A PRACTICAL RISK-BASED APPROACH TO ASSESS VIAL'S DIMENSIONS DEVIATIONS EFFECT ON THE ASEPTIC FILLING PROCESSING, ACCORDING TO ICH Q9 GUIDELINE

Objective: Qualitative risk assessment process is a new topic in the pharmaceutical industries. The main outcome of the risk assessment implementation is to help the manufacturers for a better decision-making, in a case that a quality problem arises. According to the ISO documents;  vials used in the pharmaceutical industry have a special dimension specification and Quality Control analytical results should prove that the vial samples are in the defined range. Nevertheless, the value of these tests is not the same as defined ISO specifications;  and this may have minor and/or significant impact on the final product quality.Methods: The purpose of this qualitative study was to rank the results of the vial dimention tests based on quality risk assessment. Consequently, these rankings can help to decide whether the dimension deviation from quality specification of vials is acceptable and what will be the impact of accepting the risk on the final product safety and finally how to decrease the risk.For this purpose, we consider the final product contamination could be one of the main indicators for the quality as the contamination from packaging materials in particular are more important when aseptic processing run.Results: Dimensions that are directly associated with opening the vial containing d2, d3, d4 and h4 that they affect rubber sealing and capping. Other dimensions like h1, h2, h3 and d1 affect rubber sealing and capping indirectly. Therefore, these two groups of deviations have a very high probability of contamination.Â

A Scanning Electron Microscope Study on the Effect of an Experimental Irrigation Solution on Smear Layer Removal

Introduction: The aim of this in vitro study was to evaluate the effect of an experimental irrigation solution, containing two different concentrations of papain, Tween 80, 2% chlorhexidine and EDTA, on removal of the smear layer. Methods and Materials: Thirty-six single-rooted teeth were divided into two experimental groups (n=12) and two positive and negative control groups of six. The canals were prepared with BioRaCe instruments up to BR7 (60/0.02). In group 1, canals were irrigated with a combination of 1% papain, 17% EDTA, Tween 80 and 2% CHX; in group 2, canals were irrigated with a combination of 0.1% papain, 17% EDTA, Tween 80 and 2% CHX. In group 3 (the negative control), the canal was irrigated with 2.5% NaOCl during instrumentation and at the end of preparation with 1 mL of 17% EDTA was used; in group 4 (positive control), normal saline was used for irrigation. The amount of the remaining smear layer was quantified according to Hulsmann method using scanning electron microscopy (SEM). Data was analyzed by the Kruskal-Wallis and Mann-Whitney tests. Results: Two-by-two comparisons of the groups revealed no significant differences in terms of smear layer removal at different canal sections between the negative control group (standard regiment for smear layer removal) and 1% papain groups (P<0.05). Conclusion: Under the limitations of the present study, combination of 1% papain, EDTA, 2% chlorhexidine and Tween 80 can effectively remove smear layer from canal walls

2nd National Congress on Clinical Case Reports, December 26 and 27, 2018

The second annual meeting of Clinical Case Report (CCR) has been held in Karaj, Iran from the 26th to the 27th of December, 2018 (Figure 1). The congress was organized by the Clinical Research Development Center of Shahid Rajaei Educational and Therapeutic Center, Alborz University of Medical Sciences together (Figure 2), with a Scientific Committee including some of the faculty members of the university (Table 1). The conference program was organized into the following sessions: · Cardiovascular · Nursing · Pediatrics · Obstetrics and Gynecology · Internal Medicine · Surgery · Urology · Neurology and Neurosurgery · Orthopedics · Psychiatry · Laboratory Sciences · Infectious diseases · Traditional Medicine This meeting brought together clinician and researchers from several prestigious universities and research centers throughout Iran including Rasht, Torbat Heidarieh, Qazvin, Neyshahpour, Ardebil, Isfahan, Khorramabad, Tabriz, Hamedan, Marand, Bushehr, Mashhad, Ahvaz, Sanandaj, Bojnourd, Sabzevar, Kashan, Gorgan, Ilam, Dezful, Yazd, Tehran, Urmia and Semnan, as well as leading researchers from countries such as Turkey. Participants were invited to submit scientific contributions, as oral presentations or posters. After evaluation of the 858 abstracts received, the Scientific Committee selected 40 of them for oral presentations, and accepted 231 as posters

