Background: Heart failure (HF) is a common final pathway of various insults to the heart, primarily from risk factors including diabetes mellitus (DM) type 2. This study analyzed the clinical characteristics of HF in a Jordanian population with a particular emphasis on the relationship between DM and HF. Methods: This prospective study used the Jordanian Heart Failure Registry (JoHFR) data. Patients with HF were characterized by DM status and HF type: HF with preserved ejection fraction (HFpEF) or HF with reduced ejection fraction (HFrEF). Demographics, clinical presentations, and treatment outcomes were collected. Statistical analyses and machine learning techniques were carried out for the prediction of mortality among HF patients: Recursive Feature Elimination with Cross-Validation (RFECV) and Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors (SMOTEENN) were employed. Results: A total of 2007 patients with HF were included. Notable differences between diabetic and non-diabetic patients are apparent. Diabetic patients were predominantly male, older, and obese (p < 0.001 for all). A higher incidence of HFpEF was observed in the diabetes cohort (p = 0.006). Also, diabetic patients had significantly higher levels of cholesterol (p = 0.008) and LDL (p = 0.003), reduced hemoglobin levels (p < 0.001), and more severe renal impairment (eGFR; p = 0.006). Machine learning models, particularly the Random Forest Classifier, highlighted its superiority in mortality prediction, with an accuracy of 90.02% and AUC of 80.51%. Predictors of mortality included creatinine levels >115 mu mol/L, length of hospital stay, and need for mechanical ventilation. Conclusion: This study underscores notable differences in clinical characteristics and outcomes between diabetic and non-diabetic heart failure patients in Jordan. Diabetic patients had higher prevalence of HFpEF and poorer health indicators such as elevated cholesterol, LDL, and impaired kidney function. High creatinine levels, longer hospital stays, and the need for mechanical ventilation were key predictors of mortality.

Background: Heart failure (HF) is a global health challenge affecting millions, with significant variations in patient characteristics and outcomes based on ejection fraction. This study aimed to differentiate between HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF) with respect to patient characteristics, risk factors, comorbidities, and clinical outcomes, incorporating advanced machine learning models for mortality prediction. Methodology: The study included 1861 HF patients from 21 centers in Jordan, categorized into HFrEF (EF <40%) and HFpEF (EF >= 50%) groups. Data were collected from 2021 to 2023, and machine learning models were employed for mortality prediction. Results: Among the participants, 29.7% had HFpEF and 70.3% HFrEF. Significant differences were noted in demographics and comorbidities, with a higher prevalence of males, younger age, smoking, and familial history of premature ASCVD in the HFrEF group. HFpEF patients were typically older, with higher rates of diabetes, hypertension, and obesity. Machine learning analysis, mainly using the Random Forest Classifier, demonstrated significant predictive capability for mortality with an accuracy of 0.9002 and an AUC of 0.7556. Other models, including Logistic Regression, SVM, and XGBoost, also showed promising results. Length of hospital stay, need for mechanical ventilation, and number of hospital admissions were the top predictors of mortality in our study. Conclusion: The study underscores the heterogeneity in patient profiles between HFrEF and HFpEF. Integrating machine learning models offers valuable insights into mortality risk prediction in HF patients, highlighting the potential of advanced analytics in improving patient care and outcomes.

Background:Cabozantinib, a new first-line treatment for advanced renal cell carcinoma (aRCC), targets essential tyrosine kinases and outperforms the established comparator (sunitinib) in various efficacy outcomes. This systematic review and meta-analysis aimed to assess the efficacy and safety of cabozantinib compared to other aRCC treatments. Methods:Following PRISMA and Cochrane guidelines, our protocol was registered in PROSPERO. A systematic search, without date limits, was conducted on PubMed, Cochrane, Web of Science, and EMBASE until October 8, 2023. Data extraction encompassed study details, baseline information, and outcomes. Hazard ratios (HR) and risk ratios (RR) with 95% confidence intervals were employed for each outcome, and a random-effects model was applied to account for expected heterogeneity. Results:Three studies, encompassing 967 patients, were included in our analysis. In terms of efficacy, the pooled rate for overall survival significantly favored cabozantinib. However, in subgroup analyses, cabozantinib was only statistically superior to everolimus. For progression-free survival and tumor objective response rate, cabozantinib outperformed both everolimus and sunitinib. In adverse events, compared to sunitinib, cabozantinib exhibited inferiority in nearly all evaluated aspects, except for nausea and stomatitis, which showed no difference between the two groups. Conversely, it demonstrated a comparable risk profile with everolimus across various side effects. Conclusion:Cabozantinib shows significant efficacy in extending overall survival, progression-free survival, and tumor objective response rate despite a potentially higher risk of adverse events compared to sunitinib. These findings support cabozantinib as a first-line therapy for aRCC, either as an initial treatment or after prior VEGFR-targeted therapies.

This study aimed to introduce a novel machine learning (ML) model designed to predict the need of interventions during endoscopy in patients with upper gastrointestinal bleeding (UGIB). The risk stratification tools in current use, such as the Glasgow Blatchford Score (GBS) and pre-endoscopic Rockall score, have limitations in accurately predicting the need for endoscopic interventions. All patients diagnosed with UGIB from January 2013 to October 2023 who underwent endoscopy were included in the study. Variables extracted included demographics, social history ,clinical history, clinical presentation and symptoms, drug history, management prior to endoscopy, findings during endoscopy, laboratory variables and vitals, and post-endoscopic results. Three machine learning models including Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), and AdaBoost Classifier were used to evaluate the need for intervention during endoscopy in our study. A total of 1372 patients were included from three major hospitals in Jordan, of whom 242 underwent endoscopic intervention. The GBC outperformed all other models and pre-endoscopic scores in predicting the need for endoscopic intervention with an area under the curve of 0.861. Findings during endoscopy, platelet count, pulse rate and systolic blood pressure during admission were the most contributing features in predicting the need for the need of endoscopic intervention. This study highlights the potential of machine learning models in enhancing decision making for UGIB management.

Glioblastoma (GBM) represents a profoundly aggressive and heterogeneous brain neoplasm linked to a bleak prognosis. Hypoxia, a common feature in GBM, has been linked to tumor progression and therapy resistance. In this study, we aimed to identify hypoxia-related differentially expressed genes (DEGs) and construct a prognostic signature for GBM patients using multi-omics analysis. Patient cohorts were collected from publicly available databases, including the Gene Expression Omnibus (GEO), the Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas—Glioblastoma Multiforme (TCGA-GBM), to facilitate a comprehensive analysis. Hypoxia-related genes (HRGs) were obtained from the Molecular Signatures Database (MSigDB). Differential expression analysis revealed 41 hypoxia-related DEGs in GBM patients. A consensus clustering approach, utilizing these DEGs’ expression patterns, identified four distinct clusters, with cluster 1 showing significantly better overall survival. Machine learning techniques, including univariate Cox regression and LASSO regression, delineated a prognostic signature comprising six genes (ANXA1, CALD1, CP, IGFBP2, IGFBP5, and LOX). Multivariate Cox regression analysis substantiated the prognostic significance of a set of three optimal signature genes (CP, IGFBP2, and LOX). Using the hypoxia-related prognostic signature, patients were classified into high- and low-risk categories. Survival analysis demonstrated that the high-risk group exhibited inferior overall survival rates in comparison to the low-risk group. The prognostic signature showed good predictive performance, as indicated by the area under the curve (AUC) values for one-, three-, and five-year overall survival. Furthermore, functional enrichment analysis of the DEGs identified biological processes and pathways associated with hypoxia, providing insights into the underlying mechanisms of GBM. Delving into the tumor immune microenvironment, our analysis revealed correlations relating the hypoxia-related prognostic signature to the infiltration of immune cells in GBM. Overall, our study highlights the potential of a hypoxia-related prognostic signature as a valuable resource for forecasting the survival outcome of GBM patients. The multi-omics approach integrating bulk sequencing, single-cell analysis, and immune microenvironment assessment enhances our understanding of the intricate biology characterizing GBM, thereby potentially informing the tailored design of therapeutic interventions.

Introduction: Heart failure (HF) and atrial fibrillation (AF) are constantly linked together as predictors of a substantial increase in morbidity and mortality. In this study, we investigated the effects of atrial fibrillation in patients with heart failure. Methods: This study was a prospective observational multicenter national registry encompassing 21 health institutes in Jordan, comprising university hospitals, private hospitals, and private clinics. Patients visiting the cardiology clinic or inpatients admitted due to acute decompensated HF were included. The collected variables included age, sex, BMI, comorbidities, HDL, LDL, triglycerides, BNP, Sodium, potassium, hemoglobin, and creatinine. Results: Our study of 1571 patients showed significant differences between those with and without atrial fibrillation (AF). AF patients included more females (49.4% vs 34.0%), had a higher prevalence of hypertension (88.0% vs 78.5%), and were older (57.8% aged >= 70 years). Smoking rates were lower in patients with AF (22.3% vs 37.0%), while dyslipidemia was less common (54.5% vs 65.3%). Patients with AF also had more hospital admissions than those without AF (16% vs 11.6%). In addition, triglyceride levels were notably lower, hemoglobin levels were < 10 g/dL, and eGFR was reduced in patients with AF. In predicting death, the Random Forest Classifier had the highest accuracy (93.02%) and AUC (92.51%), whereas Logistic Regression had higher sensitivity (72.09%). Creatinine, Length of Hospital Stay, and other factors influenced the predictions, with creatinine levels being a strong predictor of patient outcomes. Conclusion: Atrial fibrillation patients were older and had a higher proportion of females compared than non-atrial fibrillation patients. Hypertension, a family history of premature coronary artery disease, and structural heart disease were notably higher in the atrial fibrillation group. Patients with atrial fibrillation had higher rates of hospital admissions than those without atrial fibrillation.

Background and Objectives: This study aims to evaluate the association between the use of oral isotretinoin and menstrual irregularities in acne patients with previously regular menstrual cycles. Materials and Methods: A prospective observational study was conducted on 58,599 female patients aged 14 to 36 at King Abdullah University Hospital in Irbid, Jordan. The patients were followed for a period of 4.5 to 8 months during treatment and for 2 months post-treatment. Menstrual cycle changes were documented, and statistical analysis was performed to identify any significant associations. Results: A total of 111 (37.1%) patients, who were previously known to have regular menstrual cycles, complained of menstrual changes while using oral isotretinoin. Ninety-nine of those patients who complained of menstrual changes had their cycles back to normal post-treatment. There is a significant difference in the total accumulative dose between those with changes in menses and those without; p-value [0.008]. The most common change that occurred was amenorrhea (p < 0.001), followed by oligomenorrhea and menorrhagia (p < 0.001 and p = 0.050, respectively). The duration of treatment was a significant predictor of menstrual irregularities, with an odds ratio (OR) of 5.106 (95% CI: 1.371–19.020, p = 0.015), indicating a higher likelihood of menstrual changes with increased treatment duration. The total accumulative dose was also significantly associated with menstrual irregularities (OR = 0.964; 95% CI: 0.939–0.990; p = 0.006). Additionally, a family history of PCOS significantly increased the odds of menstrual irregularities (OR = 3.783; 95% CI: 1.314–10.892; p = 0.014). Conclusions: The study identified that 37.1% of the participants experienced changes in their menstrual cycles while undergoing isotretinoin therapy, with the vast majority (89.2%) returning to normal within two months post-treatment. Our logistic regression analysis pinpointed the duration of isotretinoin treatment, the total accumulative dose, and a family history of PCOS as significant predictors of menstrual irregularities.

Background and Objectives: Heart failure (HF) is a prevalent and debilitating condition that imposes a significant burden on healthcare systems and adversely affects the quality of life of patients worldwide. Comorbidities such as chronic kidney disease (CKD), arterial hypertension, and diabetes mellitus (DM) are common among HF patients, as they share similar risk factors. This study aimed to identify the prognostic significance of multiple factors and their correlation with disease prognosis and outcomes in a Jordanian cohort. Materials and Methods: Data from the Jordanian Heart Failure Registry (JoHFR) were analyzed, encompassing medical records from acute and chronic HF patients attending public and private cardiology clinics and hospitals across Jordan. An online form was utilized for data collection, focusing on three kidney function tests, estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), and creatinine levels, with the eGFR calculated using the Cockcroft–Gault formula. We also built six machine learning models to predict mortality in our cohort. Results: From the JoHFR, 2151 HF patients were included, with 644, 1799, and 1927 records analyzed for eGFR, BUN, and creatinine levels, respectively. Age negatively impacted all measures (p ≤ 0.001), while smokers surprisingly showed better results than non-smokers (p ≤ 0.001). Males had more normal eGFR levels compared to females (p = 0.002). Comorbidities such as hypertension, diabetes, arrhythmias, and implanted devices were inversely related to eGFR (all with p-values <0.05). Higher BUN levels were associated with chronic HF, dyslipidemia, and ASCVD (p ≤ 0.001). Higher creatinine levels were linked to hypertension, diabetes, dyslipidemia, arrhythmias, and previous HF history (all with p-values <0.05). Low eGFR levels were associated with increased mechanical ventilation needs (p = 0.049) and mortality (p ≤ 0.001), while BUN levels did not significantly affect these outcomes. Machine learning analysis employing the Random Forest Classifier revealed that length of hospital stay and creatinine >115 were the most significant predictors of mortality. The classifier achieved an accuracy of 90.02% with an AUC of 80.51%, indicating its efficacy in predictive modeling. Conclusions: This study reveals the intricate relationship among kidney function tests, comorbidities, and clinical outcomes in HF patients in Jordan, highlighting the importance of kidney function as a predictive tool. Integrating machine learning models into clinical practice may enhance the predictive accuracy of patient outcomes, thereby supporting a more personalized approach to managing HF and related kidney dysfunction. Further research is necessary to validate these findings and to develop innovative treatment strategies for the CKD population within the HF cohort.

The emergence of telemedicine and artificial intelligence (AI) has set the stage for a possible revolution in the future of medicine and neurology including the diagnosis and management of epilepsy. Telemedicine, with its proven efficacy during the COVID-19 pandemic, offers the advantage of bridging the gap between patients in resource-limited areas and specialized care, where in one study telemedicine reduced the epilepsy treatment gap from 43% to 9%. AI innovations promise a transformation in epilepsy care by possibly enhancing the accuracy of electroencephalogram (EEG) interpretation and seizure prediction through machine and deep learning. In one study, abnormal EEG recordings were classified into different categories using a convolutional neural networks (CNN) model showing a specificity of 90% and an accuracy of 88.3%. Other models constructed to predict seizures have also achieved a sensitivity of 96.8% and specificity of 95.5%. Various machine learning (ML) models highlight the potential AI holds in identifying interictal biomarkers and localizing seizure onset zones aiding in epilepsy treatment decision and outcome prediction. An ML model highlighted in this review localized seizure onset zone with an accuracy reaching 73% and predicted surgical outcomes with an accuracy reaching 79% compared to the 43% accuracy of clinicians. However, limitations and challenges hinder the application of such technologies to reach their full potential in epilepsy care. Limitations include access to compatible devices, integration into clinical workflows, data bias, and availability of sufficient data. Extensive validated research is needed to guide future clinical practice with the implementation of technology-enhanced epilepsy care. This narrative review article will explore the use of AI and telemedicine in EEG and epilepsy care, examining their individual and combined impacts in shaping the future of epilepsy care and discussing the challenges and limitations faced in their usage.

Purpose:The study aimed to create a machine learning model (MLM) to predict the stone-free status (SFS) of patients undergoing percutaneous nephrolithotomy (PCNL) and compare its performance to the S.T.O.N.E. and Guy's stone scores.Patients and Methods:This is a retrospective study that included 320 PCNL patients. Pre-operative and post-operative variables were extracted and entered into three MLMs: RFC, SVM, and XGBoost. The methods used to assess the performance of each were mean bootstrap estimate, 10-fold cross-validation, classification report, and AUC. Each model was externally validated and evaluated by mean bootstrap estimate with CI, classification report, and AUC.Results:Out of the 320 patients who underwent PCNL, the SFS was found to be 69.4%. The RFC mean bootstrap estimate was 0.75 and 95% CI: [0.65-0.85], 10-fold cross-validation of 0.744, an accuracy of 0.74, and AUC of 0.761. The XGBoost results were 0.74 [0.63-0.85], 0.759, 0.72, and 0.769, respectively. The SVM results were 0.70 [0.60-0.79], 0.725, 0.74, and 0.751, respectively. The AUC of Guy's stone score and the S.T.O.N.E. score were 0.666 and 0.71, respectively. The RFC external validation set had a mean bootstrap estimate of 0.87 and 95% CI: [0.81-0.92], an accuracy of 0.70, and an AUC of 0.795, While the XGBoost results were 0.84 [0.78-0.91], 0.74, and 0.84, respectively. The SVM results were 0.86 [0.80-0.91], 0.79, and 0.858, respectively.Conclusion:MLMs can be used with high accuracy in predicting SFS for patients undergoing PCNL. MLMs we utilized predicted the SFS with AUCs superior to those of GSS and S.T.O.N.E scores.

