Diagnostic accuracy of computed tomography scout film and chest X-ray for detection of rib fractures in patients with chest trauma: A cross-sectional study.

Background: Rib fractures are a major source of morbidity in patients with chest trauma. Computed tomography (CT) scout film is a low-dose image that is obtained prior to a complete chest CT study for all patients undergoing a CT scan. In this study, we evaluated the diagnostic performance of CT scout film vis-à-vis that of chest X-ray for detection of rib fractures using chest CT scan as the reference standard. Methods: A cross-sectional study was performed at the radiology department of Aga Khan University Hospital (Karachi, Pakistan) from October 1, 2013 to September 31, 2014. Patients who underwent CT chest for evaluation of thoracic trauma were included in the study. Sensitivity and specificity of chest X-ray and CT scout film were calculated. Results: A total of 207 patients were included in the study (193 were male). Penetrating and blunt thoracic injuries affected 104 (50.2%) and 103 (49.8%) patients respectively. On CT chest, 75 (36.2%) patients had evidence of rib fractures. Sensitivity and specificity of CT scout film for detection of rib fractures were 56% and 87.9%, while those of chest X-ray were 61.3% and 98.5% respectively. The overall accuracy of CT scout film and chest X-ray for detection of rib fractures were 76.3% and 85% respectively. Conclusion: Diagnostic performance of CT scout film for detection of rib fractures was comparable to that of the plain chest radiograph. CT scout film does not provide any additional information or advantage over a plain chest radiograph. In patients with severe thoracic trauma, CT chest remains the modality of choice for accurate delineation of rib fractures and associated internal injuries

Dependencies and Separation of Duty Constraints in GTRBAC

A Generalized Temporal Role Based Access Control (GTRBAC) model that captures an exhaustive set of temporal constraint needs for access control has recently been proposed. GTRBAC’s language constructs allow one to specify various temporal constraints on role, user-role assignments and role-permission assignments. In this paper, we identify various time-constrained cardinality, control flow dependency and separation of duty constraints (SoDs). Such constraints allow specification of dynamically changing access control requirements that are typical in today’s large systems. In addition to allowing specification of time, the constraints introduced here also allow expressing access control policies at a finer granularity. The inclusion of control flow dependency constraints allows defining much stricter dependency requirements that are typical in workflow types of applications

Diagnostic accuracy of digital mammography in the detection of breast cancer

Introduction Breast cancer has a high prevalence in the community and places very high demands on resources. Digital mammography provides a good quality image with reduced radiation dose and can detect breast carcinoma in its earlier stages, resulting in good prognosis and improved patient survival. Objective To calculate the diagnostic accuracy of digital mammography in the detection of breast cancer, using histopathology as a gold standard in women aged over 30 years, who are undergoing mammography for screening and diagnostic purposes. Materials and methods This was a cross-sectional analytical study, conducted in the department of radiology, for a total duration of 10 months. A total of 122 patients of age above 30 years, referred for digital mammography for the evaluation of different symptoms related to breast diseases, followed by biopsy/surgery and histopathology, were included in the study. Result Our data confirmed that digital mammography is a highly accurate tool for breast cancer detection having a sensitivity of 97%, a specificity of 64.5%, a positive predictive value of 89%, and a negative predictive value of 90.9%, with a diagnostic accuracy of 89.3%. Conclusion Considering our results, we recommend that digital mammography should replace screen-film mammography as a basic tool to detect breast cancer for both screening and diagnostic purpose

Cystic artery pseudo-aneurysm: a complication of xanthogranulomatous cholecystitis

A 54-year-old man presented for radiology with pain and a feeling of fullness in the upper abdomen and an epigastric mass. Ultrasound revealed a large cystic mass with internal echoes, lying posterior and inferior to left lobe of the liver. The gallbladder was thick-walled and contracted, and contained a calculus and echogenic sludge. A cystic structure that produced swirling flow signals on colour Doppler was demonstrated within the gallbladder. The CT scan showed a thickened gallbladder with adjacent inflammation and a 2-cm pseudo-aneurysm in its wall. High-density material was present in the gallbladder lumen, in the extra-hepatic bile ducts and around the gastrohepatic ligament. A thick haemorrhagic pus, from which Escherichia coli was cultured, was drained from the gastrohepatic collection. An elective coeliac angiogram demonstrated a solitary pseudo-aneurysm of the medial branch of the cystic artery. Selective catheterisation of this artery with a micro-catheter enabled complete exclusion of the pseudo-aneurysm by a single micro-coil. Histological examination of the gallbladder, which was ultimately removed at open cholecystectomy, demonstrated xanthogranulomatous cholecystitis

Risk of post-pregnancy hypertension in women with a history of hypertensive disorders of pregnancy: nationwide cohort study.

