios. La actual tesina tiene como objetivo explicar las bases teóricas necesarias y los procedimientos seguidos para el desarrollo de una aplicación web que será utilizada para el tratamiento de datos en la nube de un eNose. Este eNose se basa en otra nariz electrónica con treinta y dos sensores de gas. El dispositivo original utiliza una tarjeta de adquisición de datos de National Instruments para la parte de adquisición de los sensores y un ordenador con un software programado en LabVIEW para la parte de monitorización y almacenamiento de los datos. La principal preocupación en la actualidad de la ingeniería electrónica es el bajo consumo y la optimización de recursos. Con esta base se propone diseñar un eNose con sólo cuatro sensores de gas, que sea adquirible mediante un prototipo empotrado de bajo consumo. Otro punto importante es la utilización de herramientas (hardware y software) libres, como sistemas GNU Linux y software desarrollado mediante lenguajes libres como Python o Java.

Currently, many different methods are being used for pre-processing, statistical analysis and validation of data obtained by electronic nose technology from exhaled air. These various methods, however, have never been thoroughly compared. We aimed to empirically evaluate and compare the influence of different dimension reduction, classification and validation methods found in published studies on the diagnostic performance in several datasets. Our objective was to facilitate the selection of appropriate statistical methods and to support reviewers in this research area. We reviewed the literature by searching Pubmed up to the end of 2014 for all human studies using an electronic nose and methodological quality was assessed using the QUADAS-2 tool tailored to our review. Forty-six studies were evaluated regarding the range of different approaches to dimension reduction, classification and validation. From forty-six reviewed articles only seven applied external validation in an independent dataset, mostly with a case-control design. We asked their authors to share the original datasets with us. Four of the seven datasets were available for re-analysis. Published statistical methods for eNose signal analysis found in the literature review were applied to the training set of each dataset. The performance (area under the receiver operating characteristics curve (ROC-AUC)) was calculated for the training cohort (in-set) and after internal validation (leave-one-out cross validation). The methods were also applied to the external validation set to assess the external validity of the performance. Risk of bias was high in most studies due to non-random selection of patients. Internal validation resulted in a decrease in ROC-AUCs compared to in-set performance: -0.15,-0.14,-0.1,-0.11 in dataset 1 through 4, respectively. External validation resulted in lower ROC-AUC compared to internal validation in dataset 1 (-0.23) and 3 (-0.09). ROC-AUCs did not decrease in dataset 2 (+0.07) and 4 (+0.04). No single combination of dimension reduction and classification methods gave consistent results between internal and external validation sets in this sample of four datasets. This empirical evaluation showed that it is not meaningful to estimate the diagnostic performance on a training set alone, even after internal validation. Therefore, we recommend the inclusion of an external validation set in all future eNose projects in medicine.