Aiding the well celebrated Kuk's randomized response technique through auxiliary and prior information.

Asking direct questions in face to face surveys about sensitive traits is an intricate issue. One of the solutions to this issue is the randomized response technique (RRT). Being the most widely used indirect questioning technique to obtain truthful data on sensitive traits in survey sampling RRT has been applied in a variety of fields including behavioral science, socio-economic, psychological, epidemiology, biomedical, criminology, data masking, public health engineering, conservation studies, ecological studies and many others. This paper aims at exploring the methods to subsidize the randomized response technique through additional information relevant to the parameter of interest. Specifically, we plan to contribute by proposing more efficient hybrid estimators compared to existing estimator based on (Kuk, 1990) [31] family of randomized response models. The proposed estimators are based on the methodology of incorporating the pertinent information, available on the basis of either historical records or expert opinion. Specifically, in case of availability of auxiliary information, the regression-cum-ratio estimator is found to be the best to further enhance the estimation through (Kuk, 1990) [31] model while the (Thompson, 1968) [49] shrinkage estimation is observed to be yielding more precise and accurate estimator of sensitive proportion. The findings in this study signify the importance of the proposed methodology. Additionally, to support the mathematical findings, a detailed numerical investigation to evaluate the comparative performances is also conducted. Based on performance analysis, overwhelming evidences are witnessed in the favor of proposed strategies.

Enhancing heart disease prediction using a self-attention-based transformer model.

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction.

Trends in Monkeypox transmission: Investigation into 30 most affected countries.

This research is based on the analysis of Monkeypox transmission, from may 7, 2022 to October 11, 2022, in 30 most affected countries around the globe. The most affected countries are selected through the cut point of at least 100 reported confirmed cases of infected persons over the aforementioned time span. We novely argue the exhibition of distributional similarities between the viral flow and well known power law in context of this neglected zoonotic disease. Moreover, model-based evidence suggesting the capability of pathogen to spread far and wide around its nucleus, are collected and presented. It is estimated that 70 % of the reported confirmed cases belonged to 20 % of the top most affected countries. Also, 70 % of the reported transmission was inflicted in 34 % of the days of reporting at least one case, on average.

Epidemiological analysis of anaplasmosis in cattle from Khyber Pakhtunkhwa, Pakistan.

Background and Aim: Anaplasmosis, a tick-borne disease affecting livestock caused by the bacteria Anaplasma, poses a global concern. This study aimed to estimate the prevalence, spatiotemporal variation, and associated risk factors of anaplasmosis in cattle from the Bannu and Lakki Marwat districts of Khyber Pakhtunkhwa, Pakistan. Materials and Methods: This study used 197 cattle exhibiting clinical symptoms of anaplasmosis in natural settings. Microscopic examination was used to estimate the prevalence. Potential risk factors, such as sampling regions and months, gender, breed, and age were studied. Results: The study revealed an overall anaplasmosis prevalence of 19.79%. Bannu district exhibited a higher occurrence at 22.10%, compared to Lakki Marwat district at 17.64%. Young cattle (<2 years) demonstrated a notably higher incidence of anaplasmosis (26.78%) compared to adults (>5 years), which had a prevalence of 12.35% (p < 0.05). Female cattle (22.36%) were more susceptible than male cattle (11.11%). Prevalence peaked in June (45.71%) and was lowest in February (3.57%). Crossbred cattle had a higher prevalence (23.52%) than purebred cattle (11.47%). Conclusion: Anaplasmosis can be effectively controlled using a comprehensive approach encompassing selective breeding for resilience, targeted care of young calves and females, effective tick control during warmer months, consistent use of insecticides, and proactive risk factor management. Raising awareness among farmers through diverse channels, including media, is pivotal to bolster tick-borne disease management strategies.

Proteomic map of the differentially expressed proteins in the skin of Ctenopharyngodon idella against Aeromonas hydrophila infection.

