Validity of clinical psoriatic arthritis diagnoses made by rheumatologists in the Swedish National Patient Register.

OBJECTIVES: : Knowledge of the correspondence between clinical ICD diagnoses and classification criteria fulfilment is crucial to interpret studies identifying cases via ICD codes. We assessed the degree to which patients registered with ICD-10 diagnoses of psoriatic arthritis (PsA) in the Swedish National Patient Register (NPR) fulfil established PsA classification criteria. METHOD: Four hundred patients with at least one outpatient visit to one of five rheumatology or internal medicine departments (three university/two county departments across Sweden) in 2013-2015, with a main ICD-10 diagnosis of PsA (L40.5, M07.0-M07.3), were randomly selected (80 cases/site). Through a structured medical record review, positive predictive values (PPVs) for fulfilment of the following classification criteria were assessed: CASPAR, Moll and Wright, Vasey and Espinoza, and modified ESSG criteria for PsA. A subset analysis regarding CASPAR fulfilment was also performed among cases with available rheumatoid factor and peripheral X-ray status (central CASPAR items; n = 227). RESULTS: Of the 400 patients with a main ICD-10 diagnosis of PsA, 343 (86%) fulfilled at least one of the four PsA classification criteria. PPVs for the different criteria were: CASPAR 69% (82% in the subset analysis), Moll and Wright 51%, Vasey and Espinoza 76%, and modified ESSG 64%. Overall, only 6.5% of the 400 PsA diagnoses were judged as clearly incorrect by the medical record reviewers. CONCLUSION: The validity of rheumatologist-made, clinical ICD-10 diagnoses for PsA in the Swedish NPR is good, with PPVs of 69-82% for CASPAR fulfilment and 86% for meeting any established PsA classification criteria.

Comparison of agreement between internet-based registration of patient-reported outcomes and clinic-based paper forms within the Swedish Rheumatology Quality Register.

Objective: The Swedish Rheumatology Quality Register has implemented an internet-based method (PER) for registering patient-recorded outcome measures. The aim of this study was to compare the agreement between visual analogue scales (VASs) reported via PER and clinic-based reporting using paper forms. Methods: In a cross-sectional study (70 patients), the results of 79 registrations of VASs for global health, pain, and fatigue from PER were compared with corresponding clinic-based paper registrations. For patients with polyarthritis, 28-joint count Disease Activity Scores (DAS28) were computed. Patients with axial disease also completed Bath Ankylosing Spondylitis Disease Activity Index and Functional Index (BASDAI and BASFI) questionnaires. Mean differences and intraclass correlation coefficients (ICCs) were calculated. Agreement was visualized using Bland-Altman plots. Results: No statistically significant differences in VASs were found comparing PER and paper forms for VAS Global, VAS Pain, and VAS Fatigue (p = 0.295, 0.463, and 0.288, respectively). ICCs for VAS Global, Pain, and Fatigue ranged from 0.889 to 0.952, indicating excellent agreement. Bland-Altman plots for VAS did not show any proportional bias. The mean difference for DAS28 calculated by VASs from paper vs PER was -0.02 (n = 65, p = 0.660), and the mean difference for BASDAI was 0.04 (n = 11, p = 0.742). ICCs for DAS28 and BASDAI were 0.962 and 0.985, respectively. Of the participating patients, 60% preferred PER. Conclusion: Internet-based reporting for patient-reported outcomes in a clinical setting resulted in similar data for VASs and corresponding disease activity scores to clinic-based reporting on paper forms.

Biological treatment in ankylosing spondylitis in the Nordic countries during 2010-2016: a collaboration between five biological registries.

