Hanan Hassan Fouad

INTRODUCTION: Ulcerative colitis (UC) is a chronic disease that alters the colonic and rectal mucosa. The high prevalence rates of UC make it a worldwide healthcare problem. However, its underlying molecular mechanisms remain vague.

AIM OF THE STUDY: To investigate the molecular mechanisms underlying UC and to study the cross-talk among the regulatory role of the lncRNAs UCA1, CRNDE, and miR-145 on TLR4/NF-κB/TNF-α signaling pathway. Moreover, the study was extended to examine the beneficial effects of 3,3-Diindolylmethane (DIM) on relieving UC.

METHODS: UC was induced in rats by injecting 2 ml of 4% acetic acid (AA) solution transrectally. After 24 h, rats were treated with either DIM (20 mg/kg) or sulphasalazine (SSZ) (500 mg/kg) orally for 7 days.

RESULTS: The present study revealed that the gene expression of the lncRNAs UCA1 and CRNDE were significantly upregulated in the AA-induced UC model compared with the control group, whereas miR-145 was significantly downregulated. There was a significant association between the expression of these non-coding RNAs and TLR4/ NF-κB/TNF-α axis as well as malondialdehyde and glutathione levels. Favorably, the DIM-treated group showed significant downregulation of the lncRNAs UCA1 and CRNDE along with upregulated miR-145 compared with the AA-induced UC model. Furthermore, DIM showed remarkable inhibition of the TLR4/ NF-κB /TNF-α cascade compared with non-treated UC rats.

CONCLUSIONS: The present study is the first to document the interrelated role of the lncRNAs UCA1 and CRNDE in UC via orchestrating miR-145/TLR4/ NF-κB /TNF-α inflammatory cascade. Furthermore, the study demonstrated a new molecular basis for the pleiotropic activities of DIM in relieving UC.

INTRODUCTION: Dilated cardiomyopathy (DCM) is common in Great Danes (GDs) but screening for this condition can be challenging. We hypothesised that cardiac troponin-I (cTnI) concentration is elevated in GDs with DCM and/or ventricular arrhythmias (VAs), and is associated with reduced survival time in GDs.

ANIMALS: One hundred and twenty-four client-owned GDs assigned echocardiographically as normal (n = 53), equivocal (n = 37), preclinical DCM (n = 21), or clinical DCM (n = 13).

MATERIALS AND METHODS: A retrospective epidemiological study. Echocardiographic diagnosis, VAs, and contemporaneous cTnI concentrations were recorded. Diagnostic accuracy and cTnI cut-offs were determined with receiver operating characteristic analyses. Effects of the cTnI concentration and disease status on survival and cause of death were explored.

RESULTS: Median cTnI was greater in clinical DCM (0.6 ng/mL [25th-75th percentiles: 0.41-1.71 ng/mL]) and GDs with VAs (0.5 ng/mL [0.27-0.80 ng/mL], P<0.001). Elevated cTnI detected these dogs with good accuracy (area under the curve: 0.78-0.85; cut-offs 0.199-0.34 ng/mL). Thirty-eight GDs (30.6%) suffered a cardiac death (CD); GDs suffering CD (0.25 ng/mL [0.21-0.53 ng/mL]) and specifically sudden cardiac death (SCD) (0.51 ng/mL [0.23-0.72 ng/mL]) had higher cTnI than GDs dying of other causes (0.20 ng/mL [0.14-0.35 ng/mL]; P<0.001). Elevated cTnI (>0.199 ng/mL) was associated with shorter long-term survival (1.25 years) and increased risk of SCD. Great Danes with VAs had shorter survival times (0.97 years).

CONCLUSIONS: A cardiac troponin-I concentration is a useful adjunctive screening tool. Elevated cTnI is a negative prognostic indicator.

Limited evidence on long-term COVID-19 vaccine safety in patients with idiopathic inflammatory myopathies (IIMs) continues to contribute to vaccine hesitancy. We studied delayed-onset vaccine adverse events (AEs) in patients with IIMs, other systemic autoimmune and inflammatory disorders (SAIDs), and healthy controls (HCs), using data from the second COVID-19 Vaccination in Autoimmune Diseases (COVAD) study. A validated self-reporting e-survey was circulated by the COVAD study group (157 collaborators, 106 countries) from Feb-June 2022. We collected data on demographics, comorbidities, IIM/SAID details, COVID-19 history, and vaccination details. Delayed-onset (> 7 day) AEs were analyzed using regression models. A total of 15165 respondents undertook the survey, of whom 8759 responses from vaccinated individuals [median age 46 (35-58) years, 74.4% females, 45.4% Caucasians] were analyzed. Of these, 1390 (15.9%) had IIMs, 50.6% other SAIDs, and 33.5% HCs. Among IIMs, 16.3% and 10.2% patients reported minor and major AEs, respectively, and 0.72% (n = 10) required hospitalization. Notably patients with IIMs experienced fewer minor AEs than other SAIDs, though rashes were expectedly more than HCs [OR 4.0; 95% CI 2.2-7.0, p < 0.001]. IIM patients with active disease, overlap myositis, autoimmune comorbidities, and ChadOx1 nCOV-19 (Oxford/AstraZeneca) recipients reported AEs more often, while those with inclusion body myositis, and BNT162b2 (Pfizer) recipients reported fewer AEs. Vaccination is reassuringly safe in individuals with IIMs, with AEs, hospitalizations comparable to SAIDs, and largely limited to those with autoimmune multimorbidity and active disease. These observations may inform guidelines to identify high-risk patients warranting close monitoring in the post-vaccination period.

