Background: Methicillin-resistant Staphylococcus aureus (MRSA) is a public health threat and a major cause of hospital-acquired and community-acquired infections. This study aimed to investigate the genetic diversity of MRSA isolates from 2015 to 2017 and to characterize the major MRSA clones and anti-biogram trends in Palestine. Methodology: Isolates were obtained from 112 patients admitted to different hospitals of West Bank and East Jerusalem, originating from different clinical sources. Antibiotic susceptibility patterns, staphylococcal chromosomal cassette mec (SCC mec ) typing, and Staphylococcus aureus protein A ( spa ) typing were determined. Also, a panel of toxin genes and virulence factors was studied, including: Panton-Valentine Leukocidin (PVL), ACME- arc A, Toxic Shock Syndrome Toxin-1 (TSST-1), and Exfoliative Toxin A (ETA). Results: Of the 112 confirmed MRSA isolates, 100% were resistant to all β-lactam antibiotics. Resistance rates to other non- β-lactam classes were as the following: 18.8% were resistant to trimethoprim-sulfamethoxazole, 23.2% were resistant to gentamicin, 34.8% to clindamycin, 39.3% to ciprofloxacin, and 63.4% to erythromycin. All MRSA isolates were susceptible to vancomycin (100%). Of all isolates, 32 isolates (28.6%) were multidrug- resistant (MDR). The majority of the isolates were identified as SCC mec type IV (86.6%). The molecular typing identified 29 spa types representing 12 MLST-clonal complexes (CC). The most prevalent spa types were: spa type t386 (CC1)/(12.5%), spa type t044 (CC80)/(10.7%), spa type t008 (CC8)/(10.7%), and spa type t223 (CC22)/(9.8%). PVL toxin gene was detected in (29.5%) of all isolates, while ACME- arcA gene was present in 18.8% of all isolates and 23.2% had the TSST-1 gene. The two most common spa types among the TSST-1positive isolates were the spa type t223 (CC22)/(Gaza clone) and the spa type t021 (CC30)/(South West Pacific clone). All isolates with the spa type t991 were ETA positive (5.4%). USA-300 clone ( spa type t008, positive for PVL toxin gene and ACME- arcA genes) was found in nine isolates (8.0%). Conclusions: Our results provide insights into the epidemiology of MRSA strains in Palestine. We report a high diversity of MRSA strains among hospitals in Palestine, with frequent SCC mec type IV carriage. The four prominent clones detected were: t386-IV/ CC1, the European clone (t044/CC80), Gaza clone (t223/CC22), and the USA-300 clone (t008/CC8).

CitationAbu Seir A, Kharroubi A. Implementation of palliative care in palestine: Cultural and religious perspectives.

Although autoimmune diseases (AIDs) are known to predispose to non-Hodgkin lymphoma (NHL), their association with NHL prognosis has rarely been investigated. We examined associations between autoimmunity and B-cell NHL onset by comparing AID history (determined by self-report and medication review and supplemented by chart review where possible) among 435 adult B-NHL patients in Hadassah-Hebrew University Medical Center, diagnosed 2009-2014, and 414 age-and-sex frequency-matched controls. We examined AIDs as a whole, B- and T-cell-mediated AIDs, and autoimmune thyroid diseases. Among cases, we used Kaplan-Meier and Cox regression models to assess the association of AID with overall survival and relapse-free survival, adjusting for prognostically important patient and disease characteristics such as Ki67% staining, International Prognostic Index, rituximab treatment, and histological subgroup. Autoimmune diseases were associated with B-NHL (odds ratio [OR] = 1.95; 95% confidence interval (CI), 1.31-2.92), especially AIDs mediated by B-cell activation (OR = 5.20; CI, 1.90-14.3), which were particularly associated with marginal zone lymphoma (OR = 19.3; CI, 4.59-80.9). We found that time to relapse for all B-NHL patients with AIDs was significantly shorter (mean of 49.21 mo [±3.22]) than among patients without AID (mean of 59.74 mo [±1.62]), adjusted hazard ratio [HR ] = 1.69 (CI, 1.03-2.79). Specifically, in patients with diffuse large B-cell lymphoma, of whom 91.8% had received rituximab, a history of B-cell-mediated AIDs was associated with shorter relapse-free survival and overall survival, HR = 8.34 (CI, 3.01-23.1) and HR = 3.83 (CI, 1.20-12.3), respectively. Beyond confirming the well-known association between AIDs and B-NHL, we found that AID is an adverse prognostic factor in B-cell lymphoma, associated with a shortened time to relapse, suggesting that there are specific therapeutic challenges in the subgroup of patients suffering from both these diseases. Further work is required to address mechanisms of resistance to standard treatment in the setting of AID-associated B-NHL. In the era of immunotherapy, these findings have particular relevance.

