Antibiotic Susceptibility, Biofilm-Forming Ability, and Prevalence of Extended-Spectrum Beta-Lactamase (ESBL)- and Biofilm-Associated Genes Among Klebsiella pneumoniae Isolates from Hospitalized Patients in Northwest of Iran.

Klebsiella pneumoniae is an opportunistic bacterium, which is globally recognized for its high prevalence and antimicrobial resistance (AMR). Biofilm-forming capability, susceptibility testing, and phenotypic confirmatory test for extended-spectrum beta-lactamase (ESBL)-producing isolate recognition of 104 K. pneumoniae isolates were performed according to the Clinical Laboratory Standard Institute (CLSI) guidelines. The prevalence of ESBL-associated genes bla-VIM, bla-NDM, and bla-OXA-48, as well as biofilm-associated genes luxS, fimH1, wza, and mrkD, was determined by multiplex PCR. The highest resistance rate was against ampicillin (100.0%). Among the 104 K. pneumoniae isolates, 52 (50.0%) and 31 (29.8%) isolates were determined as multi- and extensively drug resistant (MDR, XDR), respectively. Moreover, 21 (40.4%) isolates were determined as ESBL producing. Among 50 biofilm-producing K. pneumoniae isolates, 7 (14.0%), 15 (30.0%), and 28 (56.0%) isolates exhibited high, moderate, and weak levels of biofilm formation, respectively. A number of 41 (78.8%) isolates were susceptible to colistin, and 10 (19.2%) were resistant. AMR was significantly higher (P < 0.05) in the biofilm-forming isolates compared with non-biofilm formers.

Application of next-generation sequencing in the diagnosis of gastric cancer.

Objectives: Gastric cancer (GC) is a disease with high mortality, poor prognosis and numerous risk factors. GC has an asymptomatic nature in early stages of the diseases, making timely diagnosis complicated using common conventional approaches, namely pathological examinations and imaging tests. Recently, molecular profiling of GC using next generation sequencing (NGS) has opened new doors to efficient prognostic, diagnostic, and therapeutic strategies. The current review aims to thoroughly discuss and compare the current NGS techniques and commercial platforms utilized for GC diagnosis and treatment, highlighting the most recent NGS-based GC studies. Furthermore, this review addresses the challenges of clinical implementation of NGS in GC.Materials and methods: This review was conducted according to the eligible studies identified via search of Web of Science, PubMed, Scopus, Embase and the Cochrane Library. In the present study, data on gastric cancer patients and NGS methods used to diagnose the disease were reviewed.Conclusion: Given the ever-rising advancements in NGS technologies, bioinformatics, healthcare guidelines and refined classifications, it is hoped that these technologies can actualize their advantages and optimize GC patients' experience.

Nurse-led telehealth interventions for symptom management in patients with cancer receiving systemic or radiation therapy: a systematic review and meta-analysis.

PURPOSE: Patients receiving cancer treatments experience many treatment-related symptoms. Telehealth is increasingly being used to support symptom management. The overall aim was to determine the effectiveness of nurse-led telehealth symptom management interventions for patients with cancer receiving systemic or radiation therapy compared to usual care on health service use, quality of life, and symptom severity. METHODS: A systematic review was conducted following the Cochrane Handbook and PRISMA reporting guidelines. Five electronic databases were searched. Two independent reviewers screened articles and extracted data. Meta-analysis was performed if data were clinically and methodologically homogeneous. Subanalysis was conducted on reactive and scheduled telehealth interventions. RESULTS: Of 7749 citations screened, 10 studies were included (8 randomized control trials, 2 quasi-experimental). Five were reactive telehealth interventions with patient-initiated contact and five evaluated scheduled telehealth interventions initiated by nurses. Compared to usual care (typically patient-initiated calls), nurse-led telehealth interventions for symptom management showed no statistically significant difference in hospitalizations, emergency department visits, or unscheduled clinic visits. Two of three studies of reactive telehealth interventions showed improved quality of life. All telehealth interventions showed reduction in the severity of most symptoms. Pain severity was significantly reduced (standard mean difference - 0.54; 95% CI - 0.88, - 0.19). Significant heterogeneity prevented meta-analysis for most outcomes. CONCLUSION: Few studies evaluated nurse-led telehealth interventions for cancer symptom management. Compared to usual care, patients exposed to telehealth interventions had reduced symptom severity and no difference in health services use. Future research should focus on better reporting intervention characteristics and consistently measuring outcomes.

