Male Breast Cancer:10-Year Experience at Mansoura University Hospital in Egypt

Objective Male breast cancer(MBC) is a rare disease representing less than 1%of all malignancies.The objective of the study is to report clinicopathological characteristics,treatment patterns,and outcomes of MBC in Mansoura University Hospital,Egypt. Methods This retrospective study focused on male breast cancer patients during 10 years(2000-2009).The studied variables were data regarding general characteristics of patients,treatment modalities and survival. Results The series included 37 patients(0.8%of all breast cancer).The median age was 57.7 years(range:26-86 years).The main clinical complaint was a mass beneath the areola in 94.5%of the cases.Most patients had a locally advanced disease.94.5%of tumors were invasive duct carcinomas.The treatment was essentially surgery in 91.8%,followed by adjuvant radiotherapy(in 89.2%), hormonal therapy(in 56.7%) and chemotherapy(in 91.8%).Follow-up period ranged from 6-115 months.Local recurrence occurred in 4 cases and metastasis in 11 cases.The 2-year and 5-year overall survival(OS) rates were 81.6%and 60.5%,respectively.The 2-year and 5-year disease-free survival(DFS) rates were 68.4%,and 52.6%,respectively.OS was not significantly affected by any of the studied parameters.Factors influencing DFS were:T stage(P=0.05),positive lymph nodes(P=0.043),metastasis(P=0.004),and chemotherapy (P=0.046). Conclusions MBC is a rare disease and often diagnosed at a locally advanced stage.The management of male and female breast carcinoma is identical.Future research for better understanding of this disease is needed to improve the management and prognosis of male breast cancer patients.

Characterisation of Class 1 Integron among <i>Escherichia coli</i>Isolated from Mansoura University Hospitals in Egypt

Extensive use of antibacterials in clinical practice has been associated with increasing frequency of multi-resistant E. coli strains. Genetic elements such as Class 1 integrons have an important role in resistance development. In the current study, a total of 84 E. coli clinical isolates from Mansoura hospitals patients in Egypt were screened for antibacterial susceptibility against 12 different antibacterials. High resistance rates were identified for Ampicillin (92.9%) and sulfamethoxazole/ trimethoprim (84.5%). Class 1 integron was investigated in E. coli isolates by PCR. As a result, Class 1 integron was identified in 51.2% of these isolates. The contents of amplified integron varriable regions, were digested by Alu I restriction endonuclease. Cluster analysis of Class 1 integron digested varriable regions revealed that RFLP digested fragments generated could be classified into 9 different patterns, namely A, B, C, D, E, F, G, H and I. The most prevalent genotype was identifed in Group D. PCR and sequencing were used for detection of antimicrobial resistance genes harbored in integrons of Group D. As a result, main phylogenetic group identified harbored integron cassette carrying resistance gene for two antimicrobial groups namely aminoglycoside and trimethoprim. Multiresistance profiles in Group D exhibited association between antimicrobial resistance and integron presence. These findings suggest that the strategy for treatment of patients with E. coli infections needs to be revised. Furthermore, the high prevalence of Class 1 integron carrying gene confirms the importance of integron-mediated antimicrobial gene cassettes.

Low-Dose Involved-Field Radiotherapy in Relapsed Low-Grade Non-Hodgkin's Lymphoma in Elderly Patients (Mansoura University Experience)

Purpose: To assess the response rate, duration of response and prognostic factors affecting response after low-dose involved-field radiotherapy in patients with relapsed low-grade B-cell non-Hodgkin lymphoma. Patients and Methods: Forty-four patients were included. Patients were treated with a total dose of 4 Gy (2 × 2 Gy) using 6 - 15 Mv photon or electron beam. Results: most patients were above age of 60 years (59%) with male predominance. Follicular lymphoma was the most common pathological type;bulky disease (>5 cm) was presented in 61.4%. Patients who received only 2 regimens were 63.7% and 31.8% had >2 involved sites. No treatment related toxicity was observed. The overall response rate was 88.7%;complete response was reached in 59.1% and stable disease in 6.8%, progressive disease in 4.5%. Median time to local progression was 33 months (95% CI 23.70 - 42.29);2-year local progression free survival was 78%. Response rate was found to be dependent on age, number of involved sites and lymph node size but independent on sex, pathological type, number of prior regimens, LDH level and time since diagnosis. Conclusion: Short-course-low dose palliative radiotherapy (2 × 2 Gy) affords an attractive option for treatment of relapsed low-grade non-Hodgkin’s lymphoma due to high response rates. However, these results had to be confirmed in a larger number of patients.

