Exploring the Impact of Cardiac Rehabilitation Programs on Health-Related Quality of Life and Physiological Outcomes in Patients Post Coronary Artery Bypass Grafts: A Systematic Review.

Background: This systematic review explores the impact of cardiac rehabilitation programs (CRPs) on health-related quality of life (HRQoL) and physiological outcomes post-coronary artery bypass graft (CABG) surgery. Acknowledging the increasing importance of CRPs in post-CABG care, the study emphasizes the need for a comprehensive evaluation of their effectiveness. The primary objective is to investigate how CRPs influence HRQoL and physiological outcomes in post-CABG patients, offering insights into the multifaceted impact of these rehabilitation programs. Methods: A systematic literature review approach was employed to identify relevant studies published between 2013 and 2023. Inclusion criteria encompassed clinical randomized trials and quasi-experimental studies, with a focus on CRP interventions and their impact on HRQoL and physiological parameters. Results: The review reveals a diverse array of CRP approaches, including exercise training, home-based programs, and telemonitored interventions. Despite methodological variations, a consistent positive impact on HRQoL and physiological outcomes is observed across studies. Noteworthy interventions, such as those incorporating family caregivers, demonstrate holistic benefits. However, limitations include methodological variability and the exclusion of qualitative studies. Conclusions: This systematic review underscores the substantial positive impact of CRPs on HRQoL and physiological outcomes in post-CABG patients. The diverse approaches and consistent improvements across studies provide a robust foundation for healthcare practitioners and researchers. Future efforts should focus on standardizing CRP interventions and conducting well-designed trials to further enhance the evidence base, facilitating more targeted and effective rehabilitation strategies for CABG patients.

Klebsiella pneumoniae bacteraemia epidemiology: resistance profiles and clinical outcome of King Fahad Medical City isolates, Riyadh, Saudi Arabia.

BACKGROUND AND OBJECTIVES: Klebsiella pneumoniae (K. pneumoniae) is the second leading cause of community-acquired and hospital-acquired gram-negative bloodstream infection (BSI). This study aimed to assess the epidemiological and microbial-resistance characteristics and clinical factors associated with K. pneumoniae BSI in Saudi Arabia. MATERIALS AND METHODS: Data of 152 K. pneumoniae isolates diagnosed between January 2019 and January 2020 at King Fahad Medical City, Riyadh, Saudi Arabia were evaluated retrospectively. Clinical records of the patients were collected and analysed statistically. RESULTS: In total, 152 cases of K. pneumoniae BSI were identified. Adult patients (66.4%) were at a higher risk of developing the infection than paediatric patients (33.6%). The rate of infection was slightly higher in women than in men. Neurological disorders were the predominant underlying conditions for the acquisition of K. pneumoniae BSI, at all ages. Most of the deceased patients were adults with multi-organ dysfunction. Klebsiella pneumoniae showed disturbing resistance to amoxicillin-clavulanate and cefuroxime (72.4%), ceftazidime (67.8), cephalothin (76.3%), and to Carbapenems (36.1%). CONCLUSIONS: The impact of K. pneumoniae BSI was seen not only at the patient level, but also at the community level, and was related to multi-drug resistant infection. These findings provide a better understanding of microbial resistance and its association with patient clinical outcomes.

Validation and Generalizability of Machine Learning Models for the Prediction of Discharge Disposition Following Revision Total Knee Arthroplasty.

