Level of interleukin 17 in inflammatory bowel disease and its relation with disease activity.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic relapsing inflammatory disorder of the gastrointestinal tract (GIT).It results in progressive intestinal epithelium structural and functional damage that necessitates lifetime medication.Thereis imbalance in the production of T helper 1 (Th1), Th2 and Th17 cytokines. This plays a crucial role in the chronic inflammatory process and the defective immune response to pathogenic agents; thus promoting the recurrence of the disease.Our aim of this study was to detect serum IL-17 levels in IBD patients and its relation with disease activity. METHODS: This was a single center case control study, conducted at hepatology and gastroenterology unit, Mansoura specialized Medical Hospital, Egypt.Patients who were included were aged 18-65 years, diagnosed either Ulcerative Colitis (UC)or Crohn's Disease (CD) based on previous colonoscopy.IBD activity was measured for UC using the MAYO score and CD using the CD activity index (CDAI). Fifty five patients were UC, 24 patients were CD, 21 patients were control.Patients who were excluded were under 15 years old, with history of GIT malignancy, or any serious comorbidities. Study protocol was approved by Institution Research Board (IRB) of Mansoura Medical College.All patients were subjected to full history taking, routine physical examination, colonoscopy and laboratory investigations including serum IL-17 levels by ELISA besides CBC, CRP, ESR and fecal calprotectin. RESULTS: Serum IL-17 level was increased significantly among UC; median (min-max) = 72(21-502)pg/ml, in CD 54.5(25-260) versus control 19 (14-35), P < 0.001.However, it was not correlated to the disease activity either Mayo score of UC or CDAI of CD.There was significant correlation to the extent of inflammation in UC affecting the colon (either proctosigmoiditis, left sided colitis or pan colitis), also to the type of CD (either inflammatory, stricturing or fistulizing) by P < 0.05.It was not correlated significantly with any of the IBD activity markers (CRP, ESR, or fecal calprotectin).Yet there was negative significant correlation with Hb level (r =-0.28, p = 0.005).There was not significant association between median serum level of IL-17 & duration of disease (P = 0.6).However, median IL-17 was higher among hospitalized cases than non-hospitalized (73 & 55, pg/ml respectively; p < 0.002). AUC was significantly differentiating between IBD and control group = 0.993 with the best-detected cut off point from curve 32 pg/ml yielding sensitivity of 97.5% and specificity of 95.2%. CONCLUSION: Serum IL-17 increases in colonic inflammation significantly more than in control group, however its increase is not correlated to IBD activity.

Therapeutic and diagnostic applications of exosomal circRNAs in breast cancer.

Circular RNAs (circRNAs) are regulatory elements that are involved in orchestrating gene expression and protein functions and are implicated in various biological processes including cancer. Notably, breast cancer has a significant mortality rate and is one of the most common malignancies in women. CircRNAs have been demonstrated to contribute to the pathogenesis of breast cancer including its initiation, progression, metastasis, and resistance to drugs. By acting as miRNA sponges, circRNAs can indirectly influence gene expression by disrupting miRNA regulation of their target genes, ultimately altering the course of cancer development and progression. Additionally, circRNAs can interact with proteins and modulate their functions including signaling pathways involved in the initiation and development of cancer. Recently, circRNAs can encode peptides that play a role in the pathophysiology of breast cancer and other diseases and their potential as diagnostic biomarkers and therapeutic targets for various cancers including breast cancer. CircRNAs possess biomarkers that differentiate, such as stability, specificity, and sensitivity, and can be detected in several biological specimens such as blood, saliva, and urine. Moreover, circRNAs play an important role in various cellular processes including cell proliferation, differentiation, and apoptosis, all of which are integral factors in the development and progression of cancer. This review synthesizes the functions of circRNAs in breast cancer, scrutinizing their contributions to the onset and evolution of the disease through their interactions with exosomes and cancer-related intracellular pathways. It also delves into the potential use of circRNA as a biomarker and therapeutic target against breast cancer. It discusses various databases and online tools that offer crucial circRNA information and regulatory networks. Lastly, the challenges and prospects of utilizing circRNAs in clinical settings associated with breast cancer are explored.

Highly sensitive spectrofluorimetric method for the determination of the genotoxic methylglyoxal in glycerol-containing pharmaceuticals and dietary supplements.

