Pneumothorax secondary to acupuncture in an adolescent girl

Although acupuncture is a form of traditional Chinese medicine that is gaining popularity and usage in children, the relevant complications are rarely reported. We present a case of 17-year-old girl who underwent acupuncture. The girl visited the emergency department with chest pain and dyspnea on the day after receiving the acupuncture. A chest radiograph showed a left-sided pneumothorax. She was hospitalized for observation and supplemental oxygen, and improvement was noted on follow-up radiograph. She continued to follow up in the outpatient clinic until complete resolution of the pneumothorax. Raising awareness of the potential complications of acupuncture is crucial to aid in patient education and establishment of safety guidelines

Early screening for post-stroke depression, and the effect on functional outcomes, quality of life and mortality: a protocol for a systematic review and meta-analysis

INTRODUCTION: Post-stroke depression (PSD) is a severe complication of cerebrovascular stroke affecting about one-third of stroke survivors. Moreover, PSD is associated with functional recovery and quality of life (QOL) in stroke survivors. Screening for PSD is recommended. There are, however, differences in the literature on the impact of early screening on functional outcomes. In this systematic review, we synthesise the currently available literature regarding the associations between timing and setting of PSD screening and mortality, QOL and functional outcomes in stroke survivors. METHODS AND ANALYSIS: We will systematically search electronic databases including PubMed, Embase, APA PsycINFO, Web of Science, Scopus and CINAHL from inception to August 2021. Four reviewers will screen the title and abstract and full-text level records identified in the search in a blinded fashion to determine the study eligibility. Any selection disagreements between the reviewers will be resolved by the study investigator. Data extraction of eligible studies will be conducted by two reviewers using a predefined template. We will complete the quality assessment of included articles independently by two reviewers using the Newcastle Ottawa Scale. Eventual discrepancies will be resolved by the principal investigator. ETHICS AND DISSEMINATION: Due to the nature of the study design, ethical approval is not required. The systematic review and meta-analysis findings will be published and disseminated in a peer-reviewed journal. Our results will also be disseminated through posters and presentations at appropriate scientific conferences. PROSPERO REGISTRATION NUMBER: CRD42021235993

Diagnostic impact of emergency ultrasound for cholecystitis

Background: Previous studies reported different rates of accuracy considering the use of POCUS in diagnosis of cholecystitis indicating that POCUS is not enough when deciding the management. The aim of this study is to compare POCUS findings in the diagnosing of the acute cholecystitis performed by both emergency medicine residents and radiologists. Methods: A retrospective chart review was conducted in the ED of KAMC, Riyadh, Saudi Arabia. The population consisted of patients presented to the ER with RUQ (right upper quadrant pain) and had an abdominal ultrasound performed in the past 6 year. The study used BEST Care system to access the data of patients who underwent ultrasonography, since January 2016 using a data collection sheet. Results: Our results included 1871 patients admitted in KAMC. Emergency physicians had success rate of 25% in correctly reporting the presence or absence of wall thickening, and 44.1% for pericholecystic fluid. In addition, scanning by emergency physicians has a success rate of 60% in detecting CBD dilatation, 12.7% for Murphy’s sign, and 46.3% for acute cholecystitis. Moreover, we found that the presence of thickened gallbladder wall in the ultrasonographic finding is a significant predictor for cholecystitis as it is increasing its incidence by 2.09 times (P=0.038). Conclusions: Ultrasonography conducted by emergency department could be useful in detecting characteristics of gallbladder however, in our study, it showed low accuracy in detecting gallstones and acute cholecystitis compared with surgical and pathological finding

