FSPBO-DQN: SeGAN based segmentation and Fractional Student Psychology Optimization enabled Deep Q Network for skin cancer detection in IoT applications.

Skin cancer is one of the dangerous types of cancer and the rate of death is increasing due to the lack of knowledge in prevention and the symptoms. It is a common cancer type around the world and it occurs when the skin cells are damaged. Hence, the detection of skin cancer near the beginning is important to prevent the spread of cancer and to increase the survival rate. Recently, image processing and machine learning techniques gained more interest in medical applications. However, early analysis of skin cancer images is very challenging due to factors, like variations in the color illumination, light reflections from the skin surface, and different sizes and shapes of lesions. To detect skin cancer at an early stage and to increase the survival rate, an effective skin cancer detection method is introduced in this study using the proposed Fractional Student Psychology Based Optimization-based Deep Q Network (FSPBO-based DQN) in the wireless network scenario. At first, the nodes simulated in the network area are allowed to capture the healthcare information to make the detection strategy using the proposed method. Then, the routing is performed by the proposed Fractional Student Psychology Based Optimization (FSPBO) algorithm by considering the fitness parameters, like distance, energy, trust, and delay. After the images (healthcare information) are reached the Base Station (BS), the pre-processing, segmentation, and cancer detection processes are carried out to detect the skin lesions. Initially, the image is fed to pre-processing phase, where a Type II Fuzzy System and cuckoo search optimization algorithm (T2FCS) filter is employed to remove the noise of images. Then, the pre-processed images are fed to the segmentation phase, where speech enhancement Generative Adversarial Network (SeGAN) is used to generate the segmented results. Afterward, the Deep Q Network (DQN) detects the skin cancer based on the segmented results, and the training of DQN is made using the proposed FSPBO algorithm, which is designed by integrating the Student Psychology Based Optimization (SPBO) and Fractional Calculus (FC). The proposed method is more robust and reduces computation time and complexity. Moreover, the proposed method achieved higher performance by considering the measures, namely accuracy, sensitivity, and specificity with the values of 92.364%, 93.20%, and 92.63%.

Acute toxicity studies and protective effects of Cinnamon cassia bark extract in streptozotocin-induced diabetic rats.

The medicinal properties of Cinnamon cassia (C. cassia) bark have been reported for their clinical importance for many diseases including diabetes. However, there is no clear evidence so far regarding dose selection for its hepato- and nephroprotective effect in diabetic condition. Hence, the present study aims at evaluating in vitro antioxidant activity, the acute toxicity, and dose fixation of C. cassia bark for their effective medicinal values in streptozotocin (STZ)-induced rats. All the extracts exhibited potential in vitro antioxidant activity and showed a dose-dependent (1000, 2000, 3000, 4000, and 5000 mg/kg BW) acute toxicity by in vivo model. The levels of aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), urea, and creatinine showed a significant elevation in animals treated with the highest dose. In further studies along with histopathological studies, animals treated with STZ (60 mg/kg BW) followed by a different dose (300, 400, and 500 mg/kg BW) of ethanolic extract of the C. cassia bark and glibenclamide (3 mg/kg BW) revealed that the altered level of mitochondrial enzymes, hepatic, and renal marker in STZ-induced animals were restored in C. cassia bark extract-treated group as of control. These results could be of scientific support for the use of the ethanolic extract of the C. cassia bark in folk medicine for the management of diabetes and its associated complications.

Adsorptive removal of nickel and lead ions from aqueous solutions using phosphorylated tamarind nut carbon.

The adsorption of Ni(II) and Pb(II) from aqueous solutions by Phosphorylated Tamarind Nut Carbon (PTNC) was investigated. The effects of contact time, pH and carbon dose were studied in batch experiments at 30 degrees C. The equilibrium process was determined by Freundlich and Langmuir isotherm models. The kinetic studies showed good correlation for a pseudo-second-order kinetic model. Column studies were conducted in 2.5cm diameter columns. The applicability of PTNC for wastewaters containing Ni(II) and Pb(II) was evaluated. The mechanism of adsorption of metals on PTNC was found to follow ion exchange process predominantly and was supported by FTIR. The metal removal was also confirmed by SEM studies.

Urinary pathogens' resistance to common antibiotics: a retrospective analysis.

The susceptibility of urinary pathogens to common antibiotics was investigated and the results analysed retrospectively using the WHONET computer program. Of 1776 urine samples (44 catheterized) processed, 510 (28.7%) urinary pathogens were isolated. Of these 510 positive cultures, 455 (89.2%) were gram-negative bacilli, 45 (8.8%) Candida species and 10 (1.9%) gram-positive cocci. Of the 44 catheterized samples, 32 (72.7%) yielded significant bacteriuria and these were mainly gram-negative bacteria (24/32). The commonest pathogen isolated was Escherichia coli (47.3%) followed by Klebsiella species (10.3%), non-fermenters other than Pseudomonas species (9%), Candida species (8.8%), Providentia species (7%), Pseudomonas species (5.6%), Citrobacter species (3.7%), Enterobacter species (3.3%) and Proteus species (2.5%). The isolation of gram-negative bacteria among inpatients and outpatients was 71.6% and 28.3%, respectively. The critical care unit, nephrourology, obstetric and gynaecology, medical and surgical wards were found to be high-risk areas constituting 58.7% of the major isolates. The highest and lowest mean resistance among gram-negative bacteria to common antibiotics was 93.5% to ampicillin and to 61% gentamicin. The mean resistance to norfloxacin, amoxy-clavulanic acid, nitrofurantoin, trimethoprim-sulfamethoxazole and cefazolin was 65%, 67%, 75.5%, 76% and 77.5%, respectively. The most resistant pathogen to common antibiotics was found to be Proteus species (resistance 80% and above). Overall susceptibility testing demonstrated decreased usefulness of common antibiotics and demonstrates a need for a re-evaluation of common antibiotics used in the therapy for urinary tract infection.

Prevalence of beta hemolytic streptococci carrier rate among schoolchildren in Salem.

A preliminary study was conducted to identify the carriers of beta hemolytic streptococci (BHS) among school children. BHS were identified, grouped by latex agglutination test and tested for susceptibility to penicillin, erythromycin and cefazolin from their throat swabs. Prevalence of BHS was found to be 21.6% with group G (43.2%) as the predominant group followed by group A (28.8%). All the isolates were sensitive to the antibiotics tested. Health cards were issued to the carriers of group A beta hemolytic streptococci.