BACKGROUND: Postoperative discharge to facilities account for over 33% of the $ 2.7 billion revision total knee arthroplasty (TKA)-associated annual expenditures and are associated with increased complications when compared to home discharges. Prior studies predicting discharge disposition using advanced machine learning (ML) have been limited due to a lack of generalizability and validation. This study aimed to establish ML model generalizability by externally validating its prediction for nonhome discharge following revision TKA using national and institutional databases. METHODS: The national and institutional cohorts comprised 52,533 and 1,628 patients, respectively, with 20.6 and 19.4% nonhome discharge rates. Five ML models were trained and internally validated (five-fold cross-validation) on a large national dataset. Subsequently, external validation was performed on our institutional dataset. Model performance was assessed using discrimination, calibration, and clinical utility. Global predictor importance plots and local surrogate models were used for interpretation. RESULTS: The strongest predictors of nonhome discharge were patient age, body mass index, and surgical indication. The area under the receiver operating characteristic curve increased from internal to external validation and ranged between 0.77 and 0.79. Artificial neural network was the best predictive model for identifying patients at risk for nonhome discharge (area under the receiver operating characteristic curve = 0.78), and also the most accurate (calibration slope = 0.93, intercept = 0.02, and Brier score = 0.12). CONCLUSION: All five ML models demonstrated good-to-excellent discrimination, calibration, and clinical utility on external validation, with artificial neural network being the best model for predicting discharge disposition following revision TKA. Our findings establish the generalizability of ML models developed using data from a national database. The integration of these predictive models into clinical workflow may assist in optimizing discharge planning, bed management, and cost containment associated with revision TKA.

Validation of Machine Learning Model Performance in Predicting Blood Transfusion After Primary and Revision Total Hip Arthroplasty.

BACKGROUND: The rates of blood transfusion following primary and revision total hip arthroplasty (THA) remain as high as 9% and 18%, respectively, contributing to patient morbidity and healthcare costs. Existing predictive tools are limited to specific populations, thereby diminishing their clinical applicability. This study aimed to externally validate our previous institutionally developed machine learning (ML) algorithms to predict the risk of postoperative blood transfusion following primary and revision THA using national inpatient data. METHODS: Five ML algorithms were trained and validated using data from 101,266 primary THA and 8,594 revision THA patients from a large national database to predict postoperative transfusion risk after primary and revision THA. Models were assessed and compared based on discrimination, calibration, and decision curve analysis. RESULTS: The most important predictors of transfusion following primary and revision THA were preoperative hematocrit (<39.4%) and operation time (>157 minutes), respectively. All ML models demonstrated excellent discrimination (area under the curve (AUC) >0.8) in primary and revision THA patients, with artificial neural network (AUC = 0.84, slope = 1.11, intercept = -0.04, Brier score = 0.04), and elastic-net-penalized logistic regression (AUC = 0.85, slope = 1.08, intercept = -0.01, and Brier score = 0.12) performing best, respectively. On decision curve analysis, all 5 models demonstrated a higher net benefit than the conventional strategy of intervening for all or no patients in both patient cohorts. CONCLUSIONS: This study successfully validated our previous institutionally developed ML algorithms for the prediction of blood transfusion following primary and revision THA. Our findings highlight the potential generalizability of predictive ML tools developed using nationally representative data in THA patients.

A Dynamic Trust-Related Attack Detection Model for IoT Devices and Services Based on the Deep Long Short-Term Memory Technique.

The integration of the cloud and Internet of Things (IoT) technology has resulted in a significant rise in futuristic technology that ensures the long-term development of IoT applications, such as intelligent transportation, smart cities, smart healthcare, and other applications. The explosive growth of these technologies has contributed to a significant rise in threats with catastrophic and severe consequences. These consequences affect IoT adoption for both users and industry owners. Trust-based attacks are the primary selected weapon for malicious purposes in the IoT context, either through leveraging established vulnerabilities to act as trusted devices or by utilizing specific features of emerging technologies (i.e., heterogeneity, dynamic nature, and a large number of linked objects). Consequently, developing more efficient trust management techniques for IoT services has become urgent in this community. Trust management is regarded as a viable solution for IoT trust problems. Such a solution has been used in the last few years to improve security, aid decision-making processes, detect suspicious behavior, isolate suspicious objects, and redirect functionality to trusted zones. However, these solutions remain ineffective when dealing with large amounts of data and constantly changing behaviors. As a result, this paper proposes a dynamic trust-related attack detection model for IoT devices and services based on the deep long short-term memory (LSTM) technique. The proposed model aims to identify the untrusted entities in IoT services and isolate untrusted devices. The effectiveness of the proposed model is evaluated using different data samples with different sizes. The experimental results showed that the proposed model obtained a 99.87% and 99.76% accuracy and F-measure, respectively, in the normal situation, without considering trust-related attacks. Furthermore, the model effectively detected trust-related attacks, achieving a 99.28% and 99.28% accuracy and F-measure, respectively.