Methylglyoxal (MGO) is a genotoxic α-dicarbonyl compound. Recently, it was found to be formed in glycerol preparations during storage through auto-oxidation. A simple fluorimetric determination of the carcinogenic degradation product of glycerol, MGO, was developed and validated. The proposed method is based on the derivatization of MGO with 4-carbomethoxybenzaldehyde (CMBA) and ammonium acetate to yield a fluorescent imidazole derivative that can be measured at 415 nm after excitation at 322 nm. The optimized conditions were determined to be 0.2 M CMBA, 1.0 M ammonium acetate and a reaction time of 40 min at 90°C using ethanol as diluting solvent. The linear range was 10.0-200.0 ng/ml. Detection and quantification limits were 2.22 and 6.72 ng/ml, respectively. The proposed method was validated according to International Council for Harmonisation (ICH) guidelines and compared with the reported method and no significant difference was found. It was successfully applied for the determination of MGO in six different glycerol-containing pharmaceutical preparations and dietary supplements.

Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models.

Internet of Things (IoT) devices for the home have made a lot of people's lives better, but their popularity has also raised privacy and safety concerns. This study explores the application of deep learning models for anomaly detection and face recognition in IoT devices within the context of smart homes. Six models, namely, LR-XGB-CNN, LR-GBC-CNN, LR-CBC-CNN, LR-HGBC-CNN, LR-ABC-CNN, and LR-LGBM-CNN, were proposed and evaluated for their performance. The models were trained and tested on labeled datasets of sensor readings and face images, using a range of performance metrics to assess their effectiveness. Performance evaluations were conducted for each of the proposed models, revealing their strengths and areas for improvement. Comparative analysis of the models showed that the LR-HGBC-CNN model consistently outperformed the others in both anomaly detection and face recognition tasks, achieving high accuracy, precision, recall, F1 score, and AUC-ROC values. For anomaly detection, the LR-HGBC-CNN model achieved an accuracy of 94%, a precision of 91%, a recall of 96%, an F1 score of 93%, and an AUC-ROC of 0.96. In face recognition, the LR-HGBC-CNN model demonstrated an accuracy of 88%, precision of 86%, recall of 90%, F1 score of 88%, and an AUC-ROC of 0.92. The models exhibited promising capabilities in detecting anomalies, recognizing faces, and integrating these functionalities within smart home IoT devices. The study's findings underscore the potential of deep learning approaches for enhancing security and privacy in smart homes. However, further research is warranted to evaluate the models' generalizability, explore advanced techniques such as transfer learning and hybrid methods, investigate privacy-preserving mechanisms, and address deployment challenges.

A Multi-Attention Approach for Person Re-Identification Using Deep Learning.

Person re-identification (Re-ID) is a method for identifying the same individual via several non-interfering cameras. Person Re-ID has been felicitously applied to an assortment of computer vision applications. Due to the emergence of deep learning algorithms, person Re-ID techniques, which often involve the attention module, have gained remarkable success. Moreover, people's traits are mostly similar, which makes distinguishing between them complicated. This paper presents a novel approach for person Re-ID, by introducing a multi-part feature network, that combines the position attention module (PAM) and the efficient channel attention (ECA). The goal is to enhance the accuracy and robustness of person Re-ID methods through the use of attention mechanisms. The proposed multi-part feature network employs the PAM to extract robust and discriminative features by utilizing channel, spatial, and temporal context information. The PAM learns the spatial interdependencies of features and extracts a greater variety of contextual information from local elements, hence enhancing their capacity for representation. The ECA captures local cross-channel interaction and reduces the model's complexity, while maintaining accuracy. Inclusive experiments were executed on three publicly available person Re-ID datasets: Market-1501, DukeMTMC, and CUHK-03. The outcomes reveal that the suggested method outperforms existing state-of-the-art methods, and the rank-1 accuracy can achieve 95.93%, 89.77%, and 73.21% in trials on the public datasets Market-1501, DukeMTMC-reID, and CUHK03, respectively, and can reach 96.41%, 94.08%, and 91.21% after re-ranking. The proposed method demonstrates a high generalization capability and improves both quantitative and qualitative performance. Finally, the proposed multi-part feature network, with the combination of PAM and ECA, offers a promising solution for person Re-ID, by combining the benefits of temporal, spatial, and channel information. The results of this study evidence the effectiveness and potential of the suggested method for person Re-ID in computer vision applications.