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BackgroundRisk factors for B-cell non-Hodgkin lymphoma (B-NHL) have not been assessed among Palestinian Arabs (PA) and Israeli Jews (IJ).MethodsIn a case-control study we investigated self-reported medical and lifestyle exposures, reporting odds ratios (ORs) and 95% confidence intervals [CIs], by ethnicity, for overall B-NHL and subtypes.ResultsWe recruited 823 cases and 808 healthy controls. Among 307 PA/516 IJ B-NHL cases (mean age at diagnosis = 51 [±17] versus 60 [±15] years, respectively) subtype distributions differed, with diffuse large B-cell lymphoma (DLBCL) being prominent among PA (71%) compared to IJ (41%); follicular lymphoma (FL), was observed in 14% versus 28%, and marginal zone lymphoma, in 2% versus 14%, respectively. Overall B-NHL in both populations was associated with recreational sun exposure OR = 1.43 [CI:1.07–1.91], black hair-dye use OR = 1.70 [CI:1.00–2.87], hospitalization for infection OR = 1.68 [CI:1.34–2.11], and first-degree relative with hematopoietic cancer, OR = 1.69 [CI:1.16–2.48]. An inverse association was noted with alcohol use, OR = 0.46 [CI:0.34–0.62]. Subtype-specific exposures included smoking (FL, OR = 1.46 [CI:1.01–2.11]) and >monthly indoor pesticide use (DLBCL, OR = 2.01 [CI:1.35–3.00]). Associations observed for overall B-NHL in PA only included: gardening OR = 1.93 [CI:1.39–2.70]; history of herpes, mononucleosis, rubella, blood transfusion (OR>2.5, P<0.01 for all); while for IJ risk factors included growing fruits and vegetables, OR = 1.87 [CI:1.11–3.15]; and self-reported autoimmune diseases, OR = 1.99 [CI:1.34–2.95].ConclusionsIn these geographically proximate populations we found some unique risk factors for B-NHL. Heterogeneity in the observed associations by ethnicity could reflect differences in lifestyle, medical systems, and reporting patterns, while variations by histology infer specific etiologic factors for lymphoma subtypes.

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Background: Maternal depression and anxiety are significant public health concerns that play an important role in the health and well-being of mothers and children. The COVID-19 pandemic, the consequential lockdowns and related safety restrictions worldwide negatively affected the mental health of pregnant and postpartum women. Methods: This regional study aimed to develop a machine learning (ML) model for the prediction of maternal depression and anxiety. The study used a dataset collected from five Arab countries during the COVID-19 pandemic between July to December 2020. The population sample included 3569 women (1939 pregnant and 1630 postpartum) from five countries (Jordan, Palestine, Lebanon, Saudi Arabia, and Bahrain). The performance of seven machine learning algorithms was assessed for the prediction of depression and anxiety symptoms. Results: The Gradient Boosting (GB) and Random Forest (RF) models outperformed other studied ML algorithms with accuracy values of 83.3% and 83.2% for depression, respectively, and values of 82.9% and 81.3% for anxiety, respectively. The Mathew’s Correlation Coefficient was evaluated for the ML models; the Naïve Bayes (NB) and GB models presented the highest performance measures (0.63 and 0.59) for depression and (0.74 and 0.73) for anxiety, respectively. The features’ importance ranking was evaluated, the results showed that stress during pregnancy, family support, financial issues, income, and social support were the most significant values in predicting anxiety and depression. Conclusion: Overall, the study evidenced the power of ML models in predicting maternal depression and anxiety and proved to be an efficient tool for identifying and predicting the associated risk factors that influence maternal mental health. The deployment of machine learning models for screening and early detection of depression and anxiety among pregnant and postpartum women might facilitate the development of health prevention and intervention programs that will enhance maternal and child health in low- and middle-income countries.

A cross-sectional study was conducted in the West Bank, Palestine, between January and May 2014. Study subjects were young Palestinians aged 18-25 years. The study included students recruited from six Palestinian universities and non-students of the same age group recruited from university campuses (total=1997). Students were