Objectives To determine how soon after delivery the risk of post-pregnancy hypertension increases in women with hypertensive disorders of pregnancy and how the risk evolves over time.Design Nationwide register based cohort study.Setting Denmark.Populations 482 972 primiparous women with a first live birth or stillbirth between 1995 and 2012 (cumulative incidence analyses), and 1 025 118 women with at least one live birth or stillbirth between 1978 and 2012 (Cox regression analyses).Main outcome measures 10 year cumulative incidences of post-pregnancy hypertension requiring treatment with prescription drugs, and hazard ratios estimated using Cox regression.Results Of women with a hypertensive disorder of pregnancy in a first pregnancy in their 20s, 14% developed hypertension in the first decade post partum, compared with 4% of women with normotensive first pregnancies in their 20s. The corresponding percentages for women with a first pregnancy in their 40s were 32% and 11%, respectively. In the year after delivery, women with a hypertensive disorder of pregnancy had 12-fold to 25-fold higher rates of hypertension than did women with a normotensive pregnancy. Rates in women with a hypertensive disorder of pregnancy were threefold to 10-fold higher 1-10 years post partum and remained twice as high even 20 or more years later.Conclusions The risk of hypertension associated with hypertensive disorders of pregnancy is high immediately after an affected pregnancy and persists for more than 20 years. Up to one third of women with a hypertensive disorder of pregnancy may develop hypertension within a decade of an affected pregnancy, indicating that cardiovascular disease prevention in these women should include blood pressure monitoring initiated soon after pregnancy

M3DISEEN: A Novel Machine Learning Approach for Predicting the 3D Printability of Medicines

Artificial intelligence (AI) has the potential to reshape pharmaceutical formulation development through its ability to analyze and continuously monitor large datasets. Fused deposition modeling (FDM) 3-dimensional printing (3DP) has made significant advancements in the field of oral drug delivery with personalized drug-loaded formulations being designed, developed and dispensed for the needs of the patient. However, the optimization of the fabrication parameters is a time-consuming, empirical trial approach, requiring expert knowledge. Here, M3DISEEN, a web-based pharmaceutical software, was developed to accelerate FDM 3D printing, which includes producing filaments by hot melt extrusion (HME), using AI machine learning techniques (MLTs). In total, 614 drug-loaded formulations were designed from a comprehensive list of 145 different pharmaceutical excipients, 3D printed and assessed in-house. To build the predictive tool, a dataset was constructed and models were trained and tested at a ratio of 75:25. Significantly, the AI models predicted key fabrication parameters with accuracies of 76% and 67% for the printability and the filament characteristics, respectively. Furthermore, the AI models predicted the HME and FDM processing temperatures with a mean absolute error of 8.9 °C and 8.3 °C, respectively. Strikingly, the AI models achieved high levels of accuracy by solely inputting the pharmaceutical excipient trade names. Therefore, AI provides an effective holistic modeling technology and software to streamline and advance 3DP as a significant technology within drug development. M3DISEEN is available at (http://m3diseen.com/predictions/)

Machine learning predicts 3D printing performance of over 900 drug delivery systems

Three-dimensional printing (3DP) is a transformative technology that is advancing pharmaceutical research by producing personalized drug products. However, advances made via 3DP have been slow due to the lengthy trial-and-error approach in optimization. Artificial intelligence (AI) is a technology that could revolutionize pharmaceutical 3DP through analyzing large datasets. Herein, literature-mined data for developing AI machine learning (ML) models was used to predict key aspects of the 3DP formulation pipeline and in vitro dissolution properties. A total of 968 formulations were mined and assessed from 114 articles. The ML techniques explored were able to learn and provide accuracies as high as 93% for values in the filament hot melt extrusion process. In addition, ML algorithms were able to use data from the composition of the formulations with additional input features to predict the drug release of 3D printed medicines. The best prediction was obtained by an artificial neural network that was able to predict drug release times of a formulation with a mean error of ±24.29 min. In addition, the most important variables were revealed, which could be leveraged in formulation development. Thus, it was concluded that ML proved to be a suitable approach to modelling the 3D printing workflow

Stencil lithography for bridging MEMS and NEMS

The damage inflicted to silicon nanowires (Si NWs) during the HF vapor etch release poses a challenge to the monolithic integration of Si NWs with higher-order structures, such as microelectromechanical systems (MEMS). This paper reports the development of a stencil lithography-based protection technology that protects Si NWs during prolonged HF vapor release and enables their MEMS integration. Besides, a simplified fabrication flow for the stencil is presented offering ease of patterning of backside features on the nitride membrane. The entire process on Si NW can be performed in a resistless manner. HF vapor etch damage to the Si NWs is characterized, followed by the calibration of the proposed technology steps for Si NW protection. The stencil is fabricated and the developed technology is applied on a Si NW-based multiscale device architecture to protectively coat Si NWs in a localized manner. Protection of Si NW under a prolonged (>3 h) HF vapor etch process has been achieved. Moreover, selective removal of the protection layer around Si NW is demonstrated at the end of the process. The proposed technology also offers access to localized surface modifications on a multiscale device architecture for biological or chemical sensing applications

Effect of Lubricant on Wear Debris Color Diagnosis

Color feature of debris is quite often used for source diagnosis in machine component while performing wear debris analysis (WDA). This feature is commonly observed and analyzed with different offline debris imaging setups. In these setups, its attributes remain consistent in their values as just a colorless medium i.e. air is in between the imaging setup and the observed debris. But in the case of an online debris analysis, the measurements of these attributes can be affected as the imaging has to perform in the presence of a machine lubricant. The lubricant color can affect the measurement of debris original color attributes and further can cause a wrong source diagnosis. In this paper, the effects of lubricant color on debris color measurements are discussed. A debris imaging setup is used for experimentation. Micro size steel debris are analyzed with three different lubricants. The debris color measurements are initially performed in an offline mode when the debris are placed on a glass slide. Later the mentioned measurements are taken in the presence of lubricants when the debris are flowing with the lubricant medium via a flow cell. Finally a comparison is made which concludes that the darker the lubricant the lesser will be chances to deduce the material (color and attributes). Whereas brighter lubricants do not hinder the analysis and identification of material and hence are considered suitable for qualitative wear debris analysis