Article information: COVID-19 has roused the scientic community, prompting calls for immediate solutions to avoid the infection or at least reduce the virus's spread. Despite the availability of several licensed vaccinations to boost human immunity against the disease, various mutated strains of the virus continue to emerge, posing a danger to the vaccine's ecacy against new mutations. As a result, the importance of the early detection of COVID-19 infection becomes evident. Cough is a prevalent symptom in all COVID-19 mutations. Unfortunately, coughing can be a symptom of various of diseases, including pneumonia and inuenza. Thus, identifying the coughing behavior might help clinicians diagnose the COVID-19 infection earlier and distinguish coronavirus-induced from non-coronavirus-induced coughs. From this perspective, this research proposes a novel approach for diagnosing COVID-19 infection based on cough sound. The main contributions of this study are the encoding of cough behavior, the investigation of its unique characteristics, and the representation of these traits as association rules. These rules are generated and distinguished with the help of data mining and machine learning techniques. Experiments on the Virufy COVID-19 open cough dataset reveal that cough encoding can provide the desired accuracy (100%).
Trichomoniasis is a parasitic disease caused by the protozoan Trichomonas vaginalis. It is the most common sexually transmitted protozoal infection. There is no estimated of infection intensity among reproductive-age females. Further studies of the infection intensity of trichomoniasis and other vaginal infection will highlight the importance of this pathogen as a public health problem. A total of 614 females from Baghdad city were screened for T. vaginalis from March 2015 to September 2015. Females aged 13–61 years old provided vaginal swab specimens. The vaginal fluids extracted from these swabs were checked for the presence of T. vaginalis and other vaginal infection using microscopic examination. Overall, 525 (85.5%) of 614 was scr
... Show MoreBackground: Deep vein thrombosis is a multi causal disease and its one of most common venous disorder, but only one quarter of the patients who have signs and symptoms of a clot in the vein actually have thrombosis and need treatment .The disease can be difficult to diagnose. Venous ultrasound in combination with clinical finding is accurate for venous thromboembolism, its costly because a large number of patients with suspicious signs and symptoms. Venography still the gold standard for venous thromboembolism but it is invasive. The D-dimer increasingly is being seen as valuable tool rolling out venous thromboembolism and sparing low risk patients for further workup.Objectives: this study has designed the role of D-dimer to confirm diag
... Show MoreFourty -tow Libyan patients with hydatidosis, which were
referred to by the physician for the detection of hydatid cyst by X - rays, Ultrasound and CT-Scan. The infection rate in females and males was(69% )and (31% )respectively .The highest rate 69% was in the liver, followed by the lung( 23.8%), the brain (4.8%) and kidney
(2.4%).
A total of 42 serum samples were gathered from Libyan patients infected with hydatidosis, 33 serum samples from patients cases with other parasitic diseases than hydatidosis and 30 serum samples from healthy normal controls and were tested by Dot-ELIZA utilizing antigen B from sheep hy
... Show MoreThe detection of diseases affecting plant is very important as it relates to the issue of food security, which is a very serious threat to human life. The system of diagnosis of diseases involves a series of steps starting with the acquisition of images through the pre-processing, segmentation and then features extraction that is our subject finally the process of classification. Features extraction is a very important process in any diagnostic system where we can compare this stage to the spine in this type of system. It is known that the reason behind this great importance of this stage is that the process of extracting features greatly affects the work and accuracy of classification. Proper selection of
... Show MoreBackground: Breast cancer is the first one among Iraqi females. Most of them present later for diagnosis. Early detection center in tertiary hospital practice uses FNAB for early diagnosis. Publications on accuracy of this detection are scarce.
Objective: To test the accuracy of FNAB in breast lump diagnosis.
Methods: Diagnostic test accuracy study, on 204 women with breast lump, attending the oncology department in 2017.
Results: Fine-needle aspiration biopsy diagnosis of histologically malignant cases were, malignant in 89 (87.3%), suspicious of malignancy in 5 (4.9%), and benign in 4 (3.9%). Complete sensitivity was 87.3%, and specificity
... Show MoreLeishmaniasis is a transmissible infection brought about by an obligatory intracellular protozoan from the genus Leishmania. It occurs worldwide in tropical and subtropical regions and can be burdensome in resource-constrained countries. The infection ranges in severity from mild cutaneous lesions to more severe and sometimes life-threatening visceral and distorting mucocutaneous sicknesses. Importantly, cutaneous leishmaniasis (CL) is prevalent in the Middle East with a pooled prevalence of 12%. It imposes a significant health and socioeconomic burden
Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration
... Show MoreDiabetes mellitus (DM) is a chronic metabolic disease that is considered a major worldwide healthcare problem. Multiple studies have revealed that people with DM are more likely to acquire oral problems, such as periodontal diseases, because the oral microbiota plays a major role in oral health and may affect the saliva composition. This study aimed to characterize the oral microbiota of a sample of DM patients and its association with some demographic factors, such as smoking habits and gender. A total of 91 specimens, including 51 DM patients and 40 apparently healthy individuals, were enrolled in this study, which was carried out from November 2021 to February 2022. Whole saliva was collected in a sterile tube, and oral swabs
... Show MoreBackground: It is well known that mycotic antigens have an important
role in atopy and the induction of asthma. Now one of the important
subjects is the relation between respiratory bacterial and viral
infections in the inflammatory reactions accompanied with bronchial
asthma viruses Bacteria or their metabolites act as trigger for asthma
or increase it's intensity .
Objectives: To show the relation between asthma and some viral
infections serologically.
Methods: Direct ELISA test was employed to detect lgG specific for
Respiratory Syncytial virus (Rsv) parainfluenza virus type (p13) and
influenza virus in sera of (100) asthmatic patients of two age groups.
(10-17) and(18-50) years old. Serum samples from
The aim of this study was to investigate the correlation between GRIN2A rs387906637 polymorphism and susceptibility to epilepsy. Blood samples were collected from 85 volunteers, dividing into 60 epilepsy patients (34 males and 26 females) and 25 healthy subjects (19 males and 6 females).The DNA was extracted and GRIN2A rs387906637 polymorphism was analyzed by Real-time PCR using two probes and primers. The results showed no significant differences between patients and control samples; therefore, there are no allelic and genotypic correlations of this SNP with epilepsy. This study indicated that GRIN2A rs387906637 polymorphism is not a risk factor for epilepsy in the studied set of patients.