BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) represents the most common form of chronic liver disease that urgently needs effective therapy. Rosavin, a major constituent of the Rhodiola Rosea plant of the family Crassulaceae, is believed to exhibit multiple pharmacological effects on diverse diseases. However, its effect on non-alcoholic steatohepatitis (NASH), the progressive form of NAFLD, and the underlying mechanisms are not fully illustrated.

AIM: Investigate the pharmacological activity and potential mechanism of rosavin treatment on NASH management via targeting hepatic cell death-related () mRNAs and their upstream noncoding RNA regulators ( and ) in NASH rats.

RESULTS: High sucrose high fat (HSHF) diet-induced NASH rats were treated with different concentrations of rosavin (10, 20, and 30 mg/kg/day) for the last four weeks of dietary manipulation. The data revealed that rosavin had the ability to modulate the expression of the hepatic cell death-related RNA panel through the upregulation of both () mRNAs and their epigenetic regulators ( and ). Moreover, rosavin ameliorated the deterioration in both liver functions and lipid profile, and thereby improved the hepatic inflammation, fibrosis, and apoptosis, as evidenced by the decreased protein levels of IL6, TNF-α, and caspase-3 in liver sections of treated animals compared to the untreated NASH rats.

CONCLUSION: Rosavin has demonstrated a potential ability to attenuate disease progression and inhibit hepatic cell death in the NASH animal model. The produced effect was correlated with upregulation of the hepatic cell death-related (, , , and ) mRNAs-(-() RNA panel.

The main goal of this study is to investigate the relationship between psychosocial variables and diabetic children patients and to obtain a classifier function with which it was possible to classify the patients on the basis of assessed adherence level. The rough set theory is used to identify the most important attributes and to induce decision rules from 302 samples of Kuwaiti diabetic children patients aged 7–13 years old. To increase the efficiency of the classification process, rough sets with Boolean reasoning discretization algorithm is introduced to discretize the data, then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Finally, the rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree, neural networks, and statistical discriminate analysis classifier algorithms has been made. Rough sets show a higher overall accuracy rates and generate more compact rules.

This paper introduces a new computer-aided classification system for detection of prostate cancer in
Transrectal Ultrasound images (TRUS). To increase the efficiency of the computer aided classification
process, an intensity adjustment process is applied first, based on the Pulse Coupled Neural Network
(PCNN) with a median filter. This is followed by applying a PCNN-based segmentation algorithm to
detect the boundary of the prostate image. Combining the adjustment and segmentation enable to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. Then, wavelet based features have been extracted and
normalized, followed by application of a rough set analysis to discover the dependency between the
attributes and to generate a set of reduct that contains a minimal number of attributes. Finally, a rough
confusion matrix is designed that contain information about actual and predicted classifications done by a
classification system. Experimental results show that the introduced system is very successful and has high detection accuracy

In machine learning, searching for the optimal feature subset from the original datasets is a very challenging and prominent task. The metaheuristic algorithms are used in finding out the relevant, important features, that enhance the classification accuracy and save the resource time. Most of the algorithms have shown excellent performance in solving feature selection problems. A recently developed metaheuristic algorithm, gaining-sharing knowledge-based optimization algorithm (GSK), is considered for finding out the optimal feature subset. GSK algorithm was proposed over continuous search space; therefore, a total of eight S-shaped and V-shaped transfer functions are employed to solve the problems into binary search space. Additionally, a population reduction scheme is also employed with the transfer functions to enhance the performance of proposed approaches. It explores the search space efficiently and deletes the worst solutions from the search space, due to the updation of population size in every iteration. The proposed approaches are tested over twenty-one benchmark datasets from UCI repository. The obtained results are compared with state-of-the-art metaheuristic algorithms including binary differential evolution algorithm, binary particle swarm optimization, binary bat algorithm, binary grey wolf optimizer, binary ant lion optimizer, binary dragonfly algorithm, binary salp swarm algorithm. Among eight transfer functions, V4 transfer function with population reduction on binary GSK algorithm outperforms other optimizers in terms of accuracy, fitness values and the minimal number of features. To investigate the results statistically, two non-parametric statistical tests are conducted that concludes the superiority of the proposed approach.

BACKGROUND: Patients undergoing craniotomy operations are prone to various noxious stimuli, many strategies are commenced to provide state of analgesia, for better control of the stress response and to overcome its undesired effects on the haemodynamics and post-operative pain. Scalp nerves block are considered one of these strategies. This study was conceived to evaluate the effect of addition of hyaluronidase to the local anaesthetic mixture used in the scalp nerves block in patients undergoing elective craniotomy operations.

METHODS: 64 patients undergoing elective craniotomy operations were enrolled in this prospective randomized, double-blind comparative study. Patients were randomly assigned to two groups. Group LA, patients subjected to scalp nerves block with 15 ml bupivacaine 0.5%, 15 ml lidocaine 2%, in 1:400000 epinephrine. Group H as Group LA with15 IU /ml Hyaluronidase.

RESULTS: Patients in the H group showed lower VAS values for 8 h postoperative, compared to the LA group. The haemodynamic response showed lower values in the H group, compared to the LA group. Those effects were shown in the intraoperative period and for 6 h post-operative. No difference was detected regarding the incidence of complications nor the safety profile.

CONCLUSION: Our data supports the idea that addition of hyaluronidase to the local anesthetic mixture improves the success rates of the scalp nerves block and its efficacy especially during stressful intraoperative periods and in the early postoperative period. No evident undesirable effects in relation to the addition of hyaluronidase.

TRIAL REGISTRATION: Clinical Trial registry on ClinicalTrials.gov , NCT 03411330 , 25-1-2018.

PURPOSE: To evaluate the efficacy and safety of epithelial-island crosslinking (EI-CXL) in keratoconus with corneas thinner than 400 µm.

METHODS: Twenty-six patients (30 eyes) underwent EI-CXL (preserving the epithelium over the thinnest area), using standard protocol (3 mW/cm for 30 min). Uncorrected and best spectacle-corrected distance visual acuity (UCDVA, BCDVA), manifest refractive spherical equivalent (SEQ), mean simulated keratometry (Kmean), maximum keratometry (Kmax), and thinnest corneal thickness (TCT) were determined preoperatively and at 1, 3, 6, and 12 months following CXL. Endothelial cell count (ECC) was determined preoperatively and at 6 months. Anterior segment optical coherence tomography (AS-OCT) was done at 1 month to determine the depth of the corneal stromal demarcation line (DL).

RESULTS: After 1 year, mean UCDVA improved from 1.29 preoperatively to 1.17 ( = 0.001) and BCDVA from 0.62 to 0.57 ( = 0.011). Mean manifest SEQ decreased from -7.63 to-7.32D ( = 0.001). Mean Kmean decreased from 54.92 to 54.81D ( = 0.045), and Kmax from 67.60 to 67.42D ( = 0.072), and mean TCT changed minimally from 377.17 to 375.30 µ ( = 0.11). The mean ECC decreased from 2329 to 2268 cells/mm (2.6% decrease, < 0.001). AS-OCT showed a DL in 29 out of 30 eyes at an average depth of 215.9 µ under the spared epithelium, and 299.9 µ in the de-epithelialized cornea.

CONCLUSION: EI-CXL halted keratoconus progression over a 1-year period. This was associated with statistically significant endothelial loss, but less than seen with conventional epi-off CXL in thinner corneas.