Fortification of Fermented Camel Milk with Salvia officinalis L. or Mentha piperita Leaves Powder and Its Biological Effects on Diabetic Rats.

The incorporation of fermented camel milk with natural additives possesses numerous benefits for the treatment of various pathological and metabolic conditions. The present study investigated the impact of fortification of fermented camel milk with sage or mint leaves powder (1 and 1.5%, respectively) on glucose and insulin levels, lipid profile, and liver and kidney functions in alloxan-induced diabetic rats. The gross chemical composition of sage and peppermint leaves powder was studied. The chemical composition of sage and mint extracts was performed using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-MS) of sage and mint extracts. Furthermore, a total of forty-two adult normal male albino rats were included in this study, whereas one group was kept as the healthy control group (n = 6 rats) and diabetes was induced in the remaining animals (n = 36 rats) using alloxan injection (150 mg/kg of body weight). Among diabetic rats groups, a control group (n = 6 rats) was kept as the diabetic control group whereas the other 5 groups (6 rats per group) of diabetic rats were fed fermented camel milk (FCM) or fermented camel milk fortified with 1 and 1.5% of sage or mint leaves powder. Interestingly, the oral administration of fermented camel milk fortified with sage or mint leaves powder, at both concentrations, caused a significant decrease in blood glucose level and lipid profile, and an increase in insulin level compared to the diabetic control and FCM groups. Among others, the best results were observed in the group of animals that received fermented camel milk fortified with 1.5% sage powder. In addition, the results revealed that the fermented camel milk fortified with sage or mint leaves powder improved the liver and kidney functions of diabetic rats. Our study concluded that the use of sage and mint leaves powder (at a ratio of 1.5%) with fermented camel milk produces functional food products with anti-diabetic activity.

Blood Biomarkers of Neonatal Sepsis with Special Emphasis on the Monocyte Distribution Width Value as an Early Sepsis Index.

Background and Objectives: Early detection of neonatal sepsis is critical because it is potentially fatal. Therefore, sepsis biomarkers of sufficient sensitivity and specificity are needed. This study aimed to evaluate the utility of peripheral blood parameters as neonatal sepsis biomarkers and the diagnostic performance of the monocyte distribution width (MDW) in sepsis in a neonatal intensive care unit. Materials and Methods: A cross-sectional study was conducted from September 2019 to August 2020 at the King Saud University Medical City in Riyadh, Saudi Arabia. Samples were collected and organised as follows: 77 study cases were subdivided into two subgroups (other health complication (49) and sepsis (28)), and there were 70 controls. The causative microorganisms of neonatal sepsis were isolated. Peripheral blood samples were collected from each neonate in an ethylenediaminetetraacetic acid tube for a complete blood count and a leukocyte differential count. Moreover, the receiver operating characteristic (ROC) curve analysis was used to measure the diagnostic performance of the MDW. Results: The haematological parameters and neonatal sepsis cases had a considerable correlation. The MDW was the most significant haematological parameter. The ROC analysis of the MDW demonstrated that the area under the curve was 0.89 (95% confidence interval: 0.867 to 0.998) with a sensitivity of 89.3%, a specificity of 88.2%, and a negative predictive value of 97.2% at the cut-off point of 23. Conclusions: The use of haematological parameters is feasible and can be performed rapidly. Neonatal sepsis showed a strong correlation with leukopenia, anaemia, thrombocytopenia, and an elevated MDW value. Moreover, the ROC curve analysis confirmed the high diagnostic ability of the MDW in neonatal sepsis prediction.

A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique.

Recently, Internet of Things (IoT) technology has emerged in many aspects of life, such as transportation, healthcare, and even education. IoT technology incorporates several tasks to achieve the goals for which it was developed through smart services. These services are intelligent activities that allow devices to interact with the physical world to provide suitable services to users anytime and anywhere. However, the remarkable advancement of this technology has increased the number and the mechanisms of attacks. Attackers often take advantage of the IoTs' heterogeneity to cause trust problems and manipulate the behavior to delude devices' reliability and the service provided through it. Consequently, trust is one of the security challenges that threatens IoT smart services. Trust management techniques have been widely used to identify untrusted behavior and isolate untrusted objects over the past few years. However, these techniques still have many limitations like ineffectiveness when dealing with a large amount of data and continuously changing behaviors. Therefore, this paper proposes a model for trust management in IoT devices and services based on the simple multi-attribute rating technique (SMART) and long short-term memory (LSTM) algorithm. The SMART is used for calculating the trust value, while LSTM is used for identifying changes in the behavior based on the trust threshold. The effectiveness of the proposed model is evaluated using accuracy, loss rate, precision, recall, and F-measure on different data samples with different sizes. Comparisons with existing deep learning and machine learning models show superior performance with a different number of iterations. With 100 iterations, the proposed model achieved 99.87% and 99.76% of accuracy and F-measure, respectively.

A Correlation-Based Anomaly Detection Model for Wireless Body Area Networks Using Convolutional Long Short-Term Memory Neural Network.

As the Internet of Healthcare Things (IoHT) concept emerges today, Wireless Body Area Networks (WBAN) constitute one of the most prominent technologies for improving healthcare services. WBANs are made up of tiny devices that can effectively enhance patient quality of life by collecting and monitoring physiological data and sending it to healthcare givers to assess the criticality of a patient and act accordingly. The collected data must be reliable and correct, and represent the real context to facilitate right and prompt decisions by healthcare personnel. Anomaly detection becomes a field of interest to ensure the reliability of collected data by detecting malicious data patterns that result due to various reasons such as sensor faults, error readings and possible malicious activities. Various anomaly detection solutions have been proposed for WBAN. However, existing detection approaches, which are mostly based on statistical and machine learning techniques, become ineffective in dealing with big data streams and novel context anomalous patterns in WBAN. Therefore, this paper proposed a model that employs the correlations that exist in the different physiological data attributes with the ability of the hybrid Convolutional Long Short-Term Memory (ConvLSTM) techniques to detect both simple point anomalies as well as contextual anomalies in the big data stream of WBAN. Experimental evaluations revealed that an average of 98% of F1-measure and 99% accuracy were reported by the proposed model on different subjects of the datasets compared to 64% achieved by both CNN and LSTM separately.

Effect of Lipid-Based Multiple Micronutrients Supplementation in Underweight Primigravida Pre-Eclamptic Women on Maternal and Pregnancy Outcomes: Randomized Clinical Trial.

