Novel complex fuzzy distance measures with hesitance values and their applications in complex decision-making problems.

A complex fuzzy distance measure (CFDMs) plays a significant role in applications involving complex or high-dimensional data where traditional distance measures may not adequately capture the nuances of the data relationships. The significance of CFDMs lies in their ability to handle uncertainty, imprecision, and complexity in various domains. Numerous researchers introduced different concepts of CFDMs, yet these CFDMs fails to convey any information regarding the hesitancy degree associated with an element. The main objective of this paper is to introduce some new distance measures based on complex fuzzy sets, called complex fuzzy hesitance distance measure and complex fuzzy Euclidean Hesitance distance measure, which is the generalization of complex fuzzy normalized Hamming distance measure and complex fuzzy Euclidean distance measure. Some new operations and primay results are discussed in the environment of proposed CFDMs and complex fuzzy operations. Moreover, we discussed the applications of the proposed CFDMs in addressing decision-making problems. We introduced a new decision-making algorithm that integrates CFDMs into decision-making processes, providing a robust methodology for handling real-world complexities. Further, the comparative study of the proposed CFDMs is discussed with some existing CFDMs.

Uncertainty reduction for precipitation prediction in North America.

Large differences in projected future annual precipitation increases in North America exists across 27 CMIP6 models under four emission scenarios. These differences partly arise from weak representations of land-atmosphere interactions. Here we demonstrate an emergent constraint relationship between annual growth rates of future precipitation and growth rates of historical temperature. The original CMIP6 projections show 0.49% (SSP126), 0.98% (SSP245), 1.45% (SSP370) and 1.92% (SSP585) increases in precipitation per decade. Combining observed warming trends, the constrained results show that the best estimates of future precipitation increases are more likely to reach 0.40-0.48%, 0.83-0.93%, 1.29-1.45% and 1.70-1.87% respectively, implying an overestimated future precipitation increases across North America. The constrained results also are narrow the corresponding uncertainties (standard deviations) by 13.8-31.1%. The overestimated precipitation growth rates also reveal an overvalued annual growth rates in temperature (6.0-13.2% or 0.12-0.37°C) and in total evaporation (4.8-14.5%) by the original models' predictions. These findings highlight the important role of temperature for accurate climate predictions, which is important as temperature from current climate models' simulations often still have systematic errors.

Primary leiomyosarcoma of thyroid with pulmonary metastasis: A diagnostic odyssey.

The presented primary thyroid leiomyosarcoma (TL) case report underscores the importance of recognizing and addressing the diagnostic challenges and management complexities associated with this exceedingly rare malignancy. Given the limited effective therapeutic strategies available, timely intervention, thorough diagnostics, and vigilant follow-up are paramount in managing such intricate tumors. Further research focusing on molecular-based treatment modalities is imperative to improve patient outcomes in cases of primary TL.

Augmented human thermal discomfort in urban centers of the Arabian Peninsula.

Anthropogenic climate change has amplified human thermal discomfort in urban environments. Despite the considerable risks posed to public health, there is a lack of comprehensive research, evaluating the spatiotemporal changes in human thermal discomfort and its characteristics in hot-hyper arid regions, such as the Arabian Peninsula (AP). The current study analyzes spatiotemporal changes in human thermal discomfort categories and their characteristics in AP, using the newly developed high-resolution gridded ERA5-HEAT (Human thErmAl comforT) dataset for the period 1979-2022. In addition, the study assesses the interplay between the Universal Thermal Climate Index (UTCI) and El Niño-Southern Oscillation (ENSO) indices for the study period. The results reveal a significant increase in human thermal discomfort and its characteristics, with higher spatial variability in the AP region. The major urban centers in the southwestern, central, and southeastern parts of AP have experienced significant increases in human thermal discomfort (0.4-0.8 °C), with higher frequency and intensity of thermal stress during the study period. The temporal distribution demonstrates a linear increase in UTCI indices and their frequencies and intensities, particularly from 1998 onward, signifying a transition towards a hotter climate characterized by frequent, intense, and prolonged heat stress conditions. Moreover, the UTCI and ENSO indices exhibit a dipole pattern of correlation with a positive (negative) pattern in the southwestern (eastern parts) of AP. The study's findings suggest that policymakers and urban planners need to prioritize public health and well-being in AP's urban areas, especially for vulnerable groups, by implementing climate change adaptation and mitigation strategies, and carefully designing future cities to mitigate the effects of heat stress.