Background: Glioblastoma multiforme (GBM) is the most common tumor of the central nervous system with poor prognosis. Cuproptosis is a novel programmed cell death pathway targeting lipoylated tricarboxylic acid cycle proteins. Previous studies have found that it participates in tumor progression, but its role in GBM is still elusive. In this study, we aimed to develop a cuproptosis gene-signature risk score using bioinformatics analysis and machine learning. Methods: We acquired transcriptomic and clinical information of GBM patients from The Cancer Genome Atlas (TCGA). A total of 2283 differentially expressed genes (DEGs) were obtained from the GEPIA2 database. 26 cuproptosis-related genes (CRGs) were retrieved from literature. A correlation analysis between the 26 CRGs and the DEGs were conducted to retrieve the cuproptosis-related DEGs. Then, a univariate cox analysis was conducted to obtain the prognostic-related DEGs for overall survival (OS). The least absolute shrinkage and selection operator (LASSO) were conducted for regularization and the gene risk score was constructed using the multivariate cox coefficients. Results: A total of 731 downregulated DEGs were correlated with CRGs, while 68 upregulated DEGs were correlated with CRGs and were further screened for prognostic value using the univariate cox analysis. A total of 70 prognostic related CRGs were identified and were further screened using the LASSO cox analysis. After multivariate cox analysis, a total of seven genes were significantly associated with survival (p-value<0.01). A risk-score gene signature was constructed from the cox coefficients multiplied by the expression of the following genes: -0.0012*DPP10+0.0021*EGR4+0.0015*ITPKA+ 0.0003* PTPRN+ 0.0007* STEAP2+ 0.0006* TENM2+- 0.0017* ZNF540. Conclusion: Univariate and multivariate Cox regression analyses showed the CRGs-based prognostic signature independently functioned as a risk factor for OS in GBM patients. Furthermore, our results gave a promising understanding of cuproptosis in GBM, as well as a tailored prediction tool for prognosis and immunotherapeutic responses in patients. Citation Format: Yaman B. Ahmed, Ayah N. Al-Bzour, Ghayda'a N. Al-Majali, Zaid M. Khalefa, Saja M. Alzghoul. Identification of the cuproptosis-related gene signature associated with the tumor environment and prognosis of patients with glioblastoma multiforme (GBM) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2046.

BACKGROUND:Primary focal hyperhidrosis (PH) can be managed by a wide range of medical and surgical modalities. Compensatory hyperhidrosis (CH) is a well-documented complication of surgical treatment. We aimed to investigate the occurrence of compensatory hyperhidrosis (CH) in PH patients after nonsurgical treatment with botulinum toxin A (BTX- A) or iontophoresis.METHODOLOGY:We carried out a unicentric prospective study on PH patients from King Abdullah University Hospital (KAUH) in Jordan. PH patients were evaluated after 1-month of nonsurgical treatment. Patients who developed CH were re-assessed after 3-6 months through a telephone-based interview.RESULTS:A total of 86 patients with PH who underwent nonsurgical treatment with iontophoresis or botulinum toxin were recruited. Twenty-four (27.9%) patients developed subjective CH. It was mild in (75%), moderate in (21%), and severe in (4%) of patients affected, it was self-limiting within a few months in all patients. Patients with CH did not differ significantly in demographic or clinical variables from patients who did not develop CH except at the site of PH (p value = .05).CONCLUSION:The findings of this study indicate that more than quarter (27.9%) of patients with PH may develop minor compensatory sweating, however this didn't affect satisfaction with treatment.