The impact of chemotherapy-naïve open radical cystectomy delay and perioperative transfusion on the recurrence-free survival: A perioperative parameters-based nomogram

Objective: To develop and internally validate a nomogram to predict recurrence-free survival (RFS) including the time to radical cystectomy (RC) and perioperative blood transfusion (PBT) as potential predictors.Methods: Patients who underwent open RC and ileal conduit between January 1996 to December 2016 were split into developing (n=948) and validating (n=237) cohorts. The time to radical cystectomy (TTC) was defined as the interval between the onset of symptoms and RC. The regression coefficients of the independent predictors obtained by Cox regression were used to construct the nomogram. Discrimination, validation, and clinical usefulness in the validation cohort were assessed by the area under the curve, the calibration plot, and decision curve analysis.Results: In the developing dataset, the 1-, 5-, and 10-year RFS were 83.0%, 47.2%, and 44.4%, respectively. On multivariate analysis, independent predictors were TTC (hazards ratio [HR] 1.07, 95% confidence interval [CI] 1.05-1.08, p<0.001), PBT (one unit: HR 1.40, 95% CI 1.03-1.90, p=0.03;two or more units: HR 1.72, 95% CI 1.29-2.29, p<0.001), bilateral hydronephrosis (HR 1.54, 95% CI 1.21-1.97, p<0.001), squamous cell carcinoma (HR 0.60, 95% CI 0.45-0.81, p=0.001), pT3-T4 (HR 1.77, 95% CI 1.41-2.22, p<0.001), lymph node status (HR 1.53, 95% CI 1.21-1.95, p<0.001), and lymphovascular invasion (HR 1.28, 95% CI 1.01-1.62, p=0.044). The areas under the curve in the validation dataset were 79.3%, 69.6%, and 76.2%, for 1-, 5-, and 10-year RFS, respectively. Calibration plots showed considerable correspondence between predicted and actual survival probabilities. The decision curve analysis revealed a better net benefit of the nomogram.Conclusion: A nomogram with good discrimination, validation, and clinical utility was constructed utilizing TTC and PBT in addition to standard pathological criteria.

The role of polymorphic cytochrome P450 gene(CYP2B6)in B-chronic lymphocytic leukemia(B-CLL)incidence and outcome among Egyptian patients

Cytochromes P450(CYPs)play a prominent role in catalyzing phase I xenobiotic biotransformation and account for about 75%of the total metabolism of commercially available drugs,including chemotherapeutics.The gene expression and enzyme activity of CYPs are variable between individuals,which subsequently leads to different patterns of susceptibility to carcinogenesis by genotoxic xenobiotics,as well as differences in the efficacy and toxicity of clinically used drugs.This research aimed to examine the presence of the CYP2B6*9 polymorphism and its possible association with the incidence of B-CLL in Egyptian patients,as well as the clinical outcome after receiving cyclophosphamide chemotherapy.DNA was isolated from whole blood samples of 100 de novo B-CLL cases and also from 100 sex-and age-matched healthy individuals.The presence of the CYP2B6*9(G516T)polymorphism was examined by PCR-based allele specific amplification(ASA).Patients were further indicated for receiving chemotherapy,and then they were followed up.The CYP2B6*9 variant indicated a statistically significant higher risk of B-CLL under different genetic models,comprising allelic(T-allele vs.G-allele,OR=4.8,p<0.001)and dominant(GT+TT vs.GG,OR=5.4,p<0.001)models.Following cyclophosphamide chemotherapy,we found that the patients with variant genotypes(GT+TT)were less likely to achieve remission compared to those with the wild-type genotype(GG),with a response percentage of(37.5%vs.83%,respectively).In conclusion,our findings showed that the CYP2B6*9(G516T)polymorphism is associated with B-CLL susceptibility among Egyptian patients.This variant greatly affected the clinical outcome and can serve as a good therapeutic marker in predicting response to cyclophosphamide treatment.

Pyronaridine combined with diminazene aceturate inhibits Babesia in vitro and in vivo

Objective:To evaluate the combination therapy of pyronaridine tetraphosphate and diminazene aceturate against Babesia in vitro and in vivo.Methods:Bioinformatic analysis was performed using atom pair fingerprints.An in vitro combination test was performed against Babesia bovis and Theileria equi.Moreover,the in vivo chemotherapeutic efficacy of pyronaridine tetraphosphate in combination with diminazene aceturate was investigated against the growth of Babesia microti in mice using a fluorescence inhibitory assay.Results:Pyronaridine tetraphosphate and diminazene aceturate exhibited nearly similar molecular weights.The in vitro combination of pyronaridine tetraphosphate and diminazene aceturate was synergistic on Babesia bovis and additive on Theileria equi.In addition,5 mg/kg pyronaridine tetraphosphate combined with 10 mg/kg diminazene aceturate inhibited Babesia microti growth significantly compared with those observed after treatment with 25 mg/kg diminazene aceturate alone from day 6 post treatment to day 12 post treatment.The combination therapy also normalized the hematological parameters of infected mice.Conclusions:An oral dose of pyronaridine tetraphosphate combined with a subcutaneous dose of diminazene aceturate inhibits Babesia in vitro and in mice,suggesting it might be a new paradigm for the treatment of babesiosis.

Human dental pulp stem/stromal cells in clinical practice

Dental pulp stem/stromal cells(DPSCs)are fibroblast-like,neural crest-derived,and multipotent cells that can differentiate into several lineages.They are relatively easy to isolate from healthy and inflamed pulps,with little ethical concerns and can be successfully cryopreserved and thawed.The therapeutic effects of DPSCs derived from animal or human sources have been extensively studied through in-vitro and in-vivo animal experiments and the findings indicated that DPSCs are effective not only for dental diseases but also for systemic diseases.Understanding that translational research is a critical step through which the fundamental scientific discoveries could be translated into applicable diagnostics and therapeutics that directly benefit humans,several clinical studies were carried out to generate evidence for the efficacy and safety of autogenous or allogeneic human DPSCs(hDPSCs)as a treatment modality for use in cell-based therapy,regenerative medicine/dentistry and tissue engineering.In clinical medicine,hDPSCs were effective for treating acute ischemic stroke and human exfoliated deciduous teeth-conditioned medium(SHED-CM)repaired vascular damage of the corpus cavernous,which is the main cause of erectile dysfunction.Whereas in clinical dentistry,autologous SHED was able to rege-nerate necrotic dental pulp after implantation into injured teeth,and micrografts enriched with autologous hDPSCs and collagen sponge were considered a treatment option for human intrabony defects.In contrast,hDPSCs did not add a significant regenerative effect when they were used for the treatment of post-extraction sockets.Large-scale clinical studies across diverse populations are still lacking to provide robust evidence on the safety and efficacy of hDPSCs as a new treatment option for various human diseases including dental-related problems.

Beyond boundaries:Feasibility of curved linear array echoendoscope in appendiceal neoplasm detection

We recently read with great interest a study by Zhang et al in the World Journal of Gastroenterology.In our practice,we focus specifically on examining appendiceal mucinous neoplasms(AMNs)with endoscopic ultrasound(EUS)using different scopes.AMNs are rare neoplastic lesions characterized by an accumulation of mucin inside a cystic dilatation of the appendix.Clinically,they can present as nonspecific acute appendicitis.AMNs can turn into a life-threatening condition,termed pseudomyxoma peritonei,in which the ruptured appendix causes accumulation of mucin in the abdomen.Therefore,accurate and rapid diagnosis of AMN is essential.EUS is able to confirm and stage AMNs;although,EUS examination was once limited to the rectal and anal regions due to the conven-tional oblique-view scopes.With the emergence of new forward-view linear echoendoscopes and instruments like EUS miniprobes and overtubes,the scope of examination is changing.Herein,we discuss the feasibility of using the curved linear array echoendoscopes to examine cecal and appendiceal orifice lesions.

Smart prediction of liquefaction-induced lateral spreading

The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping ground.Along with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,i.e.Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP model.The results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,respectively.The overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,respectively.For the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,respectively.Finally,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.

