Eccrine spiradenoma: A rare adnexal tumour with atypical presentation: A case report

First described in 1934, eccrine spiradenoma (ES) is a rare, benign adnexal tumour arising from eccrine sweat glands. It commonly presents as a slow-growing nodule on the upper trunk, and head and neck region, mostly in the age bracket of 15-35 years, with no gender preference. While no established guidelines exist for optimal management of malignant ES, some therapies have been studied. The diagnosis of this entity is extremely important as it can harbour a malignant component with disastrous outcomes which may be missed due to its strong resemblance to benign lesions, such as a papilloma. Here, we present the case of a 35-year-old lady who presented with a papilloma-like growth on the upper medial aspect of the thigh which was diagnosed as eccrine spiradenoma upon excision

Assessment of WT1 expression as a marker of treatment outcome in karyotype normal acute myeloid leukemia patients in Pakistan

Currently, there is an effort to predict relapse by follow-up monitoring of MRD and subsequently to begin the treatment of the patients during their clinical and hematological remission prior to overt hematological relapse. Expression of WT1 in AM Lis known to be independently associated with significant inferior response to therapy and short survival outcome. Follow-up monitoring of WT1 gene expression during or after therapy would be a valuable predictive marker for early recurrence or relapse of AMLdisease. This pilot study evaluated newly diagnosed and post-induction or consolidation chemotherapy of AMLpatients who were registered with the Oncology Clinics of the Aga Khan University Hospital, Karachi. High WT1 burden (\u3e 5000 copies/ml) in 2 patients was indicative of early recurrence of the disease along with shorter disease-free and overall survival. Low WT1 expression (\u3c 200 copies/ml) in 2 patients after induction and consolidation therapy, respectively, was suggestive of better prognosis

Multi-Person Tracking Based on Faster R-CNN and Deep Appearance Features

Mostly computer vision problems related to crowd analytics are highly dependent upon multi-object tracking (MOT) systems. There are two major steps involved in the design of MOT system: object detection and association. In the first step, desired objects are detected in every frame of video stream. Detection quality directly influences the performance of tracking. The second step involves the correspondence of detected objects in current frame with the previous to obtain their trajectories. High accuracy in object detection system results in less number of missing detection and finally produces less fragmented tracks. Better object association increases the affinity between objects in different frames. This paper presents a novel algorithm for improved object detection followed by enhanced object tracking. Object detection accuracy has been increased by employing deep learning-based Faster region convolutional neural network (Faster R-CNN) algorithm. Object association is carried out by using appearance and improved motion features. Evaluation results show that we have enhanced the performance of current state-of-the-art work by reducing identity switches and fragmentation

Increasing Employee Organizational Commitment by Correlating Goal Setting, Employee Engagement and Optimism at Workplace

The aim of this study is to explore the link among important factors with effect organizational commitment. Secondly this study focuses to make a positive significant relation in setting of goals, engagement of employee and optimistic approach of behavior at work place environment in order to enhance organizational commitment level of employee. The data for that hypothesized model will be collected form individual belongs to from different organizations and research institutions. The results of this study will contribute new improved ways to achieve maximum level of organizational commitment from employees. This study will provide new insight for the field of performance management. Key words: Organizational commitment, Employee engagement, Work place optimism, Enhancing performance, Goal Settin

Bilateral gonadoblastoma with dysgerminoma in a phenotypically normal female with 46XX karyotype: Report of a rare case and literature review

Gonadoblastoma is a rare ovarian neoplasm which belongs to germ cell-sex cord-stromal tumor category. This tumor is frequently associated with invasive germ cell malignancy. It commonly arises in dysgenetic gonads of young individuals who are phenotypically females but possess 46XY karyotype. It has been rarely reported in females with normal phenotype and genotype. We herein describe a case of 10-year-old female who presented with abdominal pain, abdominal distention and fever. CT scan of the abdomen and pelvis revealed bilateral ovarian masses, ascites and pelvic and para-aortic lymphadenopathy. Serum lactate dehydrogenase levels were also elevated. She underwent left salpingo-oophorectomy, right ovarian biopsy, omentectomy and para-aortic lymphadenopathy. Microscopically, tumor showed in situ and invasive components. In situ component was arranged in nests and lobules formed by immature sertoli cells forming acini and encircling large polygonal primitive germ cells. Immature sertoli cells were positive for immunohitochemical (IHC) stains cytokeratin AE1/AE3, inhibin and calretinin, while germ cells were positive for SALL4, Oct 3/4, placental alkaline phosphatase (PLAP) and CD117. Invasive component was arranged in sheets of large-sized, polygonal-shaped primitive germ cells which were also positive for SALL4, Oct 3/4, PLAP and CD117 IHC stains. Hence, the diagnosis of gonadoblastoma with dysgerminoma was made. The tumor was limited to both ovaries. Cytogenetic analysis of peripheral blood revealed normal female 46XX karyotype. The patient received two cycles of adjuvant chemotherapy and was then lost to follow-up. We conclude that gonadoblastoma, although rare, should be considered as a differential diagnosis in ovarian tumors of young females. Invasive germ cell component should always be carefully searched for as it guides about treatment and predicts prognosis