Characteristics of flash droughts and their association with compound meteorological extremes in China: Observations and model simulations.

Flash droughts have gained considerable public attention due to the imminent threats they pose to food security, ecological safety, and human health. Currently, there has been little research exploring the projected changes in flash droughts and their association with compound meteorological extremes (CMEs). In this study, we applied the pentad-mean water deficit index to investigate the characteristics of flash droughts and their association with CMEs based on observational data and downscaled model simulations. Our analysis reveals an increasing trend in flash drought frequency in China based on historical observations and model simulations. Specifically, the proportion of flash drought frequency with a one-pentad onset time showed a consistent upward trend, with the southern parts of China experiencing a high average proportion during the historical period. Furthermore, the onset dates of the first (last) flash droughts during year are projected to shift earlier (later) in a warmer world. Flash droughts become significantly more frequent in the future, with a growth rate approximately 1.3 times higher in the high emission scenario than in the medium emission scenario. The frequency of flash droughts with a one-pentad onset time also exhibits a significant upward trend, indicating that flash droughts will occur more rapidly in the future. CMEs in southern regions of China were found to be more likely to trigger flash droughts in the historical period. The probability of CMEs triggering flash droughts is expected to increase with the magnitude of warming, particularly in the far-future under the high emissions scenario.

Reactive oxygen species induced oxidative damage to DNA, lipids, and proteins of antibiotic-resistant bacteria by plant-based silver nanoparticles.

This study assesses the mechanism of action of plant-based silver nanoparticles (AgNPs) against antibiotic-resistant bacteria. We compared AgNPs synthesized through Salvia moorcroftiana and Origanum vulgare extracts and their conjugates with the antibiotic Ceftriaxone for their capacity to cause oxidative damage through reactive oxygen species (ROS). We quantified ROS in the cells of two bacterial strains after treating them with all AgNP types and observed that AgNPs were most effective in K. pneumoniae as they resulted in the highest ChS1 count (44,675), while in P. aeruginosa, Cfx-AgNPs induced the highest levels of ROS with ChS1 count of 56,865. DNA analysis showed that both plant-based AgNPs (O-AgNPs = 0.192 and S-AgNPs = 0.152) were most effective in K. pneumoniae and S-AgNPs (abs = 0.174) and O-Cfx-AgNPs (abs = 0.261) in P. aeruginosa. We observed a significant increase in the levels of conjugated dienes (86.4 µM) and malondialdehyde (172.25 nM) in the bacterial strains after treatment with AgNPs, compared to the control (71.65 µM and 18.064 nM, respectively, in K. pneumoniae and P. aeruginosa). These results indicate lipid peroxidation. AgNPs also increased the levels of protein thiols (0.672 nM) compared to the control (0.441 nM) in K. pneumoniae, except for Chem-AgNPs (0.21 nM). These results suggest that plant-based AgNPs are more effective in oxidizing bacterial DNA, protein, and lipids than Chem-AgNPs. Furthermore, protein oxidation varied between AgNPs alone and AgNPs-antibiotic conjugates. The highest levels of protein thiols were found in the samples treated with O-Cfx-AgNPs (0.672 nM and 0.525 nM in K. pneumoniae and P. aeruginosa, respectively). The results demonstrated that AgNPs kill bacteria by altering bacterial macromolecules such as DNA, lipids, and proteins.

A hybrid deep learning-based intrusion detection system for IoT networks.

The Internet of Things (IoT) is a rapidly evolving technology with a wide range of potential applications, but the security of IoT networks remains a major concern. The existing system needs improvement in detecting intrusions in IoT networks. Several researchers have focused on intrusion detection systems (IDS) that address only one layer of the three-layered IoT architecture, which limits their effectiveness in detecting attacks across the entire network. To address these limitations, this paper proposes an intelligent IDS for IoT networks based on deep learning algorithms. The proposed model consists of a recurrent neural network and gated recurrent units (RNN-GRU), which can classify attacks across the physical, network, and application layers. The proposed model is trained and tested using the ToN-IoT dataset, specifically collected for a three-layered IoT system, and includes new types of attacks compared to other publicly available datasets. The performance analysis of the proposed model was carried out by a number of evaluation metrics such as accuracy, precision, recall, and F1-measure. Two optimization techniques, Adam and Adamax, were applied in the evaluation process of the model, and the Adam performance was found to be optimal. Moreover, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. The results show that the proposed system achieves an accuracy of 99% for network flow datasets and 98% for application layer datasets, demonstrating its superiority over previous IDS models.

