A Faunistic Study of Sand Flies of Musian District, Southwestern of Iran

Cutaneous leishmaniasis is endemic in many parts of Iran including Ilam Province. Sand flies are biological vectors of Leishmania species in human and between human and animals in the old world and new world. The special objectives of the present study regarding to the sand flies were to determine the species diversity, relative population density and sex ratio of sand flies in Musian as a part of Ilam province. The entomological studies were conducted in the four zoonotic cutaneous leishmaniasis (ZCL) infected villages, from May 2008 - October 2008. Sticky traps were used to collect sand flies from indoor and outdoor places during the present study. In this faunistic entomological study, totally 1335 sand flies, including 17.5 females and 82.5 males, were collected from indoor and outdoor places, 857 (62.2) and 478 (37.8), respectively. Totally 10 species of sand flies we're recognized, 3 belonging to the Phlebotomus (P. alexandri, P. papatasi and P. mongolensis) and 7 belonging to Sergentomyia (S. sintoni, S. antennata, S. tnervynae, S. theodori, S. clydei, S. tiberiadis and S. palestinesis) genera. Finally, it is concluded that the composition of species in Mousian is almost similar to the other parts of Iran with dominance of P. papatasi

Outcomes of Phaco-viscocanalostomy in Primary Open Angle Glaucoma Versus Pseudoexfoliation Glaucoma

Purpose: Viscocanalostomy represents an alternative to standard penetrating glaucoma surgery. The aim of this study is to compare the outcomes of combined phacoemulsification and viscocanalostomy in eyes with primary open-angle glaucoma (POAG) versus eyes with pseudoexfoliation glaucoma (PEXG). Methods: In this prospective non-randomized comparative study, eyes with Cataract and POAG or PEXG were enrolled. Pre- and postoperative data including best corrected visual acuity (BCVA), intraocular pressure (IOP), and the number of antiglaucoma medications administered were recorded at each visit. All patients underwent phacoviscocanalostomy. Complete success was defined as the IOP of 21 mmHg or less without the administration of medication while a qualified success reported the same IOP parameters either with or without the administration of medication. Results: Fifty-four eyes with POAG and fifty-four with PEXG underwent phacoviscocanalostomy. The mean follow-up time was 23.36 ± 8.8 months (range, 6–40 months). The mean postoperative IOP reduced significantly in both groups, although the mean IOP reduction was significantly greater in PEXG eyes (14.7 ± 8.9 vs 10.1 ± 7.7 mmHg) (P = 0.05). At the final follow-up visit, the mean postoperative IOP was 14.1 ± 2.1 and 16.6 ± 3.5 mmHg in the PEXG and POAG eyes, respectively (P = 0.001). A complete success rate of 88.9% and 75.9% was achieved in PEXG and POAG eyes, respectively (P = 0.07). The qualified success rate was 100% in the PEXG and 85.2% in POAG groups (P = 0.03). Conclusion: Phacoviscocanalostomy achieved significant IOP reduction and visual improvement in both POAG and PEXG patients. Our results indicated that in terms of IOP reduction, this procedure was more effective in treating PEXG

RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights

Myocarditis is heart muscle inflammation that is becoming more prevalent these days, especially with the prevalence of COVID-19. Noninvasive imaging cardiac magnetic resonance (CMR) can be used to diagnose myocarditis, but the interpretation is time-consuming and requires expert physicians. Computer-aided diagnostic systems can facilitate the automatic screening of CMR images for triage. This paper presents an automatic model for myocarditis classification based on a deep reinforcement learning approach called as reinforcement learning-based myocarditis diagnosis combined with population-based algorithm (RLMD-PA) that we evaluated using the Z-Alizadeh Sani myocarditis dataset of CMR images prospectively acquired at Omid Hospital, Tehran. This model addresses the imbalanced classification problem inherent to the CMR dataset and formulates the classification problem as a sequential decision-making process. The policy of architecture is based on convolutional neural network (CNN). To implement this model, we first apply the artificial bee colony (ABC) algorithm to obtain initial values for RLMD-PA weights. Next, the agent receives a sample at each step and classifies it. For each classification act, the agent gets a reward from the environment in which the reward of the minority class is greater than the reward of the majority class. Eventually, the agent finds an optimal policy under the guidance of a particular reward function and a helpful learning environment. Experimental results based on standard performance metrics show that RLMD-PA has achieved high accuracy for myocarditis classification, indicating that the proposed model is suitable for myocarditis diagnosis