SARS-CoV-2 ORF8 accessory protein is a virulence factor.

IMPORTANCE: The relevance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ORF8 in the pathogenesis of COVID-19 is unclear. Virus natural isolates with deletions in ORF8 were associated with wild milder disease, suggesting that ORF8 might contribute to SARS-CoV-2 virulence. This manuscript shows that ORF8 is involved in inflammation and in the activation of macrophages in two experimental systems: humanized K18-hACE2 transgenic mice and organoid-derived human airway cells. These results identify ORF8 protein as a potential target for COVID-19 therapies.

Pulmonary function three to five months after hospital discharge for COVID-19: a single centre cohort study.

Some COVID-19 survivors suffer from persistent pulmonary function impairment, but the extent and associated factors are unclear. This study aimed to characterize pulmonary function impairment three to five months after hospital discharge and the association with disease severity. Survivors of COVID-19 after hospitalization to the VieCuri Medical Centre between February and December 2020 were invited for follow-up, three to five months after discharge. Dynamic and static lung volumes, respiratory muscle strength and diffusion capacity were measured. The cohort comprised 257 patients after a moderate (n = 33), severe (n = 151) or critical (n = 73) COVID-19 infection with a median follow-up of 112 days (interquartile range 96-134 days). The main sequelae included reduced diffusion capacity (36%) and reduced maximal expiratory pressure (24%). Critically ill patients were more likely to have reduced diffusion capacity than moderate (OR 8.00, 95% CI 2.46-26.01) and severe cases (OR 3.74, 95% CI 1.88-7.44) and lower forced vital capacity (OR 3.29, 95% CI 1.20-9.06) compared to severe cases. Many COVID-19 survivors, especially after a critical disease course, showed pulmonary function sequelae, mainly DLCO impairments, three to five months after discharge. Monitoring is needed to investigate the persistence of these symptoms and the longer-term implications of the COVID-19 burden.

Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning.

BACKGROUND: For mechanically ventilated critically ill COVID-19 patients, prone positioning has quickly become an important treatment strategy, however, prone positioning is labor intensive and comes with potential adverse effects. Therefore, identifying which critically ill intubated COVID-19 patients will benefit may help allocate labor resources. METHODS: From the multi-center Dutch Data Warehouse of COVID-19 ICU patients from 25 hospitals, we selected all 3619 episodes of prone positioning in 1142 invasively mechanically ventilated patients. We excluded episodes longer than 24 h. Berlin ARDS criteria were not formally documented. We used supervised machine learning algorithms Logistic Regression, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machine and Extreme Gradient Boosting on readily available and clinically relevant features to predict success of prone positioning after 4 h (window of 1 to 7 h) based on various possible outcomes. These outcomes were defined as improvements of at least 10% in PaO2/FiO2 ratio, ventilatory ratio, respiratory system compliance, or mechanical power. Separate models were created for each of these outcomes. Re-supination within 4 h after pronation was labeled as failure. We also developed models using a 20 mmHg improvement cut-off for PaO2/FiO2 ratio and using a combined outcome parameter. For all models, we evaluated feature importance expressed as contribution to predictive performance based on their relative ranking. RESULTS: The median duration of prone episodes was 17 h (11-20, median and IQR, N = 2632). Despite extensive modeling using a plethora of machine learning techniques and a large number of potentially clinically relevant features, discrimination between responders and non-responders remained poor with an area under the receiver operator characteristic curve of 0.62 for PaO2/FiO2 ratio using Logistic Regression, Random Forest and XGBoost. Feature importance was inconsistent between models for different outcomes. Notably, not even being a previous responder to prone positioning, or PEEP-levels before prone positioning, provided any meaningful contribution to predicting a successful next proning episode. CONCLUSIONS: In mechanically ventilated COVID-19 patients, predicting the success of prone positioning using clinically relevant and readily available parameters from electronic health records is currently not feasible. Given the current evidence base, a liberal approach to proning in all patients with severe COVID-19 ARDS is therefore justified and in particular regardless of previous results of proning.

Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records.