Multi-step attack detection in industrial networks using a hybrid deep learning architecture.

In recent years, the industrial network has seen a number of high-impact attacks. To counter these threats, several security systems have been implemented to detect attacks on industrial networks. However, these systems solely address issues once they have already transpired and do not proactively prevent them from occurring in the first place. The identification of malicious attacks is crucial for industrial networks, as these attacks can lead to system malfunctions, network disruptions, data corruption, and the theft of sensitive information. To ensure the effectiveness of detection in industrial networks, which necessitate continuous operation and undergo changes over time, intrusion detection algorithms should possess the capability to automatically adapt to these changes. Several researchers have focused on the automatic detection of these attacks, in which deep learning (DL) and machine learning algorithms play a prominent role. This study proposes a hybrid model that combines two DL algorithms, namely convolutional neural networks (CNN) and deep belief networks (DBN), for intrusion detection in industrial networks. To evaluate the effectiveness of the proposed model, we utilized the Multi-Step Cyber Attack (MSCAD) dataset and employed various evaluation metrics.

Substantial increase in abrupt shifts between drought and flood events in China based on observations and model simulations.

Drought-flood abrupt alternation (DFAA) refers to the rapid transformation between droughts and floods, posing serious threats to ecological security, food production, and human safety. Previous studies have insufficiently investigated DFAA events at large regional scales using high-resolution observations and model simulations. In this study, the standardized precipitation evapotranspiration index was used to construct the DFAA magnitude index, which considers the asymmetric effects of drought and flood alternations. Four types of DFAA events were then investigated using high-resolution station observations and NEX-GDDP-CMIP6 model simulations. The results showed that hotspot areas of drought-flood and flood-drought alternation events were mainly in the northern and eastern parts of China, while the hotspot areas of drought-flood-drought and flood-drought-flood alternation events were obviously smaller than those of drought-flood and flood-drought alternation events. Drought-flood, flood-drought, and drought-flood-drought alternation events showed significant upward trends at rates of 0.075, 0.057, and 0.051 events/decade, respectively, and these increases were attributed to significant increases in moderate, severe, and extreme events across China during 1981-2020. Generally, the total number of DFAA events above moderate grade in the northern, central, and some areas in the southern parts of China increased obviously (>50 %) during 2001-2020 compared to 1981-2000. NEX-GDDP-CMIP6 can reasonably represent the multi-year averages and long-term trends of precipitation, temperature, and DFAA events in China. Except for the flood-drought-flood alternation events, the other three types of DFAA events showed significant increasing trends in the future, with higher rates under the SSP585 scenario than under the SSP245 scenario (e.g., drought-flood alternation events at rates of 0.033 and 0.046 events/decade under SSP245 and SSP585, respectively, during 1981-2100). DFAA events above the moderate grade were predicted to increase significantly in both 2032-2065 and 2066-2099 compared to 1981-2014, especially in northern China for the 2066-2099 under the SSP585 scenario.

Targeted metabolomics reveals fatty acid abundance adjustments as playing a crucial role in drought-stress response and post-drought recovery in wheat.

Drought stress is one of the abiotic stresses restricting plant development, reproductive growth, and survival. In the present study, the effect of drought stress and post-drought recovery for the selected local wheat cultivar, Atta Habib, was studied. Wheat was grown for 16 days followed by drought stress for 7 days and allowed to recover for 7 days after the removal of the drought stress. Same-aged untreated plants were also grown as a control. The effect of drought stress and post-drought recovery on morphology (root length, shoot length, root weight, and shoot weight), enzymatic activity, and fatty acid profile were analyzed. The results showed that shoot weight (93.1 mg), root weight (85.2 mg), and shoot length (11.1 cm) decreased in the stressed plants but increased steadily in the recovered plants compared to the same-aged control plants, while root length showed a higher increase (14.0 cm) during drought stress and tended to normalize during the recovery phase (13.4 cm). The ascorbate peroxidase activity increased in the stressed plants (5.44 unit/mg protein) compared to the control, while gradually normalizing in the recovery phase (5.41 unit/mg protein). Gas chromatography coupled mass spectrometric analysis revealed abundance changes in important fatty acids, such as palmitic acid, stearic acid, oleic acid, linoleic acid, and linolenic acid. Palmitic acid (39.1%) and oleic acid (2.11%) increased in the drought-stressed plants, while a reduction in linoleic acid (6.85%) and linolenic acid (51.18%) was observed compared to the same-aged control plants, i.e., palmitic (33.71%), oleic (0.95%), linoleic (7.52%), and linolenic acid (55.23%). The results suggest that wheat tries to recover in the post-drought stage by repairing oxidative damage through ascorbate peroxidase, and by adjusting fatty acid abundances under drought stress and during the post-drought phase in an effort to maintain membranes' integrity and a suitable fat metabolism route, thus helping recovery. Targeted metabolomics may be further used to explore the role of other metabolites in the drought-stress response mechanism in wheat. Furthermore, this relatively little explored avenue of post-drought recovery needs more detailed studies involving multiple stress durations.