OBJECTIVES: Large-scale observational cohorts may be used to study the effectiveness and rare side effects of biological disease-modifying anti-rheumatic drugs (bDMARDs) in ankylosing spondylitis (AS), but may be hampered by differences in baseline characteristics and disease activity across countries. We aimed to explore the research infrastructure in the five Nordic countries regarding bDMARD treatment in AS. METHOD: This observational cohort study was based on data from biological registries in Denmark (DANBIO), Sweden (SRQ/ARTIS), Finland (ROB-FIN), Norway (NOR-DMARD), and Iceland (ICEBIO). Data were collected for the years 2010-2016. Registry coverage, registry inventory (patient characteristics, disease activity measures), and national guidelines for bDMARD prescription in AS were described per country. Incident (first line) and prevalent bDMARD use per capita, country, and year were calculated. In AS patients who started first line bDMARDs during 2010-2016 (n = 4392), baseline characteristics and disease activity measures were retrieved. RESULTS: Registry coverage of bDMARD-treated patients ranged from 60% to 95%. All registries included extensive prospectively collected data at patient level. Guidelines regarding choice of first line drug and prescription patterns varied across countries. During the period 2010-2016 prevalent bDMARD use increased (p < 0.001), whereas incident use tended to decrease (p for trend < 0.004), with large national variations (e.g. 2016 incidence: Iceland 10.7/100 000, Finland 1.7/100 000). Baseline characteristics were similar regarding C-reactive protein, but differed for other variables, including the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) (range 3.5-6.3) and Ankylosing Spondylitis Disease Activity Score (ASDAS) (2.7-3.8) (both p < 0.0001). CONCLUSION: Collaboration across the five Nordic biological registries regarding bDMARD use in AS is feasible but national differences in coverage, prescription patterns, and patient characteristics must be taken into account depending on the scientific question.

Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis.

One of the major problems in bioimaging, often highly underestimated, is whether features extracted for a discrimination or regression task will remain valid for a broader set of similar experiments or in the presence of unpredictable perturbations during the image acquisition process. Such an issue is even more important when it is addressed in the context of deep learning features due to the lack of a priori known relationship between the black-box descriptors (deep features) and the phenotypic properties of the biological entities under study. In this regard, the widespread use of descriptors, such as those coming from pre-trained Convolutional Neural Networks (CNNs), is hindered by the fact that they are devoid of apparent physical meaning and strongly subjected to unspecific biases, i.e., features that do not depend on the cell phenotypes, but rather on acquisition artifacts, such as brightness or texture changes, focus shifts, autofluorescence or photobleaching. The proposed Deep-Manager software platform offers the possibility to efficiently select those features having lower sensitivity to unspecific disturbances and, at the same time, a high discriminating power. Deep-Manager can be used in the context of both handcrafted and deep features. The unprecedented performances of the method are proven using five different case studies, ranging from selecting handcrafted green fluorescence protein intensity features in chemotherapy-related breast cancer cell death investigation to addressing problems related to the context of Deep Transfer Learning. Deep-Manager, freely available at https://github.com/BEEuniroma2/Deep-Manager , is suitable for use in many fields of bioimaging and is conceived to be constantly upgraded with novel image acquisition perturbations and modalities.

Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.

High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from genes to cells, from cells to organs, and through the whole organism. The combination of phenomics, deep learning, and machine learning represents a strong potential for the phenotypical investigation, leading the way to a more embracing approach, called machine learning phenomics (MLP). In particular, in this work we present a novel MLP platform for phenomics investigation of cancer-cells response to therapy, exploiting and combining the potential of time-lapse microscopy for cell behavior data acquisition and robust deep learning software architectures for the latent phenotypes extraction. A two-step proof of concepts is designed. First, we demonstrate a strict correlation among gene expression and cell phenotype with the aim to identify new biomarkers and targets for tailored therapy in human colorectal cancer onset and progression. Experiments were conducted on human colorectal adenocarcinoma cells (DLD-1) and their profile was compared with an isogenic line in which the expression of LOX-1 transcript was knocked down. In addition, we also evaluate the phenotypic impact of the administration of different doses of an antineoplastic drug over DLD-1 cells. Under the omics paradigm, proteomics results are used to confirm the findings of the experiments.

Accelerating the experimental responses on cell behaviors: a long-term prediction of cell trajectories using Social Generative Adversarial Network.