The skin mucus of fish is an important part of the innate immune system, which is poorly understood at the proteomic level. The study established a complete map of the proteins in the skin mucus of Ctenopharangdon idella (C. idella) and discussed the Differentially Expressed Proteins (DEPs) after Aeromonas hydrophila (A. hydrophila) infection. Using Label Free Liquid Chromatography-Mass Spectrometry (LC-MS/MS) analysis, a total of 126 proteins were identified as differentially expressed, 89 proteins of which were upregulated, and 37 proteins were downregulated. Functional annotations of DEPs showed that the upregulated proteins in the skin mucus of the treated group were mostly associated with complement system and cytoskeleton proteins, whereas downregulated proteins were associated with metabolism. The key upregulated immune proteins were transferrin variant C, lysozyme g, annexin A11, 26S proteasome non-ATPase regulatory subunit 8, hypothetical protein ROHU_000884, 60S ribosomal L7a, calpain-2 catalytic subunit-like protein, calpain-9-like protein, complement component C9, complement C3, cathepsin S, cathepsin Z, 14 kDa apolipo, heat shock protein and intelectin, whereas, leukocyte elastase inhibitor, annexin A11, C-factor-like protein, biotinidase isoform X1 and epidermal growth factor receptor substrate 15-like were the downregulated proteins. Moreover, we for the first-time report proteins such as coactosin, lamin-B2 and kelch 12, which were never reported in fish. Our study directly pointing out the possible immunological biomarkers in the skin mucus of C. idella after A. hydrophila treatment. Each of the protein we report in this study could be used as base to establish their mechanism of action during bacterial infection that may contribute to the strategies against bacterial prevention and control in fishes.

Variations in the Behaviour, Survival, Haematology, and Biochemistry of Ctenopharyngodon idella (Grass Carp) After Exposure to Commercial Grade Atrazine.

The current study assessed the harmful effects of Atrazine (ATZ) herbicide on haematology and biochemistry of the freshwater fish Ctenopharyngodon idella, a commercially significant fish in Pakistan. C. idella (13 ± 8.4 cm; 132 ± 5.6 g) was exposed to graded levels of ATZ, and its 96-hour LC50 value at 25°C was calculated to be 150.5 µl/L. After exposure to ATZ, fish displayed rapid movements, a loss of balance in position and equilibrium, anxious swimming patterns, colour changes, and increased mucous production. The MCHC, MCH, RBCs, and Hb in C. idella decreased significantly (P < 0.05), whereas the MCV, Ht, and WBCs were significantly increased (P < 0.05). At different time intervals (24, 48, 72, and 96 h) following ATZ administration (50, 100, 150, and 200 µl/L), biochemical analysis significantly decreased (P < 0.05) triglyceride, total protein, cholesterol, and albumin levels, whereas glucose levels significantly increased (P < 0.05). We concluded that ATZ is toxic to C. idella, altering their haematology and blood biochemistry even after only a brief exposure.

Rivaroxaban-loaded SLNs with treatment potential of deep vein thrombosis: in-vitro, in-vivo, and toxicity evaluation.

OBJECTIVES: Rivaroxaban (RXB), a novel Xa inhibitor having groundbreaking therapeutic potential. However, this drug is associated with few limitations, including its pharmacokinetics related toxicities. Here, we developed RXB-loaded SLNs (RXB-SLNs) to improve its biopharmaceutical profile. Methods: High pressure homogenizer was used to prepare RXB-SLNs, followed by their particle characterization, Transmission electron microscopy (TEM), Dynamic light scattering (DSC), and Powder X-ray diffraction (PXRD) analysis. Beside this, in-vitro, ex-vivo, and in-vivo evaluation, prothrombin time assessment and toxicity was investigated. RESULTS: RXB-SLNs had their particle size in nano range (99.1 ± 5.50 nm) with excellent morphology and low polydispersity index (0.402 ± 0.02) and suitable zeta potential (-25.9 ± 1.4 mV). The incorporation efficiency was observed around 95.9 ± 3.9%. In-vitro release profiles of the RXB-SLNs exhibited enhanced dissolution (89 ± 9.91%) as compared to pure drug (11 ± 1.43%) after 24 h of the study. PK study demonstrated a seven times enhanced bioavailability of RXB-SLNs when compared with pure drug. Furthermore, RXB-SLNs exhibited an expressive anti-coagulant behavior in human and rat blood plasma. Also, the final formulation exhibited no toxicity after oral administration of the SLNs. CONCLUSIONS: All together, these studies revealed the capability of the SLNs for carrying the RXB with enhanced therapeutic efficacy and no toxicity, most importantly for the treatment of deep vein thrombosis.

A new improved generalized class of estimators for population distribution function using auxiliary variable under simple random sampling.