Background and Objectives: In pre-eclampsia, restricted blood supply due to the lack of trophoblastic cell invasion and spiral artery remodeling is responsible for adverse pregnancies and maternal outcomes, which is added to by maternal undernutrition. This study was designed to observe the effect of multiple nutritional micronutrient supplements on the pregnancy outcomes of underweight pre-eclamptic women. To investigate the effects of lipid-based multiple micr supplementations (LNS-PLW) on pregnancy and maternal outcomes in underweight primigravida pre-eclamptic women. Materials and Methods: A total of 60 pre-eclamptic, underweight primigravida women from the antenatal units of tertiary care hospitals in the Khyber Pakhtunkhwa Province, Pakistan, were randomly divided into two groups (Group 1 and Group 2). The participants of both groups were receiving routine treatment for pre-eclampsia: iron (60 mgs) and folic acid (400 ug) IFA daily. Group 2 was given an additional sachet of 75 gm LNS-PLW daily till delivery. The pregnancy outcomes of both groups were recorded. The clinical parameters, hemoglobin, platelet count, and proteinuria were measured at recruitment. Results: The percentage of live births in Group 2 was 93% compared to 92% in Group 1. There were more normal vaginal deliveries (NVDs) in Group 2 compared to Group 1 (Group 2, 78% NVD; group 1, 69% NVD). In Group 1, 4% of the participants developed eclampsia. The frequency of cesarean sections was 8/26 (31%) in Group 1 and 6/28 (22%) in Group 2. The number of intrauterine deaths (IUDs) was only 1/28 (4%) in Group 2, while it was 2/26 (8%) in Group 1. The gestational age at delivery significantly improved with LNS-PLW supplementation (Group 2, 38.64 ± 0.78 weeks; Group 1, 36.88 ± 1.55 weeks, p-value 0.006). The Apgar score (Group 2, 9.3; Group 1, 8.4) and the birth weight of the babies improved with maternal supplementation with LNS-PLW (Group 2, 38.64 ± 0.78 weeks: Group 1, 36.88 ± 1.55; p-value 0.003). There was no significant difference in systolic blood pressure, while diastolic blood pressure (Group 2, 89.57 ± 2.08 mmHg; Group 1, 92.17 ± 5.18 mmHg, p-value 0.025) showed significant improvement with LNS-PLW supplementation. The hemoglobin concentration increased with the LNS-PLW supplement consumed in Group 2 (Group 2, 12.15 ± 0.78 g/dL; Group 1, 11.39 ± 0.48 g/dL, p-value < 0.001). However, no significant difference among the platelet counts of the two groups was observed. Conclusions: The pregnancy and maternal outcomes of underweight pre-eclamptic women can be improved by the prenatal daily supplementation of LNS-PLW during pregnancy, along with IFA and regular antenatal care and follow-up.

Exploring the Role of Intramolecular Interactions in the Suppression of Quantum Tunneling of the Magnetization in a 3d-4f Single-Molecule Magnet.

Hydroxide-bridged FeIII4LnIII2 clusters having the general formula [Fe4Ln2(µ3-OH)2(mdea)6(SCN)2(NO3)2(H2O)2]·4H2O·2MeCN {Ln = Y (1), Dy (2), mdea = N-methyldiethanolamine} were synthesized and magnetically characterized. The thermal relaxation of the magnetization for 2 and the diluted FeIII4DyIIIYIII complex 3 (with and without applied field) has been analyzed. The diluted sample shows a dominant QTM at low temperatures that can be removed with a 0.15 T dc field. Both 2 and 3 show moderately high Ueff barriers and exhibit hysteresis loops until 5 K.

Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine.

Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, one-class learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with O(nd) memory utilization and no communication overhead.

Nasal colonization and antibiotic resistance patterns of Staphylococcus species isolated from healthy horses in Tripoli, Libya.

The present study investigated the colonization rates and antimicrobial susceptibility of Staphylococcus species isolated from the nostrils of healthy horses. A nonselective laboratory approach was applied, followed by confirmation using a Phoenix automated microbiological system. Among the 92 horses included in the study, 48.9% (45/92) carried Staphylococcus species of mostly the coagulase-negative staphylococci (CoNS) type yielding 70 Staphylococcus strains. Of these strains, 37.1% (26/70; 24 CoNS and 2 coagulase-positive staphylococci; CoPS) were identified as methicillin-resistant staphylococci (MRS) expressing significant resistance to important antimicrobial classes represented mainly by subspecies of CoNS. This is the first study reporting a high prevalence of various Staphylococcus species, particularly strains of CoNS expressing multidrug resistance patterns of public health concern, colonizing healthy horses in Libya.

