Histopathological and Biochemical Evaluation of Albendazole in the Treatment of Infected Mice with Hydatid Cyst

Abstract Introduction: Hydatidosis, caused by the larval stage of Echinococcus granulosus, is a prevalent parasitic disease affecting both humans and animals. Albendazole is currently the most effective drug for treating hydatid cysts. This research aimed to investigate the histopathological and biochemical effects of Albendazole on the liver, lung, and kidney of mice experimentally infected by hydatid cysts. Materials and methods: A total of 20 mice weighing approximately 220 g were used. The rats were randomly divided into the Albendazole group (100 mg/kg/day) and the control group (infected Rats without treatment). At the end of the experiment, tissue samples from the liver, lung, and kidney were collected for histopathological evaluation. Liver blood tests were used to assess liver functions or liver injury (alkaline phosphatase, alanine aminotransferase, and bilirubin). Results: After 30 days of daily treatment, the total numbers of cysts, size, and weight of the largest cyst were significantly lower in the Albendazole group, compared to the control group. The study addressed histopathological changes in the liver, kidneys, and lungs caused by hydatid cysts, such as tissue necrosis, hemorrhage, and local inflammation, indicating the potential for serious complications and significant damage to these organs. The group treated with Albendazole showed severe histopathological changes in the liver, kidneys, and lungs, compared to the control group. This suggests that Albendazole may trigger a more aggressive response in these organs to the cysts, leading to increased tissue damage. In addition, alkaline phosphatase, alanine aminotransferase, and bilirubin concentrations revealed a significant increase in the Albendazole group. Conclusion: While Albendazole is an effective drug for treating hydatidosis, it can also cause severe side effects on various organs in the body. Therefore, alternative treatment strategies need to be developed to minimize these adverse effects. https://jlar.rovedar.com/index.php/JLAR/article/view/

Review of coreference resolution in English and Persian

Coreference resolution (CR) is one of the most challenging areas of natural language processing. This task seeks to identify all textual references to the same real-world entity. Research in this field is divided into coreference resolution and anaphora resolution. Due to its application in textual comprehension and its utility in other tasks such as information extraction systems, document summarization, and machine translation, this field has attracted considerable interest. Consequently, it has a significant effect on the quality of these systems. This article reviews the existing corpora and evaluation metrics in this field. Then, an overview of the coreference algorithms, from rule-based methods to the latest deep learning techniques, is provided. Finally, coreference resolution and pronoun resolution systems in Persian are investigated.Comment: 44 pages, 11 figures, 5 table

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

Background Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. Methods We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). Findings In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683–0·717]). Interpretation In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. Funding British Journal of Surgery Society