Explainable Conformer Network for Detection of COVID-19 Pneumonia from Chest CT Scan: From Concepts toward Clinical Explainability

The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT scans.This study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 diagnosis.This paper proposes a novel deep learning approach,called Conformer Network,for explainable discrimination of viral pneumonia depending on the lung Region of Infections(ROI)within a single modality radiographic CT scan.Firstly,an efficient U-shaped transformer network is integrated for lung image segmentation.Then,a robust transfer learning technique is introduced to design a robust feature extractor based on pre-trained lightweight Big Transfer(BiT-L)and finetuned on medical data to effectively learn the patterns of infection in the input image.Secondly,this work presents a visual explanation method to guarantee clinical explainability for decisions made by Conformer Network.Experimental evaluation of real-world CT data demonstrated that the diagnostic accuracy of ourmodel outperforms cutting-edge studies with statistical significance.The Conformer Network achieves 97.40% of detection accuracy under cross-validation settings.Our model not only achieves high sensitivity and specificity but also affords visualizations of salient features contributing to each classification decision,enhancing the overall transparency and trustworthiness of our model.The findings provide obvious implications for the ability of our model to empower clinical staff by generating transparent intuitions about the features driving diagnostic decisions.

MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.

Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate

In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific region.Three simulations were conducted with a 25-km grid spacing for the period 1980–2014.The first simulation(WRF_ERA5)was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)dataset and served as the validation dataset.The original GCM dataset(MPI-ESM1-2-HR model)was used to drive the second simulation(WRF_GCM),while the third simulation(WRF_GCMbc)was driven by the bias-corrected GCM dataset.The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models.Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors(RMSEs)of the climatological mean of downscaled variables,including temperature,precipitation,snow,wind,relative humidity,and planetary boundary layer height by 50%–90%compared to the WRF_GCM.Similarly,the RMSEs of interannual-tointerdecadal variances of downscaled variables were reduced by 30%–60%.Furthermore,the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities.The leading empirical orthogonal function(EOF)shows a monopole precipitation mode in the WRF_GCM.In contrast,the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China.This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression

Deep learning(DL)plays a critical role in processing and converting data into knowledge and decisions.DL technologies have been applied in a variety of applications,including image,video,and genome sequence analysis.In deep learning the most widely utilized architecture is Convolutional Neural Networks(CNN)are taught discriminatory traits in a supervised environment.In comparison to other classic neural networks,CNN makes use of a limited number of artificial neurons,therefore it is ideal for the recognition and processing of wheat gene sequences.Wheat is an essential crop of cereals for people around the world.Wheat Genotypes identification has an impact on the possible development of many countries in the agricultural sector.In quantitative genetics prediction of genetic values is a central issue.Wheat is an allohexaploid(AABBDD)with three distinct genomes.The sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat,Therefore,genome sequence approaches based on techniques of Artificial Intelligence(AI)are necessary.This paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources and managing genetic variation in their breeding program,as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current constraints.In this paper,the hyperparameters of the network are optimized in the CNN to decrease the requirement for manual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks(CNN).

Novel Sustainable Cellulose Acetate Based Biosensor for Glucose Detection

In this study,green zinc oxide(ZnO)/polypyrrole(Ppy)/cellulose acetate(CA)film has been synthesized via solvent casting.This film was used as supporting material for glucose oxidase(GOx)to sensitize a glucose biosensor.ZnO nanoparticles have been prepared via the green route using olive leaves extract as a reductant.ZnO/Ppy nanocomposite has been synthesized by a simple in-situ chemical oxidative polymerization of pyrrole(Py)monomer using ferric chloride(FeCl3)as an oxidizing agent.The produced materials and the composite films were characterized using X-ray diffraction analysis(XRD),scanning electron microscope(SEM),Fourier transform infrared(FTIR)and thermogravimetric analysis(TGA).Glucose oxidase was successfully immobilized on the surface of the prepared film and then ZnO/Ppy/CA/GOx composite was sputtered with platinum electrode for the current determination at different initial concentrations of glucose.Current measurements proved the suitability and the high sensitivity of the constructed biosensor for the detection of glucose levels in different samples.The performance of the prepared biosensor has been assessed by measuring and comparing glucose concentrations up to 800 ppm.The results affirmed the reliability of the developed biosensor towards real samples which suggests the wide-scale application of the proposed biosensor.

Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance

Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.

Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.