BACKGROUND: The COVID-19 pandemic has left an indelible effect on healthcare delivery and education system, including residency training. Particularly, neurosurgical departments worldwide had to adapt their operating model to the constantly changing pandemic landscape. This review aimed to quantify the reduction in neurosurgical operative volume and describe the impact of these trends on neurosurgical residency training.

METHODS:  We performed a comprehensive search of PubMed and EMBASE between December 2019 and October 2022 to identify studies comparing pre-pandemic and pandemic neurosurgical caseloads as well as articles detailing the impact of COVID-19 on neurosurgery residency training. Statistical analysis of quantitative data was presented as pooled odds ratio (OR) and 95% confidence intervals (CI).

RESULTS: A total of 49 studies met the inclusion criteria, of which 12 (24.5%) were survey-based. The case volume of elective surgeries and non-elective procedures decreased by 70.4% (OR=0.296, 95%CI 0.210-0.418) and 68.2% (OR=0.318, 95%CI 0.193-0.525), respectively. A significant decrease was also observed in functional (OR=0.542, 95%CI 0.394-0.746), spine (OR=0.545, 95%CI 0.409-0.725), and skull base surgery (OR=0.545, 95%CI 0.409-0.725), whereas the caseloads for tumor (OR=1.029, 95%CI 0.838-1.263), trauma (OR=1.021, 95%CI 0.846-1.232), vascular (OR=1.001, 95%CI 0.870-1.152), and pediatric neurosurgery (OR=0.589, 95%CI 0.344-1.010) remained relatively the same between pre-pandemic and pandemic periods. The reduction in caseloads had caused concerns among residents and program directors in regard to the diminished clinical exposure, financial constraints, and mental well-being. Some positives highlighted were rapid adaptation to virtual educational platforms and increasing time for self-learning and research activities.

CONCLUSION: While COVID-19 has brought about significant disruptions in neurosurgical practice and training, this unprecedented challenge has opened the door for technological advances and collaboration that broaden the accessibility of resources and reduce the worldwide gap in neurosurgical education.

BACKGROUND: The COVID-19 pandemic has left an indelible effect on healthcare delivery and education system, including residency training. Particularly, neurosurgical departments worldwide had to adapt their operating model to the constantly changing pandemic landscape. This review aimed to quantify the reduction in neurosurgical operative volume and describe the impact of these trends on neurosurgical residency training.

METHODS: We performed a comprehensive search of PubMed and EMBASE between December 2019 and October 2022 to identify studies comparing pre-pandemic and pandemic neurosurgical caseloads as well as articles detailing the impact of COVID-19 on neurosurgery residency training. Statistical analysis of quantitative data was presented as pooled odds ratio (OR) and 95% confidence intervals (CI).

RESULTS: A total of 49 studies met the inclusion criteria, of which 12 (24.5%) were survey-based. The case volume of elective surgeries and non-elective procedures decreased by 70.4% (OR=0.296, 95%CI 0.210-0.418) and 68.2% (OR=0.318, 95%CI 0.193-0.525), respectively. A significant decrease was also observed in functional (OR=0.542, 95%CI 0.394-0.746), spine (OR=0.545, 95%CI 0.409-0.725), and skull base surgery (OR=0.545, 95%CI 0.409-0.725), whereas the caseloads for tumor (OR=1.029, 95%CI 0.838-1.263), trauma (OR=1.021, 95%CI 0.846-1.232), vascular (OR=1.001, 95%CI 0.870-1.152), and pediatric neurosurgery (OR=0.589, 95%CI 0.344-1.010) remained relatively the same between pre-pandemic and pandemic periods. The reduction in caseloads had caused concerns among residents and program directors in regard to the diminished clinical exposure, financial constraints, and mental well-being. Some positives highlighted were rapid adaptation to virtual educational platforms and increasing time for self-learning and research activities.

CONCLUSION: While COVID-19 has brought about significant disruptions in neurosurgical practice and training, this unprecedented challenge has opened the door for technological advances and collaboration that broaden the accessibility of resources and reduce the worldwide gap in neurosurgical education.

Hybrid flowshops are a special type of manufacturing systems, in which a stage may contain identical or unrelated parallel machines. This paper deals with a more practical approach for lot streaming hybrid flowshop in which the sublot sizes of jobs can vary from one stage to the next according to machines `speed. Two models of mixed-integer nonlinear programming are developed to minimise the make-span of two different hybrid flowshop systems. The first model deals with unrelated parallel machines with eligibility, independent setup time, and variable sublot sizes. The second model is a special case of the hybrid flowshop as it consists of multi-stages comprising one machine at the stages preceding the final stage, while the final stage includes unrelated parallel machines. The first model was studied and the data gathered were analyzed using ANOVA test to evaluate the factors’ effect on system. The factors are number of jobs, maximum number of batches, setup time, and machine’s configuration. The analysis revealed that all the factors were effective. The second model was compared to benchmarking published paper and it gets better results.