In-Plane Seismic Strengthening of Brick Masonry Using Steel and Plastic Meshes.

Unreinforced masonry structures are vulnerable to seismic loading due to their brittle behavior, and must therefore be strengthened. This paper presents the seismic performance of brick masonry strengthened with steel and plastic meshes. For this purpose, twenty masonry wallets of (600 × 600 × 113 mm) were constructed, keeping the same materials and workmanship. Fifteen of them were reinforced using steel and plastic meshes. These specimens were tested for in-plane static cyclic diagonal tension (shear) behavior. The critical parameters, such as shear stress, strain, failure modes, ductility, energy dissipation, and stiffness degradation were investigated. Compared to reference and plastic-reinforced specimens, the steel-reinforced samples were found to be highly effective. Furthermore, the recommended category of steel increased the shear capacity, energy dissipation, and ductility ratio by 1.3, 14, and 6.3 times, respectively.

A New Intrusion Detection System for the Internet of Things via Deep Convolutional Neural Network and Feature Engineering.

The Internet of Things (IoT) is a widely used technology in automated network systems across the world. The impact of the IoT on different industries has occurred in recent years. Many IoT nodes collect, store, and process personal data, which is an ideal target for attackers. Several researchers have worked on this problem and have presented many intrusion detection systems (IDSs). The existing system has difficulties in improving performance and identifying subcategories of cyberattacks. This paper proposes a deep-convolutional-neural-network (DCNN)-based IDS. A DCNN consists of two convolutional layers and three fully connected dense layers. The proposed model aims to improve performance and reduce computational power. Experiments were conducted utilizing the IoTID20 dataset. The performance analysis of the proposed model was carried out with several metrics, such as accuracy, precision, recall, and F1-score. A number of optimization techniques were applied to the proposed model in which Adam, AdaMax, and Nadam performance was optimum. In addition, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. All experimental analysis indicates that the accuracy of the proposed approach is high and more robust than existing DL-based algorithms.

Association between erectile dysfunction, cardiovascular risk factors, and coronary artery disease: Role of exercise stress testing and International Index of Erectile Function (IIEF-5) questionnaire.

Background: The exercise stress test (EST) is a non-invasive investigation to diagnose coronary artery disease. This research aimed to determine the relationship between erectile dysfunction (ED), cardiovascular risk factors, and coronary artery disease (CAD) in men referred for EST. Methods: A prospective cohort study enrolling 303 patients from August 2020 through September 2021. All patients filled out the international Index of Erectile Function (IIEF-5) questionnaire. . They underwent an exercise stress test (EST). A two-tailed independent sample t-test, chi-square tests, and binary logistic regression were used for statistical analysis. Results: EST was positive in 110 (36.3%) patients, negative in 154 (50.8%), and inconclusive in 39 (12.8%) patients. ED was present in 225 (74.3%) patients and absent in 78 (25.8%) patients. 278 (91.7%) had one or more cardiovascular risk factors. This study reported a significant relationship between diabetes mellitus (DM), hypertension (HTN), and the results of EST and ED. Compared to patients without ED, patients with ED had a positive EST result. Definite CAD was diagnosed in 21% of patients with ED compared to 1.3% in patients without ED. For a one-unit increase in age, the odds of ED increased by about 5%. Similarly, a negative EST is compared to a positive EST. Negative EST reduced the likelihood of ED by 82%. Conclusions: This research found a statistically significant connection between CAD, certain cardiovascular risk factors, and ED using the EST and IIEF-5 questionnaires. This research is significant because it may alter the way cardiovascular risk stratification is done.

Breast Cancer; Discovery of Novel Diagnostic Biomarkers, Drug Resistance, and Therapeutic Implications.