Enhanced Deep Learning Approach for Accurate Eczema and Psoriasis Skin Detection.

This study presents an enhanced deep learning approach for the accurate detection of eczema and psoriasis skin conditions. Eczema and psoriasis are significant public health concerns that profoundly impact individuals' quality of life. Early detection and diagnosis play a crucial role in improving treatment outcomes and reducing healthcare costs. Leveraging the potential of deep learning techniques, our proposed model, named "Derma Care," addresses challenges faced by previous methods, including limited datasets and the need for the simultaneous detection of multiple skin diseases. We extensively evaluated "Derma Care" using a large and diverse dataset of skin images. Our approach achieves remarkable results with an accuracy of 96.20%, precision of 96%, recall of 95.70%, and F1-score of 95.80%. These outcomes outperform existing state-of-the-art methods, underscoring the effectiveness of our novel deep learning approach. Furthermore, our model demonstrates the capability to detect multiple skin diseases simultaneously, enhancing the efficiency and accuracy of dermatological diagnosis. To facilitate practical usage, we present a user-friendly mobile phone application based on our model. The findings of this study hold significant implications for dermatological diagnosis and the early detection of skin diseases, contributing to improved healthcare outcomes for individuals affected by eczema and psoriasis.

Internet of Medical Things and Healthcare 4.0: Trends, Requirements, Challenges, and Research Directions.

Healthcare 4.0 is a recent e-health paradigm associated with the concept of Industry 4.0. It provides approaches to achieving precision medicine that delivers healthcare services based on the patient's characteristics. Moreover, Healthcare 4.0 enables telemedicine, including telesurgery, early predictions, and diagnosis of diseases. This represents an important paradigm for modern societies, especially with the current situation of pandemics. The release of the fifth-generation cellular system (5G), the current advances in wearable device manufacturing, and the recent technologies, e.g., artificial intelligence (AI), edge computing, and the Internet of Things (IoT), are the main drivers of evolutions of Healthcare 4.0 systems. To this end, this work considers introducing recent advances, trends, and requirements of the Internet of Medical Things (IoMT) and Healthcare 4.0 systems. The ultimate requirements of such networks in the era of 5G and next-generation networks are discussed. Moreover, the design challenges and current research directions of these networks. The key enabling technologies of such systems, including AI and distributed edge computing, are discussed.

AuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models.

Color face images are often transmitted over public channels, where they are vulnerable to tampering attacks. To address this problem, the present paper introduces a novel scheme called Authentication and Color Face Self-Recovery (AuCFSR) for ensuring the authenticity of color face images and recovering the tampered areas in these images. AuCFSR uses a new two-dimensional hyperchaotic system called two-dimensional modular sine-cosine map (2D MSCM) to embed authentication and recovery data into the least significant bits of color image pixels. This produces high-quality output images with high security level. When tampered color face image is detected, AuCFSR executes two deep learning models: the CodeFormer model to enhance the visual quality of the recovered color face image and the DeOldify model to improve the colorization of this image. Experimental results demonstrate that AuCFSR outperforms recent similar schemes in tamper detection accuracy, security level, and visual quality of the recovered images.

ECG-COVID: An end-to-end deep model based on electrocardiogram for COVID-19 detection.

The early and accurate detection of COVID-19 is vital nowadays to avoid the vast and rapid spread of this virus and ease lockdown restrictions. As a result, researchers developed methods to diagnose COVID-19. However, these methods have several limitations. Therefore, presenting new methods is essential to improve the diagnosis of COVID-19. Recently, investigation of the electrocardiogram (ECG) signals becoming an easy way to detect COVID-19 since the ECG process is non-invasive and easy to use. Therefore, we proposed in this paper a novel end-to-end deep learning model (ECG-COVID) based on ECG for COVID-19 detection. We employed several deep Convolutional Neural Networks (CNNs) on a dataset of 1109 ECG images, which is built for screening the perception of COVID-19 and cardiac patients. After that, we selected the most efficient model as our model for evaluation. The proposed model is end-to-end where the input ECG images are fed directly to the model for the final decision without using any additional stages. The proposed method achieved an average accuracy of 98.81%, Precision of 98.8%, Sensitivity of 98.8% and, F1-score of 98.81% for COVID-19 detection. As cases of corona continue to rise and hospitalizations continue again, hospitals may find our study helpful when dealing with these patients who did not get significantly worse.