Giant juvenile fibroadenoma of the breast in a 13-year-old Pakistani girl with excellent cosmetic outcome after subareolar enucleation - A case report

Introduction: Fibroadenoma is the most common benign lesion of breast in young women, characterized by an aberrant proliferation of both epithelial and mesenchymal elements. It is termed giant fibroadenoma when it is larger than 5 cm or weighs more than 500 g with an incidence of 0.5-2% of all fibroadenomas.Presentation of case: In this report, we discuss a case of a 13-year-old Pakistani girl who presented with a giant juvenile fibroadenoma in left breast and was treated by a subareolar lump excision through a periareolar incision with excellent cosmetic outcome. To the best of our literature search, this is the first case of giant juvenile fibroadenoma in an adolescent being reported from Pakistan.Discussion: Surgical management of giant juvenile fibroadenoma in immature breast is challenging as it may either result in asymmetric defect or damage to developing breast tissue resulting in long term poor outcomes. Surgical decision should be carefully undertaken and reported for future reference in such cases.Conclusion: The diagnosis and management of giant juvenile fibroadenoma can be challenging because these tumors clinically and histologically mimic phyllodes tumor due to their rapid growth and large size. Excision through a periareolar approach for fibroadenomas located in subareolar region provides good cosmetic results in these patients with minimal scar visibility

Therapeutic Targets and Signaling Pathways for Diagnosis of Myeloma

Multiple myeloma (MM) is a malignancy of plasma cells that not only shows different clinical behavior but also depicts heterogeneous groups at molecular level. The prognosis of the disease has been dramatically changed with the arrival of new drugs in the past few years. In this context of better therapeutic agents, there are important challenges for accurate evaluation of patients by better prognostic and predictive tools. Transcriptomic studies have largely added to decipher MM heterogeneity, dividing MM patients into different subgroups according to prognosis. Micro-arrays and more recently RNA sequencing have helped in evaluating coding and non-coding genes, mutations, unique transcriptome convertors and different splicing events giving new information concerning biology, outcome and treatment options. Initial data from gene expression profiling studies have also pointed out genes that predict prognosis, i.e., CSK1-B, and can deliver pharmacogenomics and biologic vision into the pathophysiology, targeted treatment, and future direction. Importantly, we suggest that all prospective studies and clinical trials now accept genetic testing and risk stratification of MM patients. In this review, we discuss the part and effect of gene expression profiling in myeloma

Enhancing wettability prediction in the presence of organics for hydrogen geo-storage through data-driven machine learning modeling of rock/H2/brine systems

The success of geological H2 storage relies significantly on rock–H2–brine interactions and wettability. Experimentally assessing the H2 wettability of storage/caprocks as a function of thermos-physical conditions is arduous because of high H2 reactivity and embrittlement damages. Data-driven machine learning (ML) modeling predictions of rock–H2–brine wettability are less strenuous and more precise. They can be conducted at geo-storage conditions that are impossible or hazardous to attain in the laboratory. Thus, ML models were utilized in this research to accurately model the wettability behavior of a ternary system consisting of H2, rock minerals (quartz and mica), and brine at different operating geological conditions. The results revealed that the ML models accurately captured the wettability behavior at different geo-storage conditions by yielding less than 5% mean absolute percent error and above 0.95 coefficient of determination values. The partial dependency or sensitivity plots were generated to evaluate the impact of individual features on the trained models. These plots revealed that the models accurately captured the physics behind the problem. Furthermore, a mathematical equation is derived from the trained ML model to predict the wettability behavior without using any ML software. The accuracy of the predictions of the ML model can be beneficial for exactly predicting the H2 geo-storage capacities and assessing of H2 containment security of storage and caprocks for large-scale geo-storage projects