The incremented uptake provided by time-lapse microscopy in Organ-on-a-Chip (OoC) devices allowed increased attention to the dynamics of the co-cultured systems. However, the amount of information stored in long-time experiments may constitute a serious bottleneck of the experimental pipeline. Forward long-term prediction of cell trajectories may reduce the spatial-temporal burden of video sequences storage. Cell trajectory prediction becomes crucial especially to increase the trustworthiness in software tools designed to conduct a massive analysis of cell behavior under chemical stimuli. To address this task, we transpose here the exploitation of the presence of "social forces" from the human to the cellular level for motion prediction at microscale by adapting the potential of Social Generative Adversarial Network predictors to cell motility. To demonstrate the effectiveness of the approach, we consider here two case studies: one related to PC-3 prostate cancer cells cultured in 2D Petri dishes under control and treated conditions and one related to an OoC experiment of tumor-immune interaction in fibrosarcoma cells. The goodness of the proposed strategy has been verified by successfully comparing the distributions of common descriptors (kinematic descriptors and mean interaction time for the two scenarios respectively) from the trajectories obtained by video analysis and the predicted counterparts.

Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments.

We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microscopy images. The approach was inspired by the recent successes in application of machine learning for style recognition in paintings and artistic style transfer. The originality of the method relies i) on the generation of atlas from the collection of single-cell trajectories in order to visually encode the multiple descriptors of cell motility, and ii) on the application of pre-trained Deep Learning Convolutional Neural Network architecture in order to extract relevant features to be used for classification tasks from this visual atlas. Validation tests were conducted on two different cell motility scenarios: 1) a 3D biomimetic gels of immune cells, co-cultured with breast cancer cells in organ-on-chip devices, upon treatment with an immunotherapy drug; 2) Petri dishes of clustered prostate cancer cells, upon treatment with a chemotherapy drug. For each scenario, single-cell trajectories are very accurately classified according to the presence or not of the drugs. This original approach demonstrates the existence of universal features in cell motility (a so called "motility style") which are identified by the DL approach in the rationale of discovering the unknown message in cell trajectories.

The influence of spatial and temporal resolutions on the analysis of cell-cell interaction: a systematic study for time-lapse microscopy applications.

Cell-cell interactions are an observable manifestation of underlying complex biological processes occurring in response to diversified biochemical stimuli. Recent experiments with microfluidic devices and live cell imaging show that it is possible to characterize cell kinematics via computerized algorithms and unravel the effects of targeted therapies. We study the influence of spatial and temporal resolutions of time-lapse videos on motility and interaction descriptors with computational models that mimic the interaction dynamics among cells. We show that the experimental set-up of time-lapse microscopy has a direct impact on the cell tracking algorithm and on the derived numerical descriptors. We also show that, when comparing kinematic descriptors in two diverse experimental conditions, too low resolutions may alter the descriptors' discriminative power, and so the statistical significance of the difference between the two compared distributions. The conclusions derived from the computational models were experimentally confirmed by a series of video-microscopy acquisitions of co-cultures of unlabelled human cancer and immune cells embedded in 3D collagen gels within microfluidic devices. We argue that the experimental protocol of acquisition should be adapted to the specific kind of analysis involved and to the chosen descriptors in order to derive reliable conclusions and avoid biasing the interpretation of results.

[Anxiety, job stress and job insecurity among teachers with indefinite or definite time contract]. / Ansia, stress lavorativo e "job insecurity" negli insegnanti a tempo indeterminato e determinato.

Object of this study was the occupational stress of 336 teachers (276 women and 60 men) with stable or temporary employment in schools of Pescara, town of Central Italy. The levels of anxiety were determined by STAI and STAI II, those of job strain", "job insecurity" and social support by the Karasek's questionnaire, and the perception of subjective symptoms by a 12 item test. There were no significant differences depending on the type of school. The women with temporary contract showed higher levels of "job insecurity", while the men with temporary job showed also higher values of STAI I and STAI II. The scores of anxiety of the women were positively correlated with "job strain", "job insecurity" and perception of subjective symptoms and negatively with social support, while the only correlations of STAI I and STAI II of men showing statistical significance of men was that with "job insecurity. Job strain was negatively correlated with the perception of symptoms both in women and men. These results evidence differences in the occupational stress of men and women; in particular, job insecurity may enhance anxiety in men.