This article aims to suggest a new improved generalized class of estimators for finite population distribution function of the study and the auxiliary variables as well as mean of the usual auxiliary variable under simple random sampling. The numerical expressions for the bias and mean squared error (MSE) are derived up to first degree of approximation. From our generalized class of estimators, we obtained two improved estimators. The gain in second proposed estimator is more as compared to first estimator. Three real data sets and a simulation are accompanied to measure the performances of our generalized class of estimators. The MSE of our proposed estimators is minimum and consequently percentage relative efficiency is higher as compared to their existing counterparts. From the numerical outcomes it has been shown that the proposed estimators perform well as compared to all considered estimators in this study.

Kinetic and thermodynamic studies of novel acid phosphatase isolated and purified from Carthamus oxyacantha seedlings.

Acid phosphatase (ACP) is a key enzyme in the regulation of phosphate feeding in plants. In this study, a new ACP from C. oxyacantha was isolated to homogeneity and biochemically described for the first time. Specific activity (283 nkat/mg) was found after 2573 times purification fold and (17 %) yield. Using SDS-PAGE under denaturing and nondenaturing conditions, ACP was isolated as a monomer with a molecular weight of 36 kDa. LC-MS/MS confirmed the presence of this band, suggesting that C. oxycantha ACP is a monomer. The enzyme could also hydrolyze orthophosphate monoester with an optimal pH of 5.0 and a temperature of 50 °C. Thermodynamic parameters were also determined (Ea, ΔH°, ΔG°, and ΔS°). ACP activity was further studied in the presence of cysteine, DTT, SDS, EDTA, ß-ME, Triton-X-100 H2O2, and PMSF. The enzyme had a Km of 0.167 mM and an Ea of 9 kcal/mol for p-nitrophenyl phosphate. The biochemical properties of the C. oxyacantha enzyme distinguish it from other plant acid phosphatases and give a basic understanding of ACP in C. oxyacantha. The results of this investigation also advance our knowledge about the biochemical significance of ACP in C. oxyacantha. Thermal stability over a wide pH and temperature range make it more suitable for use in harsh industrial environments. However, further structural and physiological studies are anticipated to completely comprehend its important aspects in oxyacantha species.

Prediction of α-Glucosidase Inhibitory Activity of LC-ESI-TQ-MS/MS-Identified Compounds from Tradescantia pallida Leaves.

Diabetes is a chronic disease that leads to abnormal carbohydrate digestion and hyperglycemia. The long-term use of marketed drugs results in secondary infections and side effects that demand safe and natural substitutes for synthetic drugs. The objective of this study is to evaluate the antidiabetic potential of compounds from the leaves of Tradescantia pallida. Thirteen phenolic compounds were identified from the ethyl acetate fraction of leaves of Tradescantia pallida using liquid chromatography-mass spectrometry. The compounds were then studied for the type of interactions between polyphenols and human α-glucosidase protein using molecular docking analysis. Prime Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) calculations were performed to measure the binding free energies responsible for the formation of ligand-protein complexes. The compounds were further investigated for the thermodynamic constraints under a specified biological environment using molecular dynamic simulations. The flexibility of the ligand-protein systems was verified by Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF) and molecular interactions. The results authenticated the antidiabetic potential of polyphenols identified from the leaves of Tradescantia pallida. Our investigations could be helpful in the design of safe antidiabetic agents, but further in vitro and in vivo investigations are required.

A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling.

In this article, we proposed an improved finite population variance estimator based on simple random sampling using dual auxiliary information. Mathematical expressions of the proposed and existing estimators are obtained up to the first order of approximation. Two real data sets are used to examine the performances of a new improved proposed estimator. A simulation study is also recognized to assess the robustness and generalizability of the proposed estimator. From the result of real data sets and simulation study, it is examining that the proposed estimator give minimum mean square error and percentage relative efficiency are higher than all existing counterparts, which shown the importance of new improved estimator. The theoretical and numerical result illustrated that the proposed variance estimator based on simple random sampling using dual auxiliary information has the best among all existing estimators.

Anti-Inflammatory, analgesic and anti-pyretic activity of Fagonia bruguieri DC in rats.