PURPOSE: To assess, validate and compare the predictive performance of models for in-hospital mortality of COVID-19 patients admitted to the intensive care unit (ICU) over two different waves of infections. Our models were built with high-granular Electronic Health Records (EHR) data versus less-granular registry data. METHODS: Observational study of all COVID-19 patients admitted to 19 Dutch ICUs participating in both the national quality registry National Intensive Care Evaluation (NICE) and the EHR-based Dutch Data Warehouse (hereafter EHR). Multiple models were developed on data from the first 24 h of ICU admissions from February to June 2020 (first COVID-19 wave) and validated on prospective patients admitted to the same ICUs between July and December 2020 (second COVID-19 wave). We assessed model discrimination, calibration, and the degree of relatedness between development and validation population. Coefficients were used to identify relevant risk factors. RESULTS: A total of 1533 patients from the EHR and 1563 from the registry were included. With high granular EHR data, the average AUROC was 0.69 (standard deviation of 0.05) for the internal validation, and the AUROC was 0.75 for the temporal validation. The registry model achieved an average AUROC of 0.76 (standard deviation of 0.05) in the internal validation and 0.77 in the temporal validation. In the EHR data, age, and respiratory-system related variables were the most important risk factors identified. In the NICE registry data, age and chronic respiratory insufficiency were the most important risk factors. CONCLUSION: In our study, prognostic models built on less-granular but readily-available registry data had similar performance to models built on high-granular EHR data and showed similar transportability to a prospective COVID-19 population. Future research is needed to verify whether this finding can be confirmed for upcoming waves.

INCIDENCE, RISK FACTORS, AND OUTCOME OF SUSPECTED CENTRAL VENOUS CATHETER-RELATED INFECTIONS IN CRITICALLY ILL COVID-19 PATIENTS: A MULTICENTER RETROSPECTIVE COHORT STUDY.

ABSTRACT: Background: Aims of this study were to investigate the prevalence and incidence of catheter-related infection, identify risk factors, and determine the relation of catheter-related infection with mortality in critically ill COVID-19 patients. Methods: This was a retrospective cohort study of central venous catheters (CVCs) in critically ill COVID-19 patients. Eligible CVC insertions required an indwelling time of at least 48 hours and were identified using a full-admission electronic health record database. Risk factors were identified using logistic regression. Differences in survival rates at day 28 of follow-up were assessed using a log-rank test and proportional hazard model. Results: In 538 patients, a total of 914 CVCs were included. Prevalence and incidence of suspected catheter-related infection were 7.9% and 9.4 infections per 1,000 catheter indwelling days, respectively. Prone ventilation for more than 5 days was associated with increased risk of suspected catheter-related infection; odds ratio, 5.05 (95% confidence interval 2.12-11.0). Risk of death was significantly higher in patients with suspected catheter-related infection (hazard ratio, 1.78; 95% confidence interval, 1.25-2.53). Conclusions: This study shows that in critically ill patients with COVID-19, prevalence and incidence of suspected catheter-related infection are high, prone ventilation is a risk factor, and mortality is higher in case of catheter-related infection.

Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study.

Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of -0.04 [-0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of -0.19 [-0.27; -0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research.

Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality.

BACKGROUND: The prediction of in-hospital mortality for ICU patients with COVID-19 is fundamental to treatment and resource allocation. The main purpose was to develop an easily implemented score for such prediction. METHODS: This was an observational, multicenter, development, and validation study on a national critical care dataset of COVID-19 patients. A systematic literature review was performed to determine variables possibly important for COVID-19 mortality prediction. Using a logistic multivariable model with a LASSO penalty, we developed the Rapid Evaluation of Coronavirus Illness Severity (RECOILS) score and compared its performance against published scores. RESULTS: Our development (validation) cohort consisted of 1480 (937) adult patients from 14 (11) Dutch ICUs admitted between March 2020 and April 2021. Median age was 65 (65) years, 31% (26%) died in hospital, 74% (72%) were males, average length of ICU stay was 7.83 (10.25) days and average length of hospital stay was 15.90 (19.92) days. Age, platelets, PaO2/FiO2 ratio, pH, blood urea nitrogen, temperature, PaCO2, Glasgow Coma Scale (GCS) score measured within +/-24 h of ICU admission were used to develop the score. The AUROC of RECOILS score was 0.75 (CI 0.71-0.78) which was higher than that of any previously reported predictive scores (0.68 [CI 0.64-0.71], 0.61 [CI 0.58-0.66], 0.67 [CI 0.63-0.70], 0.70 [CI 0.67-0.74] for ISARIC 4C Mortality Score, SOFA, SAPS-III, and age, respectively). CONCLUSIONS: Using a large dataset from multiple Dutch ICUs, we developed a predictive score for mortality of COVID-19 patients admitted to ICU, which outperformed other predictive scores reported so far.