Gastrointestinal Parasites of Domestic Mammalian Hosts in Southeastern Iran

Gastrointestinal parasites (GIP) are a major cause of disease and production loss in livestock. Some have zoonotic potential, so production animals can be a source of human infections. We describe the prevalence of GIP in domestic mammals in Southeastern Iran. Fresh fecal samples (n = 200) collected from cattle (n = 88), sheep (n = 50), goats (n = 23), camels (n = 30), donkeys (n = 5), horse (n = 1), and dogs (n = 3) were subjected to conventional coprological examination for the detection of protozoan (oo)cysts and helminth ova. Overall, 83% (166/200) of the samples were positive for one or more GIP. Helminths were found in dogs, donkeys, sheep (42%), camels (37%), goats (30%), and cattle (19%), but not in the horse. Protozoa were found in cattle (82%), goats (78%), sheep (60%), and camels (13%), but not in donkeys, dogs, or the horse. Lambs were 3.5 times more likely to be infected by protozoa than sheep (OR = 3.5, 95% CI: 1.05-11.66), whereas sheep were at higher odds of being infected by helminths than lambs (OR = 4.09, 95% CI: 1.06-16.59). This is the first study assessing the prevalence of GIP in domestic mammals in Southeastern Iran.This research was funded by the Office of Vice-chancellor for Research of Iranshahr University of Medical Sciences (Grant No. 9900039) and within the scope of the project CICECOAveiro Institute of Materials, UIDB/50011/2020, UIDP/50011/2020 & LA/P/0006/2020, financed by national funds through the FCT/MEC (PIDDAC).S

Precision Diagnostics in Cardiac Tumours:Integrating Echocardiography and Pathology with Advanced Machine Learning on Limited Data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25% and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94% in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

Precision Diagnostics in Cardiac Tumours:Integrating Echocardiography and Pathology with Advanced Machine Learning on Limited Data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25% and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94% in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

The Hospitalization Rate of Cerebral Venous Sinus Thrombosis before and during COVID-19 Pandemic Era: A Single-Center Retrospective Cohort Study

Objectives: There are several reports of the association between SARS-CoV-2 infection (COVID-19) and cerebral venous sinus thrombosis (CVST). In this study, we aimed to compare the hospitalization rate of CVST before and during the COVID-19 pandemic (before vaccination program). Materials and methods: In this retrospective cohort study, the hospitalization rate of adult CVST patients in Namazi hospital, a tertiary referral center in the south of Iran, was compared in two periods of time. We defined March 2018 to March 2019 as the pre-COVID-19 period and March 2020 to March 2021 as the COVID-19 period. Results: 50 and 77 adult CVST patients were hospitalized in the pre-COVID-19 and COVID-19 periods, respectively. The crude CVST hospitalization rate increased from 14.33 in the pre-COVID-19 period to 21.7 per million in the COVID-19 era (P = 0.021). However, after age and sex adjustment, the incremental trend in hospitalization rate was not significant (95% CrI: -2.2, 5.14). Patients \u3e 50-year-old were more often hospitalized in the COVID-19 period (P = 0.042). SARS-CoV-2 PCR test was done in 49.3% out of all COVID-19 period patients, which were positive in 6.5%. Modified Rankin Scale (mRS) score ≥3 at three-month follow-up was associated with age (P = 0.015) and malignancy (P = 0.014) in pre-COVID period; and was associated with age (P = 0.025), altered mental status on admission time (P\u3c0.001), malignancy (P = 0.041) and COVID-19 infection (P = 0.008) in COVID-19 period. Conclusion: Since there was a more dismal outcome in COVID-19 associated CVST, a high index of suspicion for CVST among COVID-19 positive is recommended

Precision diagnostics in cardiac tumours: Integrating echocardiography and pathology with advanced machine learning on limited data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25 % and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94 % in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation