Assessment of the knowledge,attitudes, and practices among UNRWA health staff in Jordan concerning mental health programme pre-implementation: a cross-sectional study

Background

Mental health is a major public health priority, particularly among refugees worldwide. The United Nations Relief and Works Agency for Palestine Refugees (UNRWA) started to integrate mental health and psychosocial support (MHPSS) into its primary health-care services in Jordan in late 2017. This baseline study aimed to assess the knowledge, attitudes, practices, and perceived barriers among UNRWA health staff regarding the implementation of the MHPSS programme.

A systematic review of capacity assessment tools in pediatric surgery: Global Assessment in Pediatric Surgery (GAPS) Phase I

Background

The Lancet Commission on Global Surgery highlighted global surgical need but offered little insight into the specific surgical challenges of children in low-resource settings. Efforts to strengthen the quality of global pediatric surgical care have resulted in a proliferation of partnerships between low-and middle-income countries (LMICs) and high-income countries (HICs). Standardized tools able to reliably measure gaps in delivery and quality of care are important aids for these partnerships. We undertook a systematic review (SR) of capacity assessment tools (CATs) focused on needs assessment in pediatric surgery.

Searching for prognostic biomarkers for small renal masses in the urinary proteome

Renal cell carcinoma (RCC) is frequently diagnosed incidentally as an early-stage small renal mass (SRM; pT1a, ≤4 cm). Overtreatment of patients with benign or clinically indolent SRMs is increasingly common and has resulted in a recent shift in treatment recommendations. There are currently no available biomarkers that can accurately predict clinical behavior. Therefore, we set out to identify early biomarkers of RCC progression. We employed a quantitative label-free liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) proteomics approach and targeted parallel-reaction monitoring to identify and validate early, noninvasive urinary biomarkers for RCC-SRMs. In total, we evaluated 115 urine samples, including 33 renal oncocytoma (≤4 cm) cases, 30 progressive and 26 nonprogressive clear cell RCC (ccRCC)-SRM cases, in addition to 26 healthy controls. We identified six proteins, which displayed significantly elevated expression in clear cell RCC-SRMs (ccRCC-SRMs) relative to healthy controls. Proteins C12ORF49 and EHD4 showed significantly elevated expression in ccRCC-SRMs compared to renal oncocytoma (≤4 cm). Additionally, proteins EPS8L2, CHMP2A, PDCD6IP, CNDP2 and CEACAM1 displayed significantly elevated expression in progressive relative to nonprogressive ccRCC-SRMs. A two-protein signature (EPS8L2 and CCT6A) showed significant discriminatory ability (areas under the curve: 0.81, 95% CI: 0.70–0.93) in distinguishing progressive from nonprogressive ccRCC-SRMs. Patients (Stage I–IV) with EPS8L2 and CCT6A mRNA alterations showed significantly shorter overall survival (p = 1.407 × 10⁻⁶) compared to patients with no alterations. Our in-depth proteomic analysis identified novel biomarkers for early-stage RCC-SRMs. Pretreatment characterization of urinary proteins may provide insight into early RCC progression and could potentially help assign patients to appropriate management strategies.     

Cognitive decline and depressive symptoms: early non-motor presentations of parkinsonism among Egyptian Gaucher patients

neurogenetics - Evidence about the link between glucocerebrosidase (GCase) and parkinsonism is growing. Parkinsonism was described in adult type 1 Gaucher disease (GD); few case reports described...     

Investigating the temporal dynamics of electroencephalogram (EEG) microstates using recurrent neural networks

Electroencephalogram (EEG) microstates that represent quasi‐stable, global neuronal activity are considered as the building blocks of brain dynamics. Therefore, the analysis of microstate sequences is a promising approach to understand fast brain dynamics that underlie various mental processes. Recent studies suggest that EEG microstate sequences are non‐Markovian and nonstationary, highlighting the importance of the sequential flow of information between different brain states. These findings inspired us to model these sequences using Recurrent Neural Networks (RNNs) consisting of long‐short‐term‐memory (LSTM) units to capture the complex temporal dependencies. Using an LSTM‐based auto encoder framework and different encoding schemes, we modeled the microstate sequences at multiple time scales (200–2,000 ms) aiming to capture stably recurring microstate patterns within and across subjects. We show that RNNs can learn underlying microstate patterns with high accuracy and that the microstate trajectories are subject invariant at shorter time scales (≤400 ms) and reproducible across sessions. Significant drop in the reconstruction accuracy was observed for longer sequence lengths of 2,000 ms. These findings indirectly corroborate earlier studies which indicated that EEG microstate sequences exhibit long‐range dependencies with finite memory content. Furthermore, we find that the latent representations learned by the RNNs are sensitive to external stimulation such as stress while the conventional univariate microstate measures (e.g., occurrence, mean duration, etc.) fail to capture such changes in brain dynamics. While RNNs cannot be configured to identify the specific discriminating patterns, they have the potential for learning the underlying temporal dynamics and are sensitive to sequence aberrations characterized by changes in metal processes. Empowered with the macroscopic understanding of the temporal dynamics that extends beyond short‐term interactions, RNNs offer a reliable alternative for exploring system level brain dynamics using EEG microstate sequences.     

The Clinical and Prognostic Implications of Pluripotent Stem Cell Markers Expression and Their Correlation with the WNT signal pathway in Hepatocellular Carcinoma

Objectives: This study aimed to investigate the expression of SOX2, SOX9, p53, and β-catenin in hepatocellular carcinoma (HCC) and their correlation with clinicopathological parameters of prognostic importance. Materials and Methods: Seventy-five patients were enrolled in this study. All patients had full clinical and follow-up data and available paraffin blocks. Immunohistochemical analysis was performed and correlated with clinicopathological factors and patient survival. Results: We detected the positive expression of SOX2, SOX9, p53, and β-catenin in 76%, 50.7%, 50.7%, and 77.9% of HCC specimens respectively. All studied markers showed a significant increase in the expression in tumor tissue specimens compared to non-tumor tissue. Both SOX2 and SOX9 expressions were significantly associated with adverse prognostic factors in HCC. Significant positive correlations were found between SOX2 and SOX9 and both p53 and β-catenin expression (r= 0.528, 0.485 and; r = 0.253, 0.327, respectively; p < 0.0001 for both of them). Regarding survival, we found that HCC patients with positive SOX2 and SOX9 expressions had significantly shorter overall survival (p=0.0001, each). Additionally, larger tumor size, tumor grade, high stage, tumor multiplicity, presence of cirrhosis, tumor necrosis, high p53 expression, and positive β-catenin expression were independent predictors of worse survival. A multivariate Cox analysis revealed that tumor grade, stage, p53, and SOX2 expression were independent predictors of unfavorable prognosis in overall survival (p=0.0001, p=0.0001,p=0.033; and p=0.003, respectively). Conclusions: Our findings might provide an insight into SOX2 and SOX9’s role in HCC and suggest that SOX2 might be targeted for HCC therapy.