Predictors for extubation failure in COVID-19 patients using a machine learning approach.

INTRODUCTION: Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. METHODS: We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. RESULTS: A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure. CONCLUSION: The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records.

Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome.

OBJECTIVES: As coronavirus disease 2019 is a novel disease, treatment strategies continue to be debated. This provides the intensive care community with a unique opportunity as the population of coronavirus disease 2019 patients requiring invasive mechanical ventilation is relatively homogeneous compared with other ICU populations. We hypothesize that the novelty of coronavirus disease 2019 and the uncertainty over its similarity with noncoronavirus disease 2019 acute respiratory distress syndrome resulted in substantial practice variation between hospitals during the first and second waves of coronavirus disease 2019 patients. DESIGN: Multicenter retrospective cohort study. SETTING: Twenty-five hospitals in the Netherlands from February 2020 to July 2020, and 14 hospitals from August 2020 to December 2020. PATIENTS: One thousand two hundred ninety-four critically ill intubated adult ICU patients with coronavirus disease 2019 were selected from the Dutch Data Warehouse. Patients intubated for less than 24 hours, transferred patients, and patients still admitted at the time of data extraction were excluded. MEASUREMENTS AND MAIN RESULTS: We aimed to estimate between-ICU practice variation in selected ventilation parameters (positive end-expiratory pressure, Fio2, set respiratory rate, tidal volume, minute volume, and percentage of time spent in a prone position) on days 1, 2, 3, and 7 of intubation, adjusted for patient characteristics as well as severity of illness based on Pao2/Fio2 ratio, pH, ventilatory ratio, and dynamic respiratory system compliance during controlled ventilation. Using multilevel linear mixed-effects modeling, we found significant (p ≤ 0.001) variation between ICUs in all ventilation parameters on days 1, 2, 3, and 7 of intubation for both waves. CONCLUSIONS: This is the first study to clearly demonstrate significant practice variation between ICUs related to mechanical ventilation parameters that are under direct control by intensivists. Their effect on clinical outcomes for both coronavirus disease 2019 and other critically ill mechanically ventilated patients could have widespread implications for the practice of intensive care medicine and should be investigated further by causal inference models and clinical trials.

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients.

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients. METHODS: A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers. RESULTS: Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive. CONCLUSIONS: In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine.

Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse.

BACKGROUND: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. METHODS: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. RESULTS: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. CONCLUSION: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes.

Human Respiratory Syncytial Virus Subgroup A and B Infections in Nasal, Bronchial, Small-Airway, and Organoid-Derived Respiratory Cultures.

Human respiratory syncytial virus (HRSV) is the leading cause of bronchiolitis in infants. Two subgroups of HRSV (A and B) routinely cocirculate. Most research has been performed with HRSV-A strains because these are easier to culture than HRSV-B strains. In this study, we aimed to compare the replicative fitness and HRSV-induced innate cytokine responses of HRSV-A and HRSV-B strains in disease-relevant cell culture models. We used two recombinant (r) clinical isolate-based HRSV strains (A11 and B05) and one recombinant laboratory-adapted HRSV strain (A2) to infect commercially available nasal, bronchial, and small-airway cultures. Epithelial cells from all anatomical locations were susceptible to HRSV infection despite the induction of a dominant type III interferon response. Subgroup A viruses disseminated and replicated faster than the subgroup B virus. Additionally, we studied HRSV infection and innate responses in airway organoids (AOs) cultured at air-liquid interface (ALI). Results were similar to the commercially obtained bronchial cells. In summary, we show that HRSV replicates well in cells from both the upper and the lower airways, with a slight replicative advantage for subgroup A viruses. Lastly, we showed that AOs cultured at ALI are a valuable model for studying HRSV ex vivo and that they can be used in the future to study factors that influence HRSV disease severity.IMPORTANCE Human respiratory syncytial virus (HRSV) is the major cause of bronchiolitis and pneumonia in young infants and causes almost 200,000 deaths per year. Currently, there is no vaccine or treatment available, only a prophylactic monoclonal antibody (palivizumab). An important question in HRSV pathogenesis research is why only a fraction (1 to 3%) of infants develop severe disease. Model systems comprising disease-relevant HRSV isolates and accurate and reproducible cell culture models are indispensable to study infection, replication, and innate immune responses. Here, we used differentiated AOs cultured at ALI to model the human airways. Subgroup A viruses replicated better than subgroup B viruses, which we speculate fits with epidemiological findings that subgroup A viruses cause more severe disease in infants. By using AOs cultured at ALI, we present a highly relevant, robust, and reproducible model that allows for future studies into what drives severe HRSV disease.