Inactivation of Salmonella spp. in tahini using plant essential oil extracts.

Tahini is a popular food product in the Middle East region and is used as a major ingredient in several ready-to-eat food products. Tahini and its products have been linked to foodborne illness outbreaks and product recalls worldwide as a result of Salmonella spp. contamination. The objectives of the current study were to investigate: i) the effectiveness of 10 plant essential oil extracts on the viability of Salmonella spp. using disc diffusion ii) the antimicrobial activity of the most effective oils against Salmonella spp. in commercial or 10% w/v hydrated tahini (tahini-based product model) stored at 37, 25 and 10 °C for 28 d and iii) the effect of the addition of essential oil extracts on the sensory acceptability of tahini and hydrated tahini. Among the tested essential oils, thyme (TO) and cinnamon oil (CO) showed the highest antimicrobial activity against tested Salmonella spp. at 37 and 10 °C using a disc diffusion assay method. In tahini, the addition of 2.0% CO reduced the numbers of Salmonella spp. by 2.87, 2.64 or 2.35 log10 CFU/ml at 37, 25 or 10 °C, respectively, by 28 d. However, the antimicrobial activity of CO was more pronounced at all storage temperatures in hydrated tahini where no viable cells were detected after 3 d storage at 25 and 37 °C, or after 7 d at 10 °C. However, at 25 and 37 °C, the antimicrobial activity of CO was more evident since no viable cells were detected after 14 d when 0.5% was used. The numbers of Salmonella spp. were reduced by 3.29, 3.03 or 2.17 log10 CFU/ml at 37, 25 or 10 °C, respectively, after 28 d when 2.0% TO was added to tahini. Salmonella spp. were not detected in the hydrated tahini treated with 2.0% TO after 28 d at 37 °C or 25 °C, while at 10 °C, the numbers of Salmonella spp. were not significantly reduced after 28 d in hydrated tahini compared to the initial numbers at zero time. Therefore, the addition of TO and CO could be used to preclude the post process contamination of tahini with foodborne pathogens, yet, the addition of TO and CO to tahini reduced its consumer acceptability compared untreated tahini.

Microbial safety of oily, low water activity food products: A review.

Oily, low water activity (OL aw) products including tahini (sesame seed paste), halva (tahini halva), peanut butter, and chocolate, have been recently linked to numerous foodborne illness outbreaks and recalls. This review discusses the ingredients used and processing of OL aw products with a view to provide greater understanding of the routes of their contamination with foodborne pathogens and factors influencing pathogen persistence in these foods. Adequate heat treatment during processing may eliminate bacterial pathogens from OL aw foods; however, post-processing contamination commonly occurs. Once these products are contaminated, their high fat and sugar content can enhance pathogen survival for long periods. The physiological basis and survival mechanisms used by pathogens in these products are comprehensively discussed here. Foodborne outbreaks and recalls linked to OL aw foods are summarized and it was observed that serotypes of Salmonella enterica were the predominant pathogens causing illnesses. Further, intervention strategies available to control foodborne pathogens such as thermal inactivation, use of natural antimicrobials, irradiation and hydrostatic pressure are assessed for their usefulness to achieve pathogen control and enhance the safety of OL aw foods. Sanitation, hygienic design of manufacturing facilities, good hygienic practices, and environmental monitoring of OL aw food industries were also discussed.

Factors affecting the viability of Staphylococcus aureus and production of enterotoxin during processing and storage of white-brined cheese.