Traditional medicine has employed the plant Fagonia bruguieri DC. to alleviate inflammation, fever and pain. The goal of this study was to test the anti-inflammatory, analgesic and antipyretic properties of the methanol extract of whole plant of Fagonia bruguieri (F. bruguieri). The writhing test and Eddy's hot plate test were used to assess the analgesic potential of F. bruguieri at three different doses. Carrageenan-induced rat paw edema was applied to investigate anti-inflammatory activity, whereas antipyretic activity was estimated in Brewer's yeast induced pyrexia model. Flavonoids, alkaloids, saponins, tannins and glycosides were found in F. bruguieri's phytochemical analysis. F. bruguieri at 750 mg/kg reduced writhing count by 62.23 percent, while F. bruguieri enhanced latency in Eddy's hot plate test. In carrageenan-induced edema, F. bruguieri at 750 mg/kg exhibited considerable anti-inflammatory effect (41.11 percent) after 2 nd, 3 rd and 4 th hours of therapy. F. bruguieri was also found to show antipyretic properties. The anti-inflammatory, analgesic and antipyretic properties of F. bruguieri were confirmed in this study, which might be attributable to the presence of several phyto-constituents.

A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection.

Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body's normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of cancer since they may help cancer survivors with treatment in the early stage. It must also divide cancer patients into high- and low-risk groups. While realizing efficient detection of cancer is frequently a time-taking and exhausting task with the high possibility of pathologist errors and previous studies employed data mining and machine learning (ML) techniques to identify cancer, these strategies rely on handcrafted feature extraction techniques that result in incorrect classification. On the contrary, deep learning (DL) is robust in feature extraction and has recently been widely used for classification and detection purposes. This research implemented a novel hybrid AlexNet-gated recurrent unit (AlexNet-GRU) model for the lymph node (LN) breast cancer detection and classification. We have used a well-known Kaggle (PCam) data set to classify LN cancer samples. This study is tested and compared among three models: convolutional neural network GRU (CNN-GRU), CNN long short-term memory (CNN-LSTM), and the proposed AlexNet-GRU. The experimental results indicated that the performance metrics accuracy, precision, sensitivity, and specificity (99.50%, 98.10%, 98.90%, and 97.50) of the proposed model can reduce the pathologist errors that occur during the diagnosis process of incorrect classification and significantly better performance than CNN-GRU and CNN-LSTM models. The proposed model is compared with other recent ML/DL algorithms to analyze the model's efficiency, which reveals that the proposed AlexNet-GRU model is computationally efficient. Also, the proposed model presents its superiority over state-of-the-art methods for LN breast cancer detection and classification.

Comparative analysis of gut microbiota in healthy and diarrheic yaks.

BACKGROUND: Yak (Bos grunniens) mainly inhabiting Tibet Plateau, displayed a high incidence of diarrhea due to harsh living environment and nutritional deficit. Gut microbial community has been reported to be closely related to many diseases including diabetes, obesity and inflammatory bowel disease, but information regarding diarrheic influence on gut microbiota in yaks remains scarce. Here, this study was performed to investigate the gut bacterial and fungal alternations of diarrheic yaks. RESULTS: Results revealed that the gut bacterial and fungal communities of diarrheic yaks showed a distinct decline in alpha diversity, accompanied by significant shifts in taxonomic compositions. Specifically, diarrhea caused a distinct increase in the relative abundance of 1 phylum and 8 genera as well as a distinct decrease in 3 phyla and 30 genera. Fungal taxonomic analysis indicated that the relative richness of 1 phylum and 2 genera dramatically increased, whereas the relative richness of 2 phylum and 43 genera significantly decreased during diarrhea. Surprisingly, 2 bacterial genera and 5 fungal genera even cannot be detected in the gut microbiota of diarrheic yaks. CONCLUSIONS: In summary, this study indicated that the gut bacterial and fungal compositions and diversities of yaks altered significantly during diarrhea. Moreover, these findings also contribute to understanding the gut microbial composition and diversity of yaks and developing strategies to alleviate and prevent diarrhea from gut microbial perspective.

Long-term hexavalent chromium exposure disturbs the gut microbial homeostasis of chickens.