Inhaled corticosteroids' effect on COVID-19 patients: A systematic review and meta-analysis of randomized controlled trials.

Background: More than six million people died due to COVID-19, and 10-15% of infected individuals suffer from post-covid syndrome. Corticosteroids are widely used in the management of severe COVID-19 and post-acute COVID-19 symptoms. This study synthesizes current evidence of the effectiveness of inhaled corticosteroids (ICS) on mortality, hospital length-of-stay (LOS), and improvement of smell scores in patients with COVID-19. Methods: We searched Embase, Web of Science, PubMed, Cochrane Library, and Scopus until Aug 2022. The Cochrane risk of bias tool was used to assess the quality of studies. We evaluated the effectiveness of ICS in COVID-19 patients through measures of mortality, LOS, alleviation of post-acute COVID-19 symptoms, time to sustained self-reported cure, and sense of smell (visual analog scale (VAS)). Results: Ten studies were included in the meta-analysis. Our study showed a significant decrease in the LOS in ICS patients over placebo (MD = -1.52, 95% CI [-2.77 to -0.28], p-value = 0.02). Patients treated with intranasal corticosteroids (INC) showed a significant improvement in VAS smell scores from week three to week four (MD =1.52, 95% CI [0.27 to 2.78], p-value = 0.02), and alleviation of COVID-related symptoms after 14 days (RR = 1.17, 95% CI [1.09 to 1.26], p-value < 0.0001). No significant differences were detected in mortality (RR= 0.69, 95% CI [0.36 to 1.35], p-value = 0.28) and time to sustained self-reported cure (MD = -1.28, 95% CI [-6.77 to 4.20], p-value = 0.65). Conclusion: We concluded that the use of ICS decreased patient LOS and improved COVID-19-related symptoms. INC may have a role in improving the smell score. Therefore, using INC and ICS for two weeks or more may prove beneficial. Current data do not demonstrate an effect on mortality or time to sustained self-reported cure. However, the evidence is inconclusive, and more studies are needed for more precise data.

Stem Cell-based therapies for COVID-19-related acute respiratory distress syndrome.

As the number of confirmed cases and resulting death toll of the COVID-19 pandemic continue to increase around the globe - especially with the emergence of new mutations of the SARS-CoV-2 virus in addition to the known alpha, beta, gamma, delta and omicron variants - tremendous efforts continue to be dedicated to the development of interventive therapeutics to mitigate infective symptoms or post-viral sequelae in individuals for which vaccines are not accessible, viable or effective in the prevention of illness. Many of these investigations aim to target the associated acute respiratory distress syndrome, or ARDS, which induces damage to lung epithelia and other physiologic systems and is associated with progression in severe cases. Recently, stem cell-based therapies have demonstrated preliminary efficacy against ARDS based on a number of preclinical and preliminary human safety studies, and based on promising outcomes are now being evaluated in phase II clinical trials for ARDS. A number of candidate stem cell therapies have been found to exhibit low immunogenicity, coupled with inherent tropism to injury sites. In recent studies, these have demonstrated the ability to modulate suppression of pro-inflammatory cytokine signals such as those characterizing COVID-19-associated ARDS. Present translational studies are aiming to optimize the safety, efficacy and delivery to fully validate stem cell-based strategies targeting COVID-19 associated ARDS for viable clinical application.

Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification.

An arrhythmia happens when the electrical signals that organize the heartbeat do not work accurately. Most cases of arrhythmias may increase the risk of stroke or cardiac arrest. As a result, early detection of arrhythmia reduces fatality rates. This research aims to provide a lightweight multimodel based on convolutional neural networks (CNNs) that can transfer knowledge from many lightweight deep learning models and decant it into one model to aid in the diagnosis of arrhythmia by using electrocardiogram (ECG) signals. Thus, we gained a multimodel able to classify arrhythmia from ECG signals. Our system's effectiveness is examined by using a publicly accessible database and a comparison to the current methodologies for arrhythmia classification. The results we achieved by using our multimodel are better than those obtained by using a single model and better than most of the previous detection methods. It is worth mentioning that this model produced accurate classification results on small collection of data. Experts in this field can use this model as a guide to help them make decisions and save time.

Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model.

An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose various heart-related conditions in patients. An automated diagnostic tool is required to detect significant episodes in long-term ECG records. It is a very challenging task for cardiologists to analyze long-term ECG records in a short time. Therefore, a computer-based diagnosis tool is required to identify crucial episodes. Myocardial infarction (MI) and conduction disorders (CDs), sometimes known as heart blocks, are medical diseases that occur when a coronary artery becomes fully or suddenly stopped or when blood flow in these arteries slows dramatically. As a result, several researchers have utilized deep learning methods for MI and CD detection. However, there are one or more of the following challenges when using deep learning algorithms: (i) struggles with real-life data, (ii) the time after the training phase also requires high processing power, (iii) they are very computationally expensive, requiring large amounts of memory and computational resources, and it is not easy to transfer them to other problems, (iv) they are hard to describe and are not completely understood (black box), and (v) most of the literature is based on the MIT-BIH or PTB databases, which do not cover most of the crucial arrhythmias. This paper proposes a new deep learning approach based on machine learning for detecting MI and CDs using large PTB-XL ECG data. First, all challenging issues of these heart signals have been considered, as the signal data are from different datasets and the data are filtered. After that, the MI and CD signals are fed to the deep learning model to extract the deep features. In addition, a new custom activation function is proposed, which has fast convergence to the regular activation functions. Later, these features are fed to an external classifier, such as a support vector machine (SVM), for detection. The efficiency of the proposed method is demonstrated by the experimental findings, which show that it improves satisfactorily with an overall accuracy of 99.20% when using a CNN for extracting the features with an SVM classifier.

Large, Anionic Liposomes Enable Targeted Intraperitoneal Delivery of a TLR 7/8 Agonist To Repolarize Ovarian Tumors' Microenvironment.

Ovarian cancer is the most lethal gynecological malignancy in the United States. Current standard of treatment includes surgical debulking and chemotherapy, such as cisplatin and paclitaxel. However, the patients' response rate for chemotherapy in ovarian cancer is not optimal, and they often develop chemoresistance and suffer from side effects. Current clinical trials make extensive use of immune checkpoint blockade (ICB) as a novel cancer immunotherapeutic strategy against ovarian tumors. However, the response rates for ICB antibodies remain limited to 10-20% of treated ovarian cancer patients despite the success of this approach in melanoma, renal, head and neck, and nonsmall cell lung cancers. This lack of efficacy is often attributed to the "cold" immune status of ovarian tumors, as these tumors often have a low number of tumor-infiltrating lymphocytes (TILs) but a high number of suppressive immune cells, including tumor-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), or regulatory T cells (Tregs). Repolarizing TAMs could be a promising strategy to reshape the tumor immune microenvironment and promote antitumor activity when combined with ICBs. Toll-like receptor (TLR) 7 and 8 agonists, such as imiquimod and resiquimod, are potent immunostimulatory molecules with potential to repolarize macrophages. However, these small molecules have poor pharmacokinetic profiles and can induce severe side effects when administered systemically. Previously, our group demonstrated that various large, anionic nanomaterials (silica, PLGA, and polystyrene) specifically target TAMs when administered intraperitoneally (IP) to ovarian tumor-bearing mice. In the present study, we demonstrate that large, anionic liposomes administered IP also efficiently localize to TAMs and can be used to target the delivery of resiquimod. Resiquimod delivered in this targeted fashion promoted activation of M1 macrophages and T cell infiltration, while reducing the percentage of Tregs in the tumor microenvironment. Finally, liposome-formulated resiquimod significantly enhanced the efficacy of PD1 blockade against syngeneic ovarian tumors. We anticipate that further optimization of our liposomal delivery strategy can generate a clinically relevant strategy for more effective and safer immunotherapy for ovarian cancer patients.

Better outcome of COVID-19 positive kidney transplant recipients during the unremitting stage with optimized anticoagulation and immunosuppression.