Staphylococcus aureus is a major foodborne pathogen that causes severe disease in humans. It is commonly found in milk and dairy products, particularly in fresh brined cheese. Our aim was to investigate the behavior of Staph. aureus and enterotoxin production during the storage of white-brined cheese prepared with or without a starter culture and stored in a 10 or 15% NaCl brine at 10°C and 25°C for 28 d. NaCl concentration, water activity, pH, and number of Staph. aureus and lactic acid bacteria were determined in cheese and brine. Only 1 of 4 Staph. aureus strains (ATCC 439) was positive for enterotoxin production, and its production was detected in unsalted UHT milk, but not in salted milk or in any of the cheese treatments held at 37°C for 1, 3, or 7 d. Staphylococcus aureus grew in the cheese stored in both brines at 10°C and 25°C, regardless of the presence of a starter culture, although the latter significantly reduced Staph. aureus growth in cheese or its brine at 10°C. Staphylococcus aureus numbers were increased by 2.26 and 0.47 log10 cfu/g in cheese stored in 10 and 15% NaCl brine, respectively, in the presence of starter culture, and by 2.78 and 2.96 log10 cfu/g, respectively, in the absence of starter culture at 10°C. Nonetheless, the pathogen grew, but at a lower number in the brines. The salt concentration of cheese stored in 10% brine remained at approximately 5% during storage; however, in 15% brine, the salt concentration increased to almost 8% (wt/wt) by 28 d. The addition of a starter culture, high salt concentration, low temperature, and pH (∼5.2) had inhibitory effects on the growth of Staph. aureus. Moreover, lactic acid bacterial numbers increased considerably in cheese and brine by d 28. The use of starter cultures, salt (15%), and low storage temperature (10°C) reduced the growth of Staph. aureus, and salt may have prevented enterotoxin production in white-brined cheese.

Synthesis, Characterization and Biological Evaluation of Metal Adamantyl 2-Pyridylhydrazone Complexes.

Four new complexes derived from adamantly containing hydrazone (APH) ligand with Cu(II) (1), Co(II) (2), Ni(II) (3) and Zn(II) (4), have been synthesized and characterized using different physicochemical methods. The structure of the ligand APH and its copper complex 1 have been established by single-crystal X-ray diffraction direct methods, which reveal that complex 1 has distorted square-pyramidal geometry. Complexes 1-4 are screened against seven human cancer cell lines namely, breast cancer cell lines (MCF7, T47D, MDA-MB-231), prostate cancer cell lines (PC3, DU145) and the colorectal cancer cell line Coco-2, for their antiproliferative activities. Complex 1 has shown a promising anticancer activity compared to the other ones. The structural and spectroscopic analysis of APH and its complexes are confirmed by DFT calculations.

Does Exposure to a Traumatic Event Make Organizations Resilient?

Why do some organizations bounce-back from traumatic events more quickly than others? While the research on organizations offers extensive insights on recovery from economic or technological shocks, there is limited understanding of how organizations recover from life-threatening events such as terrorist attacks. In this study, we build on the research on resilience and argue that organizational recovery from a traumatic event is informed by the perception of threat. Higher perception of threat increases inter-organizational collaboration and the care associated with the deployment of slack as well as to learning. We tested our arguments with a sample of US and non-US firms before and after the 9/11 terrorist attacks and found that, due to spatial proximity, US firms' higher perception of threat led to a larger increase in the frequency of inter-organizational alliances than that of non-US firms. This preference was more frequently directed towards local partners and demonstrated a distinct emphasis on slack and learning. Contrary to conventional wisdom, our findings suggest that organizational resilience in the face of a traumatic event benefits not from immunity but from spatial proximity to the threat. Proximity increases the perception of threat, and with it, the impetus for adaptation.

Improved Outcomes Associated with the use of Shock Protocols: Updates from the National Cardiogenic Shock Initiative.