Industrial production, ore smelting and sewage disposal plant can discharge large amounts of heavy metals every year, which may contaminate soil, water and air, posing a great threat to ecological environment and animal production. Hexavalent chromium [Cr (VI)], a recognized metallic contaminant, has been shown to impair kidney, liver and gastrointestinal tract of many species, but little is known about the gut microbial characteristics of chickens exposed to Cr (VI). Herein, this study characterized the gut microbial alternations of chickens exposed to Cr (VI). Results indicated that the gut microbial alpha-diversity in chickens exposed to Cr (VI) decreased significantly, accompanied by a distinct shifts in taxonomic composition. Microbial taxonomic analysis demonstrated that the preponderant phyla (Firmicutes, Bacteroidetes, Proteobacteria and Epsilonbacteraeota) were the same in both groups, but different in types and relative abundances of dominant genera. Moreover, some bacterial taxa including 2 phyla and 47 genera significantly decreased, whereas 3 phyla and 17 genera significantly increased during Cr (VI) exposure. Among decreased taxa, 9 genera (Coprobacter, Ruminococcus_1, Faecalicoccus, Eubacterium_nodatum_group, Parasutterella, Slackia, Barnesiella, Family_XIII_UCG-001 and Collinsella) even cannot be detected. In conclusion, this study revealed that Cr (VI) exposure dramatically decrased the gut microbial diversity and altered microbial composition of chickens. Additionally, this study also provided a theoretical basis for relieving Cr (VI) poisoning from the perspective of gut microbiota.

Virome in the cloaca of wild and breeding birds revealed a diversity of significant viruses.

BACKGROUND: Wild birds may harbor and transmit viruses that are potentially pathogenic to humans, domestic animals, and other wildlife. RESULTS: Using the viral metagenomic approach, we investigated the virome of cloacal swab specimens collected from 3182 birds (the majority of them wild species) consisting of > 87 different species in 10 different orders within the Aves classes. The virus diversity in wild birds was higher than that in breeding birds. We acquired 707 viral genomes from 18 defined families and 4 unclassified virus groups, with 265 virus genomes sharing < 60% protein sequence identities with their best matches in GenBank comprising new virus families, genera, or species. RNA viruses containing the conserved RdRp domain with no phylogenetic affinity to currently defined virus families existed in different bird species. Genomes of the astrovirus, picornavirus, coronavirus, calicivirus, parvovirus, circovirus, retrovirus, and adenovirus families which include known avian pathogens were fully characterized. Putative cross-species transmissions were observed with viruses in wild birds showing > 95% amino acid sequence identity to previously reported viruses in domestic poultry. Genomic recombination was observed for some genomes showing discordant phylogenies based on structural and non-structural regions. Mapping the next-generation sequencing (NGS) data respectively against the 707 genomes revealed that these viruses showed distribution pattern differences among birds with different habitats (breeding or wild), orders, and sampling sites but no significant differences between birds with different behavioral features (migratory and resident). CONCLUSIONS: The existence of a highly diverse virome highlights the challenges in elucidating the evolution, etiology, and ecology of viruses in wild birds. Video Abstract.

Improving the Survival Time of Multiagents in Social Dilemmas through Neurotransmitter-Based Deep Q-Learning Model of Emotions.

In multiagent systems, social dilemmas often arise whenever there is a competition over the limited resources. The major challenge is to establish cooperation among intelligent virtual agents for solving the situations of social dilemmas. In humans, personality and emotions are the primary factors that lead them toward a cooperative environment. To make agents cooperate, they have to become more like humans, that is, believable. Therefore, we hypothesize that emotions according to the personality give birth to believability, and if believability is introduced into agents through emotions, it improves their survival rate in social dilemma situations. The existing researches have introduced different computational models to introduce emotions in virtual agents, but they lack emotions through neurotransmitters. We have proposed a neurotransmitters-based deep Q-learning computational model in multiagents that is a suitable choice for emotion modeling and, hence, believability. The proposed model regulates the agents' emotions by controlling the virtual neurotransmitters (dopamine and oxytocin) according to the agent's personality. The personality of the agent is introduced using OCEAN model. To evaluate the proposed system, we simulated a survival scenario with limited food resources in different experiments. These experiments vary the number of selfish agents (higher neuroticism personality trait) and the selfless agents (higher agreeableness personality trait). Experimental results show that by adding the selfless agents in the scenario, the agents develop cooperation, and their collective survival time increases. Thus, to resolve the social dilemma problems in virtual agents, we can make agents believable through the proposed neurotransmitter-based emotional model. This proposed work may help in developing nonplayer characters (NPCs) in games.

On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer's Disease Using Neuroimaging Modalities and Data Augmentation Methods.