The Formation of a Modified Surface Layer on Elastomeric Materials.

Surface modification of an elastomer may be formed during sliding contact with a rigid counter surface. This alteration leads to a change of mechanical properties at the surface and as a result a change in frictional behavior. Therefore, investigations related to the formation of a modified surface layer on elastomers and its effect on friction are of importance. In the present study, the formation of a modified surface layer on elastomer reinforced by silica is studied. Sliding friction is performed using a pin-on-disc tribometer. Several parameters are varied, namely contact pressure, velocity, and roughness of the counter surface. The existence of a modified surface layer is investigated by using a scanning electron microscope. The results show that the existence of a modified surface layer depends on the competition between the formation rate of the layer and the wear rate. The formation of the layer depends on the contact pressure, velocity, and sliding distance. A general formulation to calculate the volume of formation is proposed. Furthermore, a map of the formation of a modified surface layer is developed.

Characterization of the Adsorption Mechanism of Manganese Phosphate Conversion Coating Derived Tribofilms.

Casing connections in the oil and gas industry are typically coated with zinc and/or manganese phosphate for corrosion protection during storage. The presence of phosphate coatings is also known to give beneficial tribological performance. The coating allows the system to run without problems long after it is worn off. This is because of two mechanisms. Glaze layer formation on the coated surface and, as will be shown, tribofilm formation on the uncoated counter-surface. An investigation into the mechanism behind this tribofilm formation is presented in this paper. The aim is to develop lubricants that exploit these mechanisms. A pin-on-disc set-up was used to investigate the interaction of a manganese phosphated disc and bare counter surface. Six base oils with different polarity and viscosity were used. The resulting tribofilms were analysed using optical microscopy, scanning electron microscopy, X-ray diffractometry, X-ray photoelectron spectroscopy, focused ion beam, and atomic force microscopy. The tribofilm is robust, amorphous, and only forms in the presence of a lubricant under sliding conditions and adsorbs on substrates with a large variation in chemical composition. It is concluded that the tribofilm consists of physisorbed manganese phosphate and formation is shear stress activated.

Hepatosplenic T-cell lymphoma in a 47-year-old Crohn's disease patient on thiopurine monotherapy.

Hepatosplenic T-cell lymphoma (HSTCL) is a rare non-Hodgkin lymphoma with a high mortality rate. Higher incidence is reported in patients with inflammatory bowel disease, specifically in male patients that are younger than 35 years, and have been treated with thiopurine and tumor necrosis factor (TNF)-α inhibitor combination therapy for over 2 years. In this case report we describe a 47-year-old patient with Crohn's disease (CD) who developed HSTCL after having been treated with thiopurine monotherapy for 14 years. To our best knowledge, only eleven cases exist of patients with CD who developed HSTCL while on thiopurine monotherapy. We report the first patient with CD, older than 35 years, who developed HSTCL while on thiopurine monotherapy. This emphasizes that HSTCL risk is not limited to young men receiving both thiopurines and TNF-α inhibitors.

Engineered micro-objects as scaffolding elements in cellular building blocks for bottom-up tissue engineering approaches.

A material-based bottom-up approach is proposed towards an assembly of cells and engineered micro-objects at the macroscale. We show how shape, size and wettability of engineered micro-objects play an important role in the behavior of cells on these objects. This approach can, among other applications, be used as a tool to engineer complex 3D tissues of clinically relevant size.

[Conservative treatment of acute diverticulitis]. / Conservatieve behandeling van acute diverticulitis.