BACKGROUND: The National Cardiogenic Shock Initiative is a single-arm, prospective, multicenter study to assess outcomes associated with early mechanical circulatory support (MCS) in patients presenting with acute myocardial infarction and cardiogenic shock (AMICS) treated with percutaneous coronary intervention (PCI). METHODS: Between July 2016 and February 2019, 35 sites participated and enrolled into the study. All centers agreed to treat patients with AMICS using a standard protocol emphasizing invasive hemodynamic monitoring and rapid initiation of MCS. Inclusion and exclusion criteria mimicked those of the "SHOCK" trial with an additional exclusion criteria of intra-aortic balloon pump counter-pulsation prior to MCS. RESULTS: A total of 171 consecutive patients were enrolled. Patients had an average age of 63 years, 77% were male, and 68% were admitted with AMICS. About 83% of patients were on vasopressors or inotropes, 20% had a witnessed out of hospital cardiac arrest, 29% had in-hospital cardiac arrest, and 10% were under active cardiopulmonary resuscitation during MCS implantation. In accordance with the protocol, 74% of patients had MCS implanted prior to PCI. Right heart catheterization was performed in 92%. About 78% of patients presented with ST-elevation myocardial infarction with average door to support times of 85 ± 63 min and door to balloon times of 87 ± 58 min. Survival to discharge was 72%. Creatinine ≥2, lactate >4, cardiac power output (CPO) <0.6 W, and age ≥ 70 years were predictors of mortality. Lactate and CPO measurements at 12-24 hr reliably predicted overall mortality postindex procedure. CONCLUSION: In contemporary practice, use of a shock protocol emphasizing best practices is associated with improved outcomes.

Myristica fragrans Kernels Prevent Paracetamol-Induced Hepatotoxicity by Inducing Anti-Apoptotic Genes and Nrf2/HO-1 Pathway.

Paracetamol is responsible for acute liver failure in humans and experimental animals when taken at high doses and transformed into a reactive metabolite by the liver cytochrome P450. On the other hand, nutmeg is rich with many phytochemical ingredients that are known for their ability to inhibit cytochrome P450. Hence, the present experiment was aimed at studying the hepatoprotective effect of Myristica fragrans (nutmeg), kernel extract (MFKE) in respect to paracetamol (acetaminophen; N-acetyl-p-amino-phenol (APAP))-induced hepatotoxicity in rats, focusing on its antioxidant, anti-inflammatory, and anti-apoptotic activities. Liver toxicity was induced in rats by a single oral administration of APAP (2 g/kg). To evaluate the hepatoprotective effect of MFKE against this APAP-induced hepatotoxicity, rats were pre-treated with either oral administration of MFKE at 300 mg/kg daily for seven days or silymarin at 50 mg/kg as a standard hepatoprotective agent. APAP intoxication caused a drastic elevation in liver function markers (transaminases, alkaline phosphatase, and total bilirubin), oxidative stress indicators (lipid peroxidation and nitric oxide), inflammatory biomarkers (tumour necrosis factor-α, interleukin-1ß, inducible nitric oxide synthase, and nuclear factor ĸB) and the pro-apoptotic BCL2 Associated X (Bax) and caspases-3 genes. Furthermore, analyses of rat liver tissue revealed that APAP significantly depleted glutathione and inhibited the activities of antioxidant enzymes in addition to downregulating two key anti-apoptotic genes: Cellular FLICE (FADD-like IL-1ß-converting enzyme)-inhibitory protein (c-FLIP) and B-cell lymphoma 2 (Bcl-2). Pre-treatment with MFKE, however, attenuated APAP-induced liver toxicity by reversing all of these toxicity biomarkers. This hepatoprotective effect of MFKE was further confirmed by improvement in histopathological findings. Interestingly, the hepatoprotective effect of MFKE was comparable to that offered by the reference hepatoprotector, silymarin. In conclusion, our results revealed that MFKE had antioxidant, anti-inflammatory, and anti-apoptotic properties, and it is suggested that this hepatoprotective effect could be linked to its ability to promote the nuclear factor erythroid 2⁻related factor 2 (Nrf2)/antioxidant responsive element (ARE) pathway.