Publications by authors named "Mohannad N AbuHaweeleh"

Adult-onset Still's disease (AOSD) is a rare systemic inflammatory condition with hallmark features of spiking fevers, arthritis, and a salmon-colored maculopapular rash. It typically affects young adults, with a bimodal age distribution of 15-25 and 36-46 years. The prevalence of AOSD ranges from 1 to 34 cases per million people, with an incidence rate of 0.

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There are two types of criteria for diagnosing gestational diabetes mellitus (GDM). The first is based on measurement of three values on the glucose tolerance test (GTT) and making a diagnosis when any value is abnormal (individual time-point criterion). The second is based on creating a weighted average of the three values and using the average to split glycemic status into normal gestational glycemia (NGG), impaired gestational glycemia (IGG), gestational diabetes (GDM), or high-risk gestational diabetes (hGDM) (unified criterion).

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Inner speech recognition (ISR) is an emerging field with significant potential for applications in brain-computer interfaces (BCIs) and assistive technologies. This review focuses on the critical role of machine learning (ML) in decoding inner speech, exploring how various ML techniques improve the analysis and classification of neural signals. We analyze both traditional methods such as support vector machines (SVMs) and random forests, as well as advanced deep learning approaches like convolutional neural networks (CNNs), which are particularly effective at capturing the dynamic and non-linear patterns of inner speech-related brain activity.

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SKP2, an E3 ubiquitin ligase component of the SCF complex, plays a critical role in cell cycle regulation by targeting key inhibitors like p27, p21, and p57 for degradation, thereby promoting G1-S transition. Its overexpression is strongly associated with urological malignancies, including prostate, bladder, and kidney cancers, where it correlates with aggressive disease and poor prognosis. SKP2 drives tumor progression, via enhancing cancer cell proliferation, invasion, and metastasis.

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Introduction: Ovarian serous cystadenocarcinoma (SCA), a deadly gynecologic cancer, often goes undetected until the late stages. Tissue proteomics unveils disease heterogeneity, enhancing tumor classification and enabling personalized treatments tailored to individual expression profiles.

Material And Methods: Tissue samples from 46 serous ovarian tumors were quantified using label-free liquid chromatography-tandem mass spectrometry.

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Background: Regular physical activity (PA) has beneficial health effects in controlling and managing diabetes. Identifying key factors associated with poor adherence to PA recommendations among patients with type 2 diabetes (T2D) has significant implications for future targeted interventions aimed at improving adherence and health outcomes in this population. The present study aims to determine the level of adherence to PA recommendations and the associated factors among adults with T2D in Qatar.

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: Artificial intelligence has made significant strides in healthcare, contributing to diagnosing, treating, monitoring, preventing, and testing various diseases. Despite its broad adoption, clinical consensus on AI's role in infection control remains uncertain. This scoping review aims to understand the characteristics of AI applications in bacterial infection control.

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Background: Diabetes mellitus (DM) prevalence in Qatar is among the highest worldwide. DM has been shown to be associated with reduced performance on numerous domains of cognitive function in elderly population. Here, we sought to determine whether such association also exists in a middle-aged cohort.

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: Diabetes is linked to a higher risk of urinary tract infections (UTIs) in women, often leading to recurrent antibiotic treatments. Frequent antibiotic use for UTIs can contribute to antimicrobial resistance (AMR), a critical public health threat that increases treatment failure. This study investigated the prevalence of AMR and its associated factors among women with UTIs, comparing those with and without diabetes.

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Article Synopsis
  • Small cell lung cancer (SCLC) is a highly aggressive cancer with poor survival rates, and current diagnostic methods are invasive and limited.
  • This study introduces a new machine learning technique that uses metabolomics data to distinguish between SCLC, non-small cell lung cancer (NSCLC), and healthy individuals, achieving high accuracy in classification.
  • Key metabolites were identified as important predictors, and the stacking ensemble model effectively combines different classifiers, providing a promising non-invasive alternative for early lung cancer detection.
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Small cell lung cancer (SCLC), a neuroendocrine aggressive subtype of lung cancer, is associated with paraneoplastic disorders in about 9% of patients. In this report, we describe a middle-aged man who presented with chronic bowel obstruction caused by chronic intestinal pseudo-obstruction (CIPO) due to SCLC.

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Brachydactyly is a genetic condition leading to shortened or absent digits in hands or feet. It can occur independently or as part of syndromes. This case focuses on Brachydactyly type B, the rarest form.

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The accurate diagnosis of small-cell lung cancer (SCLC) is crucial, as treatment strategies differ from those of other lung cancers. This systematic review aims to identify proteins differentially expressed in SCLC compared to normal lung tissue, evaluating their potential utility in diagnosing and prognosing the disease. Additionally, the study identifies proteins differentially expressed between SCLC and large cell neuroendocrine carcinoma (LCNEC), aiming to discover biomarkers distinguishing between these two subtypes of neuroendocrine lung cancers.

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