Supportive care needs of Iranian cancer patients.

BACKGROUND: A supportive needs assessment is an essential component of any care program. There is no research evidence regarding the supportive care needs of cancer patients in Iran or other Middle Eastern countries. AIMS: The aim of this study was to determine the supportive care needs of Iranian cancer patients. MATERIALS AND METHODS: This descriptive study was conducted in a referral medical center in the northwest of Iran. A total of 274 cancer patients completed the Supportive Care Needs Survey (SCNS-59). Descriptive statistics were used for data analysis. RESULTS: In 18 items of the SCNS, more than 50% of the participants reported that their needs were unmet. Most frequently, unmet needs were related to the health system, information, physical, and daily living domains, and most met needs were related to sexuality, patient care, and support domains. CONCLUSIONS: Iranian cancer patients experience many unmet needs and there is an urgent need for establishing additional supportive care services in Iran.

Deep EEG source localization via EMD-based fMRI high spatial frequency.

Brain imaging with a high-spatiotemporal resolution is crucial for accurate brain-function mapping. Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two popular neuroimaging modalities with complementary features that record brain function with high temporal and spatial resolution, respectively. One popular non-invasive way to obtain data with both high spatial and temporal resolutions is to combine the fMRI activation map and EEG data to improve the spatial resolution of the EEG source localization. However, using the whole fMRI map may cause spurious results for the EEG source localization, especially for deep brain regions. Considering the head's conductivity, deep regions' sources with low activity are unlikely to be detected by the EEG electrodes at the scalp. In this study, we use fMRI's high spatial-frequency component to identify the local high-intensity activations that are most likely to be captured by the EEG. The 3D Empirical Mode Decomposition (3D-EMD), a data-driven method, is used to decompose the fMRI map into its spatial-frequency components. Different validation measurements for EEG source localization show improved performance for the EEG inverse-modeling informed by the fMRI's high-frequency spatial component compared to the fMRI-informed EEG source-localization methods. The level of improvement varies depending on the voxels' intensity and their distribution. Our experimental results also support this conclusion.

Frequencies of mobilized colistin resistance (mcr-1, 2) genes in clinically isolated Escherichia coli; a cross sectional study.

OBJECTIVE: Escherichia coli (E. coli) is an opportunistic bacterium, which is globally recognized for its high prevalence and antimicrobial resistance (AMR). The presence of colistin-resistant representative mcr- 1, 2 genes in multi-drug resistant (MDR) clinically isolated E. coli is the main goal of this survey. After biochemically and molecular confirmation tests, susceptibility testing, biofilm formation, and minimum inhibitory concentration to colistin were performed on 100 E. coli isolates. Subsequently, taking advantage of uniplex-PCR, the presence of some responsible genes (mcr- 1, mcr- 2) to colistin-resistant phenotypes in mentioned bacterium was assessed. RESULTS: Disc diffusion methods indicated that the highest resistance rate was against ampicillin (80.0%), and trimethoprim/sulfamethoxazole (63%). Among the E. coli isolates, 72 (72.0%) was determined as MDR, respectively. Moreover, 47 (47%) strains were determined as extreme beta-lactamase (ESBL) phenotypes. Among 41 slime-producing E. coli strains, 7 (17.07%), 14 (34.14%), and 20 (48.78%) strains exhibited high, moderate, and weak levels of biofilm formation, respectively. Fifty-nine (81.94%), and 1(100%) of MDR isolates were assessed as colistin resistant (MIC > 2) and susceptible (MIC ≤ 2) as well. In 26(36.11%) of colistin-resistant isolates and 1(1.38%) of colistin, susceptible isolate mcr-1 gene was found. There is no mcr- 2 gene was detected in isolates. CONCLUSION: The diversity of high antibiotic-resistant rates could be avoided by developing appropriate healthcare policies and community awareness. Alarming resistance rates were observed in colistin and ampicillin, which should be taken into account in therapy guidelines.

Holo-Hilbert spectral-based noise removal method for EEG high-frequency bands.