Breast cancer is the second most reported cancer in women with high mortality causing millions of cancer-related deaths annually. Early detection of breast cancer intensifies the struggle towards discovering, developing, and optimizing diagnostic biomarkers that can improve its prognosis and therapeutic outcomes. Breast cancer-associated biomarkers comprise macromolecules, such as nucleic acid (DNA/RNA), proteins, and intact cells. Advancements in molecular technologies have identified all types of biomarkers that are exclusively studied for diagnostic, prognostic, drug resistance, and therapeutic implications. Identifying biomarkers may solve the problem of drug resistance which is a challenging obstacle in breast cancer treatment. Dysregulation of non-coding RNAs including circular RNAs (circRNAs) and microRNAs (miRNAs) initiates and progresses breast cancer. The circulating multiple miRNA profiles promise better diagnostic and prognostic performance and sensitivity than individual miRNAs. The high stability and existence of circRNAs in body fluids make them a promising new diagnostic biomarker. Many therapeutic-based novels targeting agents have been identified, including ESR1 mutation (DNA mutations), Oligonucleotide analogs and antagonists (miRNA), poly (ADP-ribose) polymerase (PARP) in BRCA mutations, CDK4/6 (cell cycle regulating factor initiates tumor progression), Androgen receptor (a steroid hormone receptor), that have entered clinical validation procedure. In this review, we summarize the role of novel breast cancer diagnostic biomarkers, drug resistance, and therapeutic implications for breast cancer.

HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles.

Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets-a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework.

Changes in kinematics, kinetics, and muscle activity in patients with lumbar spinal stenosis during gait: systematic review.

BACKGROUND CONTEXT: Lumbar spinal stenosis (LSS) is one of the most common orthopaedic conditions and affects more than half a million people over the age of 65 in the US. Patients with LSS have gait dysfunction and movement deficits due to pain and symptoms caused by compression of the nerve roots within a narrowed spinal canal. PURPOSE: The purpose of the current systematic review was to summarize existing literature reporting biomechanical changes in gait function that occur with LSS, and identify knowledge gaps that merit future investigation in this important patient population. STUDY DESIGN/SETTING: This study is a systematic literature review. OUTCOME MEASURES: The current study included biomechanical variables (e.g., kinematic, kinetic, and muscle activity parameters). METHODS: Relevant articles were selected through MEDLINE, Scopus, Embase, and Web of Science. Articles were included if they: 1) included participants with LSS or LSS surgery, 2) utilized kinematic, kinetic, or muscle activity variables as the primary outcome measure, 3) evaluated walking or gait tasks, and 4) were written in English. RESULTS: A total of 11 articles were included in the current systematic review. The patients with LSS exhibited altered gait function as compared to healthy controls. Improvements in some biomechanical variables were found up to one year after surgery, but most gait changes were found within one month after surgery. CONCLUSIONS: Although numerous studies have investigated gait function in patients with LSS, gait alterations in joint kinetics and muscle activity over time remain largely unknown. In addition, there are limited findings of spinal kinematics in patients with LSS during gait. Thus, future investigations are needed to investigate longer-term gait changes with regard to spinal kinematics, joint kinetics, and muscle activity beyond one month after LSS surgery.

An Experimental and Empirical Study on the Use of Waste Marble Powder in Construction Material.

Marble is currently a commonly used material in the building industry, and environmental degradation is an inevitable consequence of its use. Marble waste occurs during the exploitation of deposits using shooting technologies. The obtained elements most mainly often have an irregular geometry and small dimensions, which excludes their use in the stone industry. There is no systematic way of disposing of these massive mounds of waste, which results in the occurrence of landfills and environmental pollution. To mitigate this problem, an effort was made to incorporate waste marble powder into clay bricks. Different percentage proportions of marble powder were considered as a partial substitute for clay, i.e., 5-30%. A total of 105 samples were prepared in order to assess the performance of the prepared marble clay bricks, i.e., their water absorption, bulk density, apparent porosity, salt resistance, and compressive strength. The obtained bricks were 1.3-19.9% lighter than conventional bricks. The bricks with the addition of 5-20% of marble powder had an adequate compressive strength with regards to the values required by international standards. Their compressive strength and bulk density decreased, while their water absorption capacity and porosity improved with an increased content of marble powder. The obtained empirical equations showed good agreement with the experimental results. The use of waste marble powder in the construction industry not only lowers project costs, but also reduces the likelihood of soil erosion and water contamination. This can be seen to be a crucial factor for economic growth in agricultural production.

Rifampicin conjugated silver nanoparticles: a new arena for development of antibiofilm potential against methicillin resistant Staphylococcus aureus and Klebsiella pneumoniae.