Objective: In this study, we aim to study the role of metabolic pathways in AD and to conduct a bioinformatics analysis and gene ontology to understand the candidate metabolic pathway genes in the pathogenesis of AD, and to construct a machine learning diagnostic model using those genes. Background: Alzheimer's disease (AD) is the most common form of dementia. It is known that the metabolic pathways are altered in AD. So, we investigated their mechanisms to aid in finding a therapeutic targets for patients with AD. Design/Methods: We used (GSE159541, GSE104704, GSE15222, GSE5281) datasets from Gene Expression Omnibus database to find the differentially expressed genes (DEGs). Then we analyzed the metabolic pathway-related genes from the Molecular Signature database (MSigDB) and intersected them with the DEGs. We then constructed a machine learning diagnostic model using Support vector machine (SVM) and recursive feature elimination (RFE) to select the top 5 ranking genes. Then we fitted them in a random forest classification (RFC) to assess their performance. Results: We identified 2054 DEGs and intersected them with metabolic pathway-related genes resulting in 72 metabolic genes. Then we fitted them into a SVM and used RFE to choose the top 5 ranked genes which were (ENOPH1, PSMA2, PSMB2, TXN2, UROD). Among those genes, UROD was the highest contributing gene. Those genes were fitted into an RFC which was trained to predict the diagnosis of patients in a dataset of 343 total patients. The performance of the RFC model on the testing set was measured by a mean bootstrap estimate of 0.711 with 95% CI: [0.58–0.83], 10-fold cross-validation of 0.72, and AUC of 0.70. Conclusions: We identified 5 metabolic pathway-related genes and constructed a novel diagnostic model. Those results may help in understanding the pathogenesis in AD, which may aid in the creation of therapeutic targets and more widely available biomarkers. Disclosure: Miss Al-Bzour has nothing to disclose. Mr. Ahmed has nothing to disclose. Mr. Al-Khalili has nothing to disclose. Mr. Hamza has nothing to disclose. Ms. Ibrahim has nothing to disclose. Miss Al-Bzour has nothing to disclose.

Royal jelly (RJ) is a multifunctional bee product with a unique composition and wide-ranging biological properties, including antioxidant, anti-inflammatory and antiproliferative activities. Still, little is known about the possible myocardial protective properties of RJ. Considering that sonication could enhance RJ bioactivity, this study aimed to assess the effects of non-sonicated (NS) and sonicated (S) RJ on fibrotic signaling, cell proliferation, and collagen production in cardiac fibroblasts. S-RJ was produced by ultrasonication at 20 kHz. Ventricular fibroblasts isolated from neonatal rats were cultured and treated with different concentrations of NS-RJ or S-RJ (0, 50, 100, 150, 200, and 250 µg/well). S-RJ significantly depressed the expression levels of transglutaminase 2 (TG2) mRNA across all the concentrations tested and was inversely associated with the expression of this profibrotic marker. S-RJ and NS-RJ displayed distinct dose-dependent effects on mRNA expression of several other profibrotic, proliferation, and apoptotic markers. Unlike NS-RJ, S-RJ elicited strong negative dose-dependent relationships with the expression of profibrotic markers (TG2, COL1A1, COL3A1, FN1, CTGF, MMP-2, α-SMA, TGF-β1, CX43, periostin), as well as proliferation (CCND1) and apoptotic (BAX, BAX/BCL-2) markers, indicating that RJ dose-response effects were significantly modified by sonification. NS-RJ and S-RJ increased the content of soluble collagen, while decreasing collagen cross-linking. Collectively, these findings show that S-RJ has a greater range of action than NS-RJ for downregulating the expression of biomarkers associated with cardiac fibrosis. Reduced biomarker expression and collagen cross-linkages upon cardiac fibroblast treatment with specific concentrations of S-RJ or NS-RJ suggests putative roles and mechanisms by which RJ may confer some protection against cardiac fibrosis.

Abstract Background Chemotherapy induced cognitive impairment (CICI) is a term used for the cognitive dysfunction reported with non-central nervous system cancer patients during or after chemotherapy. Recent neuroimaging studies have shown decreased activation in cancer patients during cognitive tasks post-chemotherapy. Structural changes are sought to be associated with cognitive decline in cancer survivors after chemotherapy. Here we aim to investigate the neuroimaging findings of structural abnormalities and gray matter alterations associated with chemotherapy in cancer survivors. Material and Methods A systematic search through PubMed database for peer-reviewed English-language studies yielded a total of 302 studies. Eligible studies were included in the activation likelihood estimation (ALE) meta-analysis if they reported coordinates in a stereotactic format (MNI or Talairach space) for voxel-based morphometric (VBM) studies on gray matter volume and post-chemotherapy cancer survivors compared to matched healthy controls. GingerALE (3.0.2) software from Brainmap.org was used to perform the ALE meta-analysis with threshold settings of uncorrected P-value < 0.001 for multiple comparisons. Results The study sample included 299 patients comprised of breast cancer survivors (BCS), childhood acute lymphocytic leukemia (ALL) survivors, ovarian cancer survivors (OCS) and lung cancer with 279 matched healthy controls from 12 studies and 21 experiments. ALE-maps for post-chemotherapy cancer survivors showed reduced gray matter volume in comparison to healthy controls in the left inferior frontal gyrus, right thalamus, right superior frontal gyrus, right medial frontal gyrus and right cerebellum (peak coordinates: [-43,40,-6], [23,-29,2], [14,30,55], [9,57,-16], [9,-78,-41] respectively, and cluster size of 544 mm3, 312 mm3 and 257 mm3, 296 mm3, 297 mm3 respectively). Conclusion This is the first ALE meta-analysis that studied the converged areas of reduced gray matter volume in post-chemotherapy cancer survivors. Our findings of reduced gray matter volume in frontal regions and cerebellum might be responsible for the cognitive dysfunction in executive function, attention, and memory observed in cancer survivors. Further studies are needed to assess the impact of different chemotherapeutic regimens related to CICI .