Scientific evidence concerning the treatment of acute diverticulitis is scarce. We describe 2 patients with this condition in this article. The first, a 64-year-old man, came to the emergency room because he had experienced persistent abdominal pain for the previous 4 days. He was diagnosed with uncomplicated diverticulitis. The second patient, a 58-year-old woman, had had pain in her left lower abdomen for 4 weeks; the pain appeared to have been caused by complicated diverticulitis. Both patients were treated conservatively. Only the patient with complicated diverticulitis was administered antibiotics; she underwent surgery at a later date because of persistent pain. Several guidelines recommend the administration of antibiotics; however, a number of recent studies have revealed no benefit to the clinical course from the use of antibiotics. The Dutch guideline, therefore, recommends withholding antibiotics in the acute phase. Conclusive evidence on the best treatment for patients with frequent recurrences or chronic symptoms after an episode of acute diverticulitis is not available. Guidelines advise a personalised treatment strategy for each patient. More research is necessary on the effect of mesalazine in these cases.

[Jaundice and a pancreatic tumour caused by auto-immune pancreatitis]. / Auto-immune pancreatitis als oorzaak van geelzucht en een tumor in het pancreas.

Three male patients aged between 50 and 70 years were referred with jaundice and weight loss. Imaging showed a pancreatic mass and changes in the calibre of the choledochal or pancreatic duct, suggestive of malignancy. Two patients were operated on. One patient was considered to have an unresectable carcinoma but showed remarkable clinical improvement after steroids were given for his poor condition. In the other patient a resection was performed. Histology showed IgG4-positive plasma cell infiltration without signs of malignancy. Eventually these patients were diagnosed with auto-immune pancreatitis (AIP). In the third patient AIP was considered beforehand and this patient was treated with steroids. He responded quickly both clinically and radiologically. CT imaging showed complete remission of the mass. AIP is a benign inflammatory process which can mimic pancreatic carcinoma. In doubtful cases, a short trial of steroids might be considered.

Adherence to safety guidelines on paediatric procedural sedation: the results of a nationwide survey under general paediatricians in The Netherlands.

OBJECTIVES: Following two fatal accidents during paediatric procedural sedation (PS), the authors investigated the level of adherence to established safety standards on PS in a nationwide cohort of fully trained general paediatricians, entrusted with PS. STUDY DESIGN AND METHODS: Sample survey Safety guidelines on PS were split into four domains ("Presedation Assessment", "Monitoring during PS", "Recovery after PS" and "Facilities and Competences for Emergencies and Rescue"). Each domain was operationalised into sub-domains and items. Items were presented within a questionnaire list as procedural points of attention on which respondents could give their personal adherence score. Percentages of full adherence were calculated. Non-adherence was defined as gradual deviation from full adherence. After factor and reliability analysis, observed scores were summed up to scales, and results were transformed to a 0-10 report mark (RM). An RM of ≥9 is considered as a satisfactory level of adherence while an RM <6 is considered as unacceptably low. RESULTS: Full adherence was rare. For most (sub) domains, only a minority of respondents achieved a satisfactory level of adherence. Large numbers of respondents had scores below 6. CONCLUSIONS: Potentially unsafe PS practices are common under Dutch general paediatricians, despite the availability of guidelines. The design of guidelines should include a goal-directed plan for implementation including training, initiatives for continuous quality assurance and improvement and repeated measurements of adherence to guidelines.

Influence of the adipate and dissolved oxygen concentrations on the beta-lactam production during continuous cultivations of a Penicillium chrysogenum strain expressing the expandase gene from Streptomyces clavuligerus.

The influence of adipate concentration and dissolved oxygen on production of adipoyl-7-aminodeacetoxycephalosporanic acid (ad-7-ADCA) by a recombinant strain of Penicillium chrysogenum expressing the expandase gene from Streptomyces clavuligerus was studied in glucose-limited continuous cultures. Operating conditions were maintained constant but the adipate and dissolved oxygen concentrations (DOC) were varied separately in a range from 1 to 37.5gl(-1) and from 2% to 125% air saturation (%AS), respectively. The total beta-lactams specific productivity, r(ptotal), was not significantly changed for adipate concentrations from 5 to 25gl(-1), but the flux towards an unknown by-product decreased as the adipate concentration increased. Investigations at different DOC showed that r(ptotal) was stable around 18 micro molgDW(-1)h(-1) for DOC being in the range from 15 to 125%AS. When DOC was decreased from 15 to 7%AS, r(ptotal) increased to 25 micro molgDW(-1)h(-1), mainly due to a two-fold increase in the adipoyl-6-aminopenicillanic acid (ad-6-APA) specific productivity.