Alzheimer's disease (AD) is an irreversible illness of the brain impacting the functional and daily activities of elderly population worldwide. Neuroimaging sensory systems such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) measure the pathological changes in the brain associated with this disorder especially in its early stages. Deep learning (DL) architectures such as Convolutional Neural Networks (CNNs) are successfully used in recognition, classification, segmentation, detection, and other domains for data interpretation. Data augmentation schemes work alongside DL techniques and may impact the final task performance positively or negatively. In this work, we have studied and compared the impact of three data augmentation techniques on the final performances of CNN architectures in the 3D domain for the early diagnosis of AD. We have studied both binary and multiclass classification problems using MRI and PET neuroimaging modalities. We have found the performance of random zoomed in/out augmentation to be the best among all the augmentation methods. It is also observed that combining different augmentation methods may result in deteriorating performances on the classification tasks. Furthermore, we have seen that architecture engineering has less impact on the final classification performance in comparison to the data manipulation schemes. We have also observed that deeper architectures may not provide performance advantages in comparison to their shallower counterparts. We have further observed that these augmentation schemes do not alleviate the class imbalance issue.

Detection and molecular characterization of picobirnaviruses in the wild birds: Identification of a novel picobirnavirus possessing yeast mitochondrial genetic code.

Picobirnaviruses (PBVs) are bi-segmented dsRNA viruses that have been detected in various animal species including vertebrates and invertebrates. In this study, 17 complete or incomplete PBV segment-2 and one unsegmented PBV-like virus sequence were identified in fecal samples from different bird species using viral metagenomic approach. The bird PBV and PBV-like virus retained the conservative motifs that are conserved in dsRNA2 of common PBVs. The RdRp of these 17 PBVs shared the highest Amino acid (aa) identity of 45.90%∼94.19% with previous animal and human PBVs, while the RdRp of the unsegment PBV-like virus shared the highest aa sequence identity of 31.93% with one chicken PBV (GenBank No. MW837829). The unsegmented PBV-like virus unexpectedly used the yeast mitochondrial genetic code (transl_table=3) for all ORFs translation. In addition, the prokaryotic RBS sequence was not only detected upstream to ORF2 at position 360AGGAGG365 of this unsegmented PBV-like virus, but also found upstream to ORF of bird PBV dsRNA2. The presence of the prokaryotic ribosomal binding site in the bird PBV genomes, and the finding of one novel unsegmented PBV-like virus using the yeast mitochondrial genetic code for translation supported recent speculations that PBVs may actually infect prokaryotic or fungal host cells. This study enhanced our understanding of PBVs and provided data support for exploring the real host of PBVs.

Antibiotic susceptibility patterns of bacterial isolates of patients with upper respiratory tract infections

Abstract To evaluate the antibiotic susceptibility patterns in URTIs reporting to tertiary hospitals of Lahore. A cross-sectional study employing 259 culture sensitivity reports obtained from tertiary care hospitals of Lahore. Using SPSS, descriptive statistics were used to estimate frequencies and percentages. In URTIs, S. aureus (5%) was the frequent gram-positive isolate followed by MRSA (1.5%) and MSSA (1.5%), while P. aeruginosa (15.8%) was the prevalent gram-negative isolate followed by Klebsiella (13.1%) and E. coli (6.9%). Against P. aeruginosa, ceftazidime (7.7%), cefuroxime/ceftriaxone (4.6%), amoxicillin (4.3%) and ciprofloxacin (4.2%), were tested resistant, while imipenem (11.2%), ciprofloxacin (9.2%), amikacin (9.2%), meropenem/ levofloxacin/gentamicin (8.1%) and piptaz (6.9%) were found sensitive. Against Klebsiella, carbepenems (7.3%), amikacin (6.5%), ciprofloxacin (5.4%) and gentamicin (5%) were tested sensitive, whereas, ceftazidime (8.5%), ceftriaxone (5.8%), cefaclor (5.5%), ampicillin (4.6%), co-amoxiclave (4.2%) and ciftazidime/ciprofloxacin (3.8%) were found resistant. Overall, imipenem (35%), meropenem (30.8%) and amikacin (31.9%) were the three most sensitive antibiotics, while ceftazidime (25.4%), ceftriaxone (19.2%) and ampicillin (18.5%) were the three most resistant antibiotics. Data suggested that P.aeruginosa and Klebsiella, were the most frequent bacterial isolates in URTIs of Lahore. These isolates were resistant to ampicillin, cefuroxime and ceftazidime, but were sensitive to carbapenem and aminoglycosides