Simultaneous EEG-fMRI is a growing and promising field, as it has great potential to further our understanding of the spatiotemporal dynamics of brain function in health and disease. In particular, there is much interest in understanding the fMRI correlates of brain activity in the gamma band (> 30 Hz), as these frequencies are thought to be associated with cognitive processes involving perception, attention, and memory, as well as with disorders such as schizophrenia and autism. However, progress in this area has been limited due to issues such as MR-induced artifacts in EEG recordings, which seem to be more problematic for gamma frequencies. This paper presents a noise removal method for the gamma band of EEG that is based on the Holo-Hilbert spectral analysis (HHSA), but with a new implementation strategy. HHSA uses a nested empirical mode decomposition (EMD) to identify amplitude and frequency modulations (AM and FM, respectively) by averaging over frequencies with high and significant powers. Our method examines gamma band by applying two layers of EMD to the FM and AM components, removing components with very low power based on the power-instantaneous frequency spectrum, and subsequently reconstructs the denoised gamma-band signal from the remaining components. Simulations demonstrate that our proposed method efficiently reduces artifacts while preserving the original gamma signal which is especially critical for simultaneous EEG/fMRI studies.

Interventions to facilitate interprofessional collaboration in the operating theatre: A scoping review.

BACKGROUND: Ineffective collaboration can increase adverse events in the operating theatre. When professionals work collaboratively, they are more likely to improve patient safety and outcomes. AIM: To identify interprofessional collaboration interventions involving operating theatre teams and describe their effect on facilitating communication, teamwork, and safety. METHODS: A scoping review of four databases. Results were analysed by identifying interventions and mapping their related outcomes. RESULTS: Twenty studies evaluated single or multi-faceted interventions. Despite low-quality study designs (no randomised controlled trials), four interventions (eg: briefings, checklists, team training, debriefing) improved communication and teamwork, and enhanced safety outcomes. Only one study, using team training, reported that organisational level interventions (eg: Standard Operating Procedures, Lean quality improvement management system) improved teamwork and safety outcomes. CONCLUSION: Several studies reported interventions enhanced interprofessional collaboration within operating theatre teams. Although findings were in favour of improved communication and teamwork, more rigorous research is required.

Frequency and antimicrobial resistance pattern of bacterial isolates from patients with COVID-19 in two hospitals of Zanjan.

BACKGROUND AND OBJECTIVES: The outbreak of COVID-19 has been challenging the global health systems. As one of the major associated concerns, microbial co-infections and antimicrobial resistance play critical roles in the prognosis of the disease. This study aims to evaluate co-infections in COVID-19 patients regarding drug resistance. MATERIALS AND METHODS: A total number of 5530 Real Time PCR-confirmed COVID-19 cases, who were admitted to two major educational Hospitals in Zanjan, Iran, from February 2019 to February 2020 were included. Respiratory, blood and urine specimens were collected and cultured on selective media. Subsequently, isolates identification, disc diffusion susceptibility tests, and data analysis were carried out. RESULTS: Bacterial and fungal co-infections were confirmed in 423 patients (8.1%). Co-infections were more prevalent among females (53.2%) than males (46.8%). Coinfected patients had a significantly higher mortality rate compared to those without co-infections (54.8% vs. 12.2%, P<0.001). Acinetobacter baumannii was the most prevalent bacteria isolated from respiratory tract (15.4%) and blood (2.1%). Escherichia coli (12.5%) was the most frequent bacteria in urine. Fungal co-infection was confirmed in 174 (3.36%) patients. Gram-negative bacteria were highly sensitive to colistin (97.85%) and widely resistant to cefixime (91.79%) and trimethoprim-sulfamethoxazole (89.64%). Gram-positive bacteria were considerably sensitive to vancomycin (68%) and nitrofurantoin (66%). Tetracycline and ampicillin were the least effective antibiotics for Gram-positive bacteria with respective resistance rates of 90.91% and 83.33%. CONCLUSION: Given the high incidence of bacterial co-infections in COVID-19 patients, it is important to develop rapid and efficient diagnostic, therapeutic and disinfection guidelines to control these infections in the hospitals.

Nursing students' attitude and preparedness for nurse prescribing and its relationship with self-efficacy.

AIM: The number of countries where nurses are allowed to prescribe has increased over the past two decades. Nursing students' self-efficacy has the main impact on their clinical and cognitive skills. The aim of this study is to determine nursing students' attitudes and preparedness for nurse prescribing and its relationship with their perceived self-efficacy. DESIGN: The descriptive-correlational study METHODS: This study was conducted at five Nursing and Midwifery schools of Tabriz, Ardebil, Ilam, Ramsar, and Mashhad, Iran from March to July 2018. Through random sampling 250 undergraduate nursing students from the different geographical areas of Iran were selected. The research tool consisted of three parts including demographic information, nurse prescribing questionnaire, and a questionnaire on self-efficacy in patient care. RESULTS: Nursing students had a positive attitudes and relatively high levels of preparedness for nurse prescribing. However, near to half of the participants (48.4%) mentioned that they have not good knowledge of pharmacology. Also there was a significant positive correlation between the students' self-efficacy and their attitudes and preparedness to nurse prescribing (p < 0.001, rs= 0.467;p = 0.00, rs= 0.633). CONCLUSIONS: Given the nursing students' positive attitude to and their preparedness for nurse prescribing, it is possible to make them more prepared for this new role by increasing their pharmacological knowledge and improving their accountability, management, and leadership skills.

Estimation of global and local complexities of brain networks: A random walks approach.

The complexity of brain activity has been observed at many spatial scales and has been proposed to differentiate between mental states and disorders. Here we introduced a new measure of (global) network complexity, constructed as the sum of the complexities of its nodes (i.e., local complexity). The complexity of each node is obtained by comparing the sample entropy of the time series generated by the movement of a random walker on the network resulting from removing the node and its connections, with the sample entropy of the time series obtained from a regular lattice (ordered state) and a random network (disordered state). We studied the complexity of fMRI-based resting-state networks. We found that positively correlated (pos) networks comprising only the positive functional connections have higher complexity than anticorrelation (neg) networks (comprising the negative connections) and the network consisting of the absolute value of all connections (abs). We also observed a significant correlation between complexity and the strength of functional connectivity in the pos network. Our results suggest that the pos network is related to the information processing in the brain and that functional connectivity studies should analyze pos and neg networks separately instead of the abs network, as is commonly done.

Spatiotemporal Empirical Mode Decomposition of Resting-State fMRI Signals: Application to Global Signal Regression.

Resting-state functional connectivity MRI (rs-fcMRI) is a common method for mapping functional brain networks. However, estimation of these networks is affected by the presence of a common global systemic noise, or global signal (GS). Previous studies have shown that the common preprocessing steps of removing the GS may create spurious correlations between brain regions. In this paper, we decompose fMRI signals into 5 spatial and 3 temporal intrinsic mode functions (SIMF and TIMF, respectively) by means of the empirical mode decomposition (EMD), which is an adaptive data-driven method widely used to analyze non-linear and non-stationary phenomena. For each SIMF, functional connectivity matrices were computed by means of Pearson correlation between TIMFs of different brain areas. Thus, instead of a single connectivity matrix, we obtained 5 × 3 = 15 functional connectivity matrices. Given the high correlation and global efficiency values of the connectivity matrices related to the low spatial maps (SIMF3, SIMF4, and SIMF5), our results suggest that these maps can be considered as spatial global signal masks. Thus, by summing up the first two SIMFs extracted from the fMRI signals, we have automatically excluded the GS which is now voxel-specific. We compared the performance of our method with the conventional GS regression and to the results when the GS was not removed. While the correlation pattern identified by the other methods suffers from a low level of precision in identifying the correct brain network connectivity, our approach demonstrated expected connectivity patterns for the default mode network and task-positive network.

Un-met supportive care needs of Iranian breast cancer patients.

BACKGROUND: Assessment of supportive needs is the requirement to plan any supportive care program for cancer patients. There is no evidence about supportive care needs of Iranian breast cancer patients. So, the aims of present study were to investigate this question and s predictive factors. MATERIALS AND METHODS: A descriptive- correlational study was conducted, followed by logistic regression analyses. The Supportive Care Needs Survey was completed by 136 breast cancer patients residing in Iran following their initial treatment. This assessed needs in five domains: psychological, health system and information, physical and daily living, patient care and support, and sexuality. RESULTS: Patient perceived needs were highest in the health systems and information (71%), and physical and daily living (68%) domains. Logistic regression modeling revealed that younger participants have more un-met needs in all domains and those with more children reported fewer un-met needs in patient care and support domains. In addition, married women had more un-met supportive care needs related to sexuality. CONCLUSIONS: The high rate of un-met supportive care needs in all domains suggests that supportive care services are desperately required for breast cancer patients in Iran. Moreover, services that address informational needs and physical and daily living needs ought to be the priority, with particular attention paid to younger women. Further research is clearly needed to fully understand supportive care needs in this cultural context.