Intraventricular Hemorrhage in Preterm Infants, Review Article

Intraventricular hemorrhage (IVH) or germinal matrix (GM) in other words, is a condition that can occur in premature births and can lead to long-term medical and developmental effects. While GM/IVH can happen in full-term infants, the hemorrhage in this group of infants is different from periventricular hemorrhage (PVH)/IVH in premature infants. Family members and caregivers of preterm infants and those at risk of preterm birth are confronted with two significant uncertainties concerning these newborns: Is the survival of this child likely? Will the child experience long-term sequelae, particularly developmental sequelae, if they survive? The significance of these questions lies in their potential to impact future medical decisions, including the level of intensity in the care provided. Infants born prematurely can suffer from various acquired lesions in the central nervous system (CNS), leading to long-term disability. These lesions include GM/IVH, periventricular white matter injury, hemorrhage, and diffuse injury to the developing brain. GM/IVH continues to be a major contributor to both illness and death in premature newborns.  GM/IVH is primarily diagnosed by brain imaging techniques, typically cranial ultrasonography, as depicted below. Screening and serial examinations are essential for diagnosing GM/IVH, as it can occur without any noticeable clinical indications

The role of transformational leadership in influencing students’ outcomes in public secondary schools in Kuwait

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.This study investigates the role of transformational leadership in influencing students’ outcomes in public secondary schools using Kuwait as a case study. The standard of academic achievement in Kuwait’s public schools has been declining over the years, which calls for a different type of leadership to transform these schools. It is argued in this thesis that there is merit in bringing in private sector business models to the public education sector in order to transform the sector and improve the schools’ outcomes. Furthermore, not much research has been undertaken on the paths through which transformational leadership influences public school outcomes in developing countries such as Kuwait. Following a critical review of leadership literature, a theoretical model for leadership that is transformational was conceptualised and this formed the basis of hypotheses formation and data collection. The thesis is thus original in its attempt to understand the paths through which school heads’ transformational leadership influence student’s outcomes in public secondary schools in a developing country (Kuwait). The study adopted a positivist ontology and objective epistemology and obtained data from 495 school heads and staff from 86 public secondary schools in Kuwait via a structured questionnaire. Confirmatory factor analysis (CFA) and structural equation models (SEM) tested the direct and indirect effects of school heads’ transformational leadership in influencing students’ outcomes the student “achievement” and the student “engagement” via several mediating variables including “school culture”, “class room condition” and “academic emphasis”. The analysis identified idealized influence (attributes) and idealized influence (behaviour) as the underlying dimensions of transformational leadership that directly and indirectly influences both student engagement and student achievement as the final outcome. The findings also confirmed differences between males and females in their leadership styles and subsequent influence on students’ achievement, and student engagement with the latter appearing to be better school heads. Therefore, two structure equation models were built to investigate the characteristics of each gender leadership style on the outcome variables. The findings also revealed that males’ leadership style has significant effect on student achievement but not student on engagement, while female leader ship style has significant effect on both student achievement and student engagement stronger than the males’ effect counterpart. Generally however, transformational leadership style has significant effect on both student achievement and student engagement. The study objectives were met and the study contributes to understanding the role of transformational leadership and its influence on staff and students’ achievement, from a developing country in the GCC. Managerial recommendations and suggestions for policy makers are made. Study limitations are highlighted leading to suggestions for further study.Brunel University Londo

Feature extraction and clustering techniques for digital image forensics.