Background: Infections caused by drug resistant bacteria are a major health concern worldwide and have prompted scientists to carry out efforts to overcome this challenge. Researchers and pharmaceutical companies are trying to develop new kinds of antimicrobial agents by using different physical and chemical methods to overcome these problems. Materials and methods: In the present study, rifampicin conjugated silver (Rif-Ag) nanoparticles have successfully been synthesized using a chemical method. Characterization of the nanoparticles was performed using a UV-Vis spectrophotometer, FTIR, SEM, TEM, and AFM. Results: The AFM, SEM, and TEM results showed that the average particle size of Rif-Ag nanoparticles was about 15-18±4 nm. The FTIR spectra revealed the conjugation of -NH2 and -OH functional moiety with silver nanoparticles surface. Considering the penetrating power of rifampicin, the free drug is compared with synthesized nanoparticle for antimicrobial, biofilm inhibition, and eradication potential. Synthesized nanoparticles were found to be significantly active as compared to drug alone. Conclusion: This study has shown greater biofilm inhibitory and eradicating potential against methicillin resistant Staphylococcus aureus and Klebsiella pneumoniae, as evident by crystal violet, MTT staining, and microscopic analysis. So, it will be further modified, and studies for the mechanism of action are needed.

Synergistic combinatorial antihyperlipidemic study of selected natural antioxidants; modulatory effects on lipid profile and endogenous antioxidants.

BACKGROUND: Hyperlipidemia, a major pathological condition associated with disrupted lipid levels and physiological redox homeostasis. The excessive release of reactive oxygen species (ROS) leads to enhanced lipid peroxidation, aggravated atherosclerosis and oxidative stress. Integration of natural antioxidant blends in alone or with conventional treatments can alleviate these issues synergistically contributing least side effects. Published literature reported the efficacy of natural antioxidants as individual and in combinations in various conditions but less data is available on their evaluation in low dose ratio blends particularly in hypercholesterolemic diet. METHODS: Antihyperlipidemic effects of selected natural antioxidants; the phenolic oligomeric proanthocyanidins (OPC) and pterostilbene (PT) with niacin (NA) were investigated in current study. Their effects on lipid profile, lipid peroxidation and their aptitude to establish redox state between oxidants and antioxidants in body were evaluated in high cholesterol diet fed animal model. Male albino rabbits (n = 6) weighing 1.2-1.6 kg, supplemented with high cholesterol diet (400 mg/kg) for 12 weeks were used in the experiment. Antioxidants were administered individual high (100 mg/kg) and in low dose combinations (total dose = 100 mg/kg). Student's t test and one way analysis of variance (ANOVA) followed by Dunnet's test were used as statistical tools for evaluation. RESULTS: The results showed synergistic effects of low dose antioxidant blends. Therapies retarded elevation in blood lipid levels, lipid peroxidation and blood antioxidant depletion and consequently contributed in reestablishing redox homeostasis. The LDL/HDL ratio and atherogenic index were suppressed significantly in blend therapies with maximum effects of 59.3 and 25 % (p >0.001) observed in 50:30:20 ratios of OPC, NA and PT, compared to individual therapies 37 and 18 % max respectively. Moreover the results were also in close proximity with the statin therapy (52.66, 26.28 %). CONCLUSION: This study provides an evidence for natural antioxidants blends superiority over individual therapy in chronic diseases like hyperlipidemia. Such therapies in human equivalent doses can help in mitigating chronic illnesses in general populations.

Status of hospital infection control measures at seven major tertiary care hospitals of northern punjab.

OBJECTIVE: To determine the availability and implementation of various hospital infection control measures at tertiary care hospitals. STUDY DESIGN: Survery. PLACE AND DURATION OF STUDY: National Institute of Science and Technology, Islamabad, from June through August 2008. METHODOLOGY: Seven tertiary care very busy hospitals were selected; one from Islamabad, 5 from Rawalpindi, and one from Lahore. A detailed proforma was designed addressing all the issues pertaining to hospital infection control measures. Air sampling was done and growth yielded was identified by standard methods. RESULTS: Analyses revealed that all of the hospitals had an Infection Control Committee. Microbiological diagnostic facilities were adequate at all the hospitals and overall microorganism yield was very high. Antibiotic policy was claimed by most, not available on ground. Majority of the operation theatres were without proper air flow system and autoclaves were not being regularly monitored. There was no proper disposal for sharps and needles. Incineration was not the usual mode for infectious waste. CONCLUSION: The results of the present study imply availability of proper hospital infection control policies with need of strict implementation of such measures.