Background: Patients with chronic kidney
disease have different grades of sensorineural
deafness .
Objective: To study the incidence of
sensorineural hearing loss and possible contributing
factors in patients with chronic kidney disease.
Methods: A total of 100 patients with chronic
kidney disease were studied. All of them were
males. 92 of them were on regular haemodialysis
programme. Only 8 patients were on conservative
management the age range of the study patients was
18-40 year patients were divided into three groups
according to age. All patients were assessed
clinically and were evaluated by audiometry , and
analysis was made on bone conduction threshold
.The mean follow up period was 28 weeks .
Results: 36 patients (36 %) showed sensorineural
hearing loss .The incidence of sensorineural
deafness was found to increase with the advancing
age and duration of chronic kidney disease but not
directly proportional to the number of
haemodialysis sessions .The number of
haemodialysis sessions did not show increase in the
degree of sensorineural deafness .
Conclusion: Patients with chronic kidney disease
have sensorineural deafness of some degree which
should be assessed and evaluated to halt its
progression.
Three new polyphosphates were synthesized in good yields by reacting diethylenetriamine with the appropriate phosphate ester in ethanol under acidic conditions. The polyphosphate structures were determined using FT-IR and 1H-NMR spectroscopies, and their elemental compositions were confirmed by EDX spectroscopy. Polyphosphates were added to poly(vinyl chloride) (PVC) at low concentrations to fabricate thin films. The PVC films were irradiated with ultraviolet light for long periods, and the effect of polyphosphates as the photostabilizer was investigated by determining changes in the infrared spectra (intensity of specific functional group peaks), reduction in molecular weight, weight loss, and surface morphology. Minimal changes we
... Show MoreImage segmentation using bi-level thresholds works well for straightforward scenarios; however, dealing with complex images that contain multiple objects or colors presents considerable computational difficulties. Multi-level thresholding is crucial for these situations, but it also introduces a challenging optimization problem. This paper presents an improved Reptile Search Algorithm (RSA) that includes a Gbest operator to enhance its performance. The proposed method determines optimal threshold values for both grayscale and color images, utilizing entropy-based objective functions derived from the Otsu and Kapur techniques. Experiments were carried out on 16 benchmark images, which inclu
In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compare
... Show MoreMost reinforced concrete (RC) structures are constructed with square/rectangular columns. The cross-section size of these types of columns is much larger than the thickness of their partitions. Therefore, parts of these columns are protruded out of the partitions. The emergence of columns edges out of the walls has some disadvantages. This limitation is difficult to be overcome with square or rectangular columns. To solve this problem, new types of RC columns called specially shaped reinforced concrete (SSRC) columns have been used as hidden columns. Besides, the use of SSRC columns provides many structural and architectural advantages as compared with rectangular columns. Therefore, this study was conducted to explain the structura
... Show MoreThis study includes synthesis of some nitrogenous heterocyclic compounds linked to amino acid esters or heterocyclic amines that may have a potential activity as antimicrobial and/or cytotoxic. Quinolines are an important group of organic compounds that possess useful biological activity as antibacterial, antifungal and antitumor .8-Hydroxyquinoline (8-HQ) and numerous of its derivatives exhibit potent activities against fungi and bacteria which make them good candidates for the treatment of many parasitic and microbial infection diseases.
These pharmacological properties of quinolones aroused our interest in synthesizing several new compounds featuring heterocyclic rings of the quinoline derivatives linke
... Show MoreThis research study experimentally the effect of air flow rate on humidification process
parameters. Experimental data are obtained from air conditioning study unit T110D. Results obtained
from experimental test, calculations and psychometrics software are discussed. The effect of air flow rate
on steam humidification process parameters as a part of air-conditioning processes can be explained
according to obtained results. Results of the steam humidification processes (1,2) with and without
preheating with 5A and 7.5A shows decreasing in dry bulb temperature, humidity ratio, and heat add to
moist air with increasing air flow rate, but humidification load, and total energy of moist air increase with
increasing air flo
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for