This thesis proposes an adaptive algorithm which applies feature extraction and clustering techniques for cloning detection and localization in digital images. Multiple contributions have been made to test the performance of different feature detectors for forensic use. The �first contribution is to improve a previously published algorithm by Wang et al. by localizing tampered regions using the grey-level co-occurrence matrix (GLCM) for extracting texture features from the chromatic component of an image (Cb or Cr component). The main trade-off� is a diminishing detection accuracy as the region size decreases. The second contribution is based on extracting Maximally Stable Extremal Regions (MSER) features for cloning detection, followed by k-means clustering for cloning localization. Then, for comparison purposes, we implement the same approach using Speeded Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT). Experimental results show that we can detect and localize cloning in tampered images with an accuracy reaching 97% using MSER features. The usability and effi�cacy of our approach is verified by comparing with recent state-of-the-art approaches. For the third contribution we propose a flexible methodology for detecting cloning in images, based on the use of feature detectors. We determine whether a particular match is the result of a cloning event by clustering the matches using k-means clustering and using a Support Vector Machine (SVM) to classify the clusters. This descriptor-agnostic approach allows us to combine the results of multiple feature descriptors, increasing the potential number of keypoints in the cloned region. Results using MSER, SURF and SIFT outperform state of the art where the highest true positive rate is achieved at approximately 99.60% and the false positive rate is achieved at 1.6%, when different descriptors are combined. A statistical �filtering step, based on computing the median value of the dissimilarity matrix, is also proposed. Moreover, our algorithm uses an adaptive technique for selecting the optimal k value for each image independently, allowing our method to detect multiple cloned regions. Finally, we propose an adaptive technique that chooses feature detectors based on the type of image being tested. Some detectors are robust in detecting features in textured images while other detectors are robust in detecting features in smooth images. Combining the detectors makes them complementary to each other and can generate optimal results. The highest value for the area under ROC curve is achieved at approximately 98.87%. We also test the performance of agglomerative hierarchical clustering for cloning localization. Hierarchical and k-means clustering techniques have a similar performance for cloning localization. The True Positive Rate (TPR) for match level localization is achieved at approximately 97.59% and 96.43% for k-means and hierarchical clustering techniques, respectively. The robustness of our technique is analyzed against additive white Gaussian noise and JPEG compression. Our technique is still reliable even when using a low signal-to-noise (SNR = 20 dB) or a low JPEG compression quality factor (QF = 50)

Feature extraction and clustering techniques for digital image forensics.

This thesis proposes an adaptive algorithm which applies feature extraction and clustering techniques for cloning detection and localization in digital images. Multiple contributions have been made to test the performance of different feature detectors for forensic use. The first contribution is to improve a previously published algorithm by Wang et al. by localizing tampered regions using the grey-level co-occurrence matrix (GLCM) for extracting texture features from the chromatic component of an image (Cb or Cr component). The main trade-off is a diminishing detection accuracy as the region size decreases. The second contribution is based on extracting Maximally Stable Extremal Regions (MSER) features for cloning detection, followed by k-means clustering for cloning localization. Then, for comparison purposes, we implement the same approach using Speeded Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT). Experimental results show that we can detect and localize cloning in tampered images with an accuracy reaching 97% using MSER features. The usability and efficacy of our approach is verified by comparing with recent state-of-the-art approaches. For the third contribution we propose a flexible methodology for detecting cloning in images, based on the use of feature detectors. We determine whether a particular match is the result of a cloning event by clustering the matches using k-means clustering and using a Support Vector Machine (SVM) to classify the clusters. This descriptor-agnostic approach allows us to combine the results of multiple feature descriptors, increasing the potential number of keypoints in the cloned region. Results using MSER, SURF and SIFT outperform state of the art where the highest true positive rate is achieved at approximately 99.60% and the false positive rate is achieved at 1.6%, when different descriptors are combined. A statistical filtering step, based on computing the median value of the dissimilarity matrix, is also proposed. Moreover, our algorithm uses an adaptive technique for selecting the optimal k value for each image independently, allowing our method to detect multiple cloned regions. Finally, we propose an adaptive technique that chooses feature detectors based on the type of image being tested. Some detectors are robust in detecting features in textured images while other detectors are robust in detecting features in smooth images. Combining the detectors makes them complementary to each other and can generate optimal results. The highest value for the area under ROC curve is achieved at approximately 98.87%. We also test the performance of agglomerative hierarchical clustering for cloning localization. Hierarchical and k-means clustering techniques have a similar performance for cloning localization. The True Positive Rate (TPR) for match level localization is achieved at approximately 97.59% and 96.43% for k-means and hierarchical clustering techniques, respectively. The robustness of our technique is analyzed against additive white Gaussian noise and JPEG compression. Our technique is still reliable even when using a low signal-to-noise (SNR = 20 dB) or a low JPEG compression quality factor (QF = 50)