Problem of living liver donation in the absence of deceased liver transplantation program:Mansoura experience

We report our experience with potential donors for living donor liver transplantation(LDLT), which is the first report from an area where there is no legalized deceased donation program. This is a single center retrospective analysis of potential living donors(n = 1004) between May 2004 and December 2012. This report focuses on the analysis of causes, duration, cost, and various implications of donor exclusion(n = 792).Most of the transplant candidates(82.3%) had an experience with more than one excluded donor(median = 3). Some recipients travelled abroad for a deceased donor transplant(n = 12) and some died before finding a suitable donor(n = 14). The evaluation of an excluded donor is a time-consuming process(median = 3 d, range 1 d to 47 d). It is also a costly process with a median cost of approximately 70 USD(range 35 USD to 885 USD). From these results, living donor exclusion has negative implications on the patients and transplant program with ethical dilemmas and an economic impact. Many strategies are adopted by other centers to expand the donor pool; however, they are not all applicable in our locality. We conclude that an active legalized deceased donor transplantation program is necessary to overcome the shortage of available liver grafts in Egypt.

Epidemiological,clinical and laboratory findings of infectious keratitis at Mansoura Ophthalmic Center,Egypt

AIM： To analyze the epidemiological, clinical and laboratory findings of infectious keratitis.METHODS： A retrospective study on cases of infective keratitis, attended our institution from Mar. 2013 to Feb. 2015, was done at Mansoura Ophthalmic Center, Egypt. Corneal scrapings were performed and processed for direct microscopy and culture in appropriate media using standard laboratory protocols.RESULTS： Out of 245 patients enrolled for study, 247 corneal scrapings were obtained. Ocular trauma was the most common predisposing factor （51.4%）, followed by diabetes mellitus （15.1%）. Cultures were positive in 110 scraping samples （44.5%）： 45.5% samples had pure fungal infection, 40% had pure bacterial infections and 10% had mixed fungal and bacterial growths. Acanthamoeba was detected in 5 （4.5%） samples. The most common fungal pathogen was Aspergillus spp. （41%）. The most common bacterial isolates were Staphylococcus aureus （38.2%） and Pseudomonas aeruginosa （21.8%）.CONCLUSION： Incidence of fungal keratitis is high in our region. Therapeutic approach can initially be based on clinical features and sensitivity/resistance patterns. Microbiological research should direct the antimicrobial treatment. Antibiotic resistance to fluoroquinolones and aminoglycosides is an important consideration.

Mechanical Ventilation Outcomes at the NICU at EL-Zahraa University Hospital

Background: Mechanical ventilation (MV) is one of the most common therapies in the neonatal Intensive Care Unit (NICU) and is associated with increased morbidity and mortality. MV is a complex and highly specialized area in neonatology that has several complications related to different modes, techniques, and devices. Aim of the Study: To detect the causes of morbidity and mortality in mechanically ventilated neonates and to correlate the neonatal morbidity and mortality with gestational age, birth weight and duration of MV. Patients and Methods: This prospective observation analytical study was carried out on 110 neonates who were admitted to NICU at Al-Zahraa university hospital on MV during the period from March 2019 to March 2020. All neonates were followed up till the time of discharge with record of any complications during mechanical ventilation. Detailed medical history, examination to detect indication of MV and laboratory, radiological investigations were recorded. As regard the general characterization of studied neonates, 64 (58.2%) were males while the rest 46 (41.8%) were females, 50 (45.4%) were full term (37 - 42 wks), 42 (38.18%) were early preterm (28 - 33 wks), 18 (16.3%) were late preterm (34 - 42 wks). Results: Among (110 mechanically ventilated neonates, 58.2% were males, 41.8% were females. The most common indication for MV in the studied cases was respiratory distress syndrome (RDS) in 30% neonates. Ventilator associated pnemonia and Devices associated infection were the most common complications related to MV (19.1% and 11.8% respectively). While septic shock and multiorgan failure were the most common complications related to the underlying disease (24%). There is a significant negative association between gestational age, birth weight and mortality in mechanically ventilated neonates. the overall recovery was 59.1% among studied neonates. Conclusion: Respiratory disorders were the commonest indication of MV in NICU mostly RDS. Ventilator associated pneumonia and devices associated infection are common complications. Preterm and low birth weight neonates are vulnerable group for need of mechanical ventilation.