INTRODUCTION: COVID-19 is an ongoing pandemic with high morbidity and mortality and with a reported high risk of severe disease in kidney transplant recipients (KTR). AIM: We aimed to report the largest number of COVID-19-positive cases in KTR in a single center and to discuss their demographics, management, and evolution. METHODS: We enrolled all the two thousand KTR followed up in our center in Kuwait and collected the data of all COVID-19-positive KTR (104) from the start of the outbreak till the end of July 2020 and have reported the clinical features, management details, and both patient and graft outcomes. RESULTS: Out of the one hundred and four cases reported, most of them were males aged 49.3 ± 14.7 years. Eighty-two of them needed hospitalization, of which thirty-one were managed in the intensive care unit (ICU). Main comorbidities among these patients were hypertension in 64.4%, diabetes in 51%, and ischemic heart disease in 20.2%. Management strategies included anticoagulation in 56.7%, withdrawal of antimetabolites in 54.8%, calcineurin inhibitor (CNI) withdrawal in 33.7%, the addition of antibiotics in 57.7%, Tocilizumab in 8.7%, and antivirals in 16.3%. During a follow-up of 30 days, the reported number of acute kidney injury (AKI) was 28.7%, respiratory failure requiring oxygen therapy 46.2%, and overall mortality rate was 10.6% with hospital mortality of 13.4% including an ICU mortality rate of 35.5%. CONCLUSION: Better outcome of COVID-19-positive KTR in our cohort during this unremitting stage could be due to the younger age of patients and early optimized management of anticoagulation, modification of immunosuppression, and prompt treatment of secondary bacterial infections. Mild cases can successfully be managed at home without any change in immunosuppression.

Influence of adding zeolite loaded with different charges to semen extender on sperm quality in rabbits after cryopreservation.

The aim of the present study was to investigate the effect of supplementing rabbit semen extender with zeolite loaded with different charges (Z+ or Z-, Z±) on sperm cryopreservation. Semen was collected from six healthy, fertile New Zealand rabbit bucks using an artificial vagina. The collected ejaculates were pooled and diluted with a tris-yolk fructose (TYF) extender supplemented with Z± (+16, +12, +8, -16, -12, and -8) at a concentration of 1% for a final sperm concentration of 25 × 106 sperm cells/mL. The diluted semen samples were then cryopreserved in 0.25 mL straws and stored in liquid nitrogen for 1 month. To evaluate sperm quality, we examined sperm progressive motility, vitality, morphological abnormalities, and plasma membrane integrity. In addition, apoptotic rates were determined using flow cytometry and by examining sperm ultrastructure under a transmission electron microscope (TEM). Moreover, total antioxidant capacity and markers of lipid peroxidation were measured in the extender after thawing. Addition of Z± had a positive effect on progressive motility, vitality, and membrane integrity after an equilibration period and post-thawing as compared with the controls (P < 0.05). Z± supplementation, particularly with a strong negative charge, also decreased the percentages of apoptotic and necrotic sperm cells compared to controls (P < 0.05), as shown both by flow cytometry and TEM. This was not associated with any marked effects on the oxidative biomarkers in the extender. In conclusion, addition of Z± to semen extender improved post-thawing sperm quality by improving sperm characteristics, decreasing apoptosis, and minimizing sperm damage during cryopreservation.

Influence of allicin administration on reproductive efficiency, immunity and lipid peroxidation of rabbit does under high ambient temperature.

This study investigated the effect of daily oral administration with allicin levels (0, 5 and 10 mg/kg of female body weight), 30 days pre-insemination, on reproductive performance in vivo and in vitro, immunity, and oxidative stress of rabbit does under high ambient temperature. Niliparous NZW does (n = 105) were randomly divided into three groups (35 in each) treated with 0, 5 and 10 mg allicin dissolved in 2 ml distilled water, respectively, for 30 days pre-insemination. At the end of treatment (30 days), does were artificially inseminated with fresh diluted semen of 20 fertile NZW bucks. Reproductive performance and ovulatory response parameters were determined. Serum biochemicals, enzyme activity, immunoglobulins (IgG and IgM) and antioxidant status were determined on day 30 of treatment. Serum progesterone and prolactin were determined pre-insemination (30 days of treatment), on 15 days of pregnancy and 7 days post-partum. Results showed that both allicin levels increased live litter size, and bunny viability rat and litter size at birth and weaning. Allicin levels increased ovulation rate and improved embryo quality. Number of total follicles decreased only with 10 mg allicin. Progesterone increased pre-insemination, 15 days of pregnancy and 7 days post-partum progesterone by allicin levels. Prolactin pre-insemination and on day 7 post-partum increased with 10 mg allicin. Serum total proteins, albumin, globulin, IgG and IgM increased, while glucose, aspartate and alanine aminotransaminases, and thiobarbituric acid reaction decreased by both allicin levels. In conclusion, the mechanism by which allicin administration 30 days pre-insemination to improve the reproductive performance of rabbit does is based on that allicin can play an important role, as a natural exogenous antioxidant, increasing immune response and reducing lipid peroxidation.