We assessed whether stroke severity, functional outcome, and mortality in patients with ischemic stroke differed between patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and those without. We conducted a prospective, single-center cohort study in Irbid, North Jordan. All patients diagnosed with ischemic stroke and SARS-CoV-2 infection were consecutively recruited from October 15, 2020, to October 16, 2021. We recorded demographic data, vascular risk factors, National Institutes of Health Stroke Scale (NIHSS) score, stroke subtype according to the Trial of ORG 10172 in Acute Stroke Treatment Criteria (TOAST), treatments at admission, and laboratory variables for all patients. The primary endpoint was the functional outcome at 3 months assessed using the modified Rankin Score. Secondary outcomes involved in-hospital mortality and mortality at 3 months. We included 178 patients with a mean (standard deviation) age of 67.3 (12), and more than half of the cases were males (96/178; 53.9%). Thirty-six cases were coronavirus disease 2019 (COVID-19) related and had a mean (standard deviation) age of 70 (11.5). When compared with COVID-19-negative patients, COVID-19-positive patients were more likely to have a higher median NIHSS score at baseline (6 vs 11; P = .043), after 72 hours (6 vs 12; P = .006), and at discharge (4 vs 16; P < .001). They were also more likely to have a higher median modified Rankin Score after 3 months of follow-up ( P < .001). NIHSS score at admission (odds ratio = 1.387, 95% confidence interval = 1.238–1.553]; P < .001) predicted having an unfavorable outcome after 3 months. On the other hand, having a concomitant SARS-CoV-2 infection did not significantly impact the likelihood of unfavorable outcomes (odds ratio = 1.098, 95% confidence interval = 0.270–4.473; P = .896). The finding conclude that SARS-CoV-2 infection led to an increase in both stroke severity and in-hospital mortality but had no significant impact on the likelihood of developing unfavorable outcomes.

Background:As the incidence of intracranial infections increase due to diagnostic procedures improvement, more real-life data is needed to reach a more solid informed management approach.Objective:This study aims to describe and analyse clinical features of intracranial abscesses patients treated at a tertiary hospital in North Jordan during a 10-year period.Methods:We retrospectively identified 37 patients treated at King Abdullah University Hospital (KAUH) from 2011 to 2020 in Irbid, North Jordan. Treatment consisted of either aspiration, open craniotomy excision (OCE) or conservative therapy. Extracted variables included demographic data such (age, gender), clinical presentation, lab findings, radiological findings as well as management plan. Retrieved data was compared between the patients who underwent a single operation and those who underwent reoperation after the initial procedure.Results:Thirty-seven patients with 55 intracerebral abscesses were identified, 29 of whom had intraparenchymal brain abscesses, 4 patients had epidural empyema, and 4 had subdural empyema. The mean age was 28.8 (± 20.7) years, with a male predominance (78.4%). Sixteen patients underwent open craniotomy excision (OCE), 14 patients were treated by aspiration and 7 patients were treated conservatively. When comparing the single operation and the reoperation groups, there was no statistically significant difference across variables.Conclusion:Our study presents valuable insight from a tertiary hospital in north Jordan on intracranial abscesses and empyemas. Our findings confirm that good recovery can be established after aspiration or OCE in the majority of patients. Similar results were obtained when comparing the SOP and the ROP groups.