[The perception of risk in construction workers]. / La percezione del rischio negli operatori edili.

Accidents in building sites led us to study risk perception in workers employed in this field. We disposed of 300 workers, aged 18-65, who had to answer to a questionnaries were signing among seven risks the more present in their activity, chosing among five levels. So for each risk the worker had to quantify parameters such as: danger, frequence of accidents, actitude to risk-control, training on risk. The risk of falling from high sites has been perceived as extremely dangerous but it is possible to control it. Less dangerous is eyes exposition to chemical agents or dust. Manual movementation of weight and lacerations would be the most frequent source of accidents. Moreover would be insufficient the capacity to control the rick related to Weight manual movementation and vibrations. Workers answered in questionnaries that risk training on vibration was not sufficient, on the contrary they knew the risk of falling from high site. So we conclude that to reduce accidents in building sites it is necessary make better training on field, modifying the individual behavior among workers.

[Occupational stress and job insecurity in men working in a university]. / Stress lavorativo e "job insecurity" in uomini che lavorano nell'università.

77 men working in a university were investigated. Trait and state anxiety were determined by STAI I and STAI II; job strain (job demand/decision latitude), social support and job insecurity were analysed by a 46 item Karasek's questionnaire and subjective symptoms by a 12 item test. The employees of a library (mean age 49 years), in contact with students, showed significantly higher values of job strain, STAI I, STAI II and subjective symptoms than a control group of employees with similar age. Young employees and sanitary staff with temporary employment showed higher level of job insecurity than control subjects with stable position. Blood cytotoxic activity (reported in another study) was significantly lower in the old employees with job strain or in the young employees with job insecurity (but not in the sanitary staff) than in the controls; this demonstrates that not only occupational stress but also job insecurity may play an important role in affecting the health status.

Thiolation and nitrosation of cysteines in biological fluids and cells.

Thiols (RSH) are potent nucleophilic agents, the rates of which depend on the pKa of the sulfhydryl. Unlike compounds having other nucleophile moieties (-OH or -NH(2)), RSH are involved in reactions, such as conjugations, redox and exchange reactions. Although protein SH groups (PSH) react like non-protein thiols (NPSH), the biochemistry of proteins is much more complex for reasons such as steric hindrance, charge distribution and accessibility of PSH to the solvent (protein conformation). The reaction rates and types of end-products of PSH vary a lot from protein to protein. The biological problem is even more complex because in all compartments and tissues, there may be specific competition between thiols (namely between GSH and PSH), regulated by the properties of antioxidant enzymes. Moreover, PSH are divided biologically into essential and non-essential and their respective influence in the various biological systems is unknown. It follows that during phenomena eliciting a prompt thiol response (oxidative stress), the antioxidant PSH response and reaction mechanisms vary considerably from case to case. For example, in spite of a relatively low pKa that should guarantee good antioxidant capacity, PSH of albumin has much less propensity to form adducts with conjugating agents than NPSH; moreover, the structural characteristics of the protein prevent albumin from forming protein disulfides when exposed to oxidants (whereas protein-thiol mixed disulfides are formed in relative abundance). On the other hand, proteins with a relatively high reactivity, such rat hemoglobin, have much greater antioxidant capacity than GSH, but although human hemoglobin has a pKa similar to GSH, for structural reasons it has less antioxidant capacity than GSH. When essential PSH are involved in S-thiolation and S-nitrosation reactions, a similar change in biological activity is observed. S-thiolated proteins are a recurrent phenomenon in oxidative stress elicited by reactive oxygen species (ROS). This event may be mediated by disulfides, that exchange with PSH, or by the protein intermediate sulfenic acid that reacts with thiols to form protein-mixed disulfides. During nitrosative stress elicited by reactive nitrogen species (RNS), depending on the oxygen concentration of the system, nitrosation reactions of thiols may also be accompanied by protein S-thiolation. In this review we discuss a number of cell processes and biochemical modifications of enzymes that indicate that S-thiolation and S-nitrosation may occur simultaneously in the same protein in the presence of appropriate interactions between ROS and RNS.