Does The Type of Shoulder Brace Affect Postoperative Pain and Clinical Outcome After Arthroscopic Rotator Cuff Repair?

PURPOSE: To compare postoperative pain and clinical outcome after arthroscopic rotator cuff repair in patients immobilized with an abduction brace versus patients placed in an antirotation brace. METHODS: One hundred twenty patients (72 women and 48 men) with symptomatic degenerative complete tear of the supraspinatus or infraspinatus tendons were included in the study. Exclusion criteria were history of trauma/traumatic tears, concomitant shoulder pathology, psychological illnesses, and previous shoulder surgery. The Constant-Murley score (CMS) was obtained before surgery and at 3 months, 6 months, and 1 year after surgery. The visual analogue scale (VAS) was done on the day of surgery and at 1 week, 3 weeks, 6 weeks, and 3 months after surgery. Postoperative isokinetic muscle strength evaluation was done for the shoulder external rotator at 1 year. RESULTS: Mean age was 50.4 years for the abduction brace group (ABG) and 50.8 years for the open pouch arm sling group (PASG). The mean level of the VAS score ranged from 75.6 for the ABG and 74.9 for the PASG on the day of the operation to 17.7 and 18.5 at 3 months after surgery. In both groups, the repeated measure analysis of variance showed that there were highly significant changes (P < .001) in the VAS from the day of operation to 3 months after surgery. A significant improvement was detected in both groups in the mean level of the CMS, from 40.9 for the ABG and 41.2 for the PASG before surgery to 84.7 and 84.5 at 1 year after the operation. There was no statistically significant difference between the 2 groups regarding the isokinetic muscle strength, VAS, or the CMS. CONCLUSIONS: Our study did not find a significant difference between abduction brace and antirotation sling in patient-oriented outcome measures or postoperative pain after rotator cuff repair. LEVEL OF EVIDENCE: Level II, prospective comparative study.

Prescribing statins among patients with type 2 diabetes: The clinical gap between the guidelines and practice.

BACKGROUND: Statins are recommended for cardiovascular protection for people with diabetes (high-risk groups). This study aimed to evaluate the gap between the guidelines of statin utilization and clinical practice among outpatients with type 2 diabetes regarding the patient's age and gender, to assess if this preventive drug is being satisfactorily utilized or not. MATERIALS AND METHODS: In this cross-sectional study, patients aged <40 or >75 years, pregnant patients, and patients with type 1 diabetes, human immunodeficiency virus, or liver cirrhosis were excluded. Demographics, laboratory parameters, and prevalence of exposure to statin therapy were evaluated. This study was guided by the 2013 American College of Cardiology/American Heart Association cholesterol guidelines. IBM SPSS software was used for data management. RESULTS: The study cohort involved 576 patients, with age being 58.3 ± 8.9 years. There were 50.5% of females and 49.5% of males. Overall 81.1% of patients aged 58.8 ± 8.8 years were statin users and 18.9% of patients aged 56.2 ± 9 years were statin nonusers. About 83.2% of females and 78.9% of males were prescribed statins. Statin medications included simvastatin 79.2%, atorvastatin 11.6%, lovastatin 5.8%, rosuvastatin 2.1%, and pravastatin 1.3%. Statin users' and nonusers' adherence was 56.5%, and 41.3% (P = 0.004), respectively. The adherence to medication plan of females and males was 55.7% and 51.6%, respectively (P = 0.004). CONCLUSION: Patients with diabetes who are at high risk of cardiovascular events, exposure to statin treatment is significantly less than perfect position both in females and males. Nearly one-fifth of the patients with type 2 diabetes are not using statins despite therapeutic necessities.