Synthesis and Investigation of Mechanical Properties of the Acrylonitrile Butadiene Styrene Fiber Composites Using Fused Deposition Modeling.

Additive manufacturing is becoming a global phenomenon due to its versatile properties and numerous benefits, which is not possible by conventional machining processes. Fused deposition modeling (FDM) shows a huge potential of shift from rapid prototyping toward the rapid manufacturing. Nowadays, the strength of the FDM-printed parts is very important to consider along with all the printing parameters, which affect the strength of these parts. This study includes the investigation of printing parameters (infill density, layer thickness, and shell count) on the strength of FDM-printed parts of acrylonitrile butadiene styrene (ABS) and carbon fiber-reinforced ABS (ABS-CF). These printing parameters directly affect the quality as well as the strength of the 3D-printed parts through FDM. Tensile tests were performed on the universal testing machine on both types of printed parts. The optimized parameters for the 3D-printed samples of the pristine ABS are found to be 0.1045 mm of layer thickness, 57.72% of infill density, and 7.63 numbers of shell count, while the optimum parameters obtained for ABS-CF are 0.2780 mm of layer thickness, 28.37% of infill density, and 9.88 numbers of shell count. The results show that the layer thickness and shell count have a significant effect on the ultimate tensile strength of the 3D-printed parts.

Predicting short-term thromboembolic risk following Roux-en-Y gastric bypass using supervised machine learning.

BACKGROUND: Roux-en-Y gastric bypass (RYGB) is a widely recognized bariatric procedure that is particularly beneficial for patients with class III obesity. It aids in significant weight loss and improves obesity-related medical conditions. Despite its effectiveness, postoperative care still has challenges. Clinical evidence shows that venous thromboembolism (VTE) is a leading cause of 30-d morbidity and mortality after RYGB. Therefore, a clear unmet need exists for a tailored risk assessment tool for VTE in RYGB candidates. AIM: To develop and internally validate a scoring system determining the individualized risk of 30-d VTE in patients undergoing RYGB. METHODS: Using the 2016-2021 Metabolic and Bariatric Surgery Accreditation Quality Improvement Program, data from 6526 patients (body mass index ≥ 40 kg/m2) who underwent RYGB were analyzed. A backward elimination multivariate analysis identified predictors of VTE characterized by pulmonary embolism and/or deep venous thrombosis within 30 d of RYGB. The resultant risk scores were derived from the coefficients of statistically significant variables. The performance of the model was evaluated using receiver operating curves through 5-fold cross-validation. RESULTS: Of the 26 initial variables, six predictors were identified. These included a history of chronic obstructive pulmonary disease with a regression coefficient (Coef) of 2.54 (P < 0.001), length of stay (Coef 0.08, P < 0.001), prior deep venous thrombosis (Coef 1.61, P < 0.001), hemoglobin A1c > 7% (Coef 1.19, P < 0.001), venous stasis history (Coef 1.43, P < 0.001), and preoperative anticoagulation use (Coef 1.24, P < 0.001). These variables were weighted according to their regression coefficients in an algorithm that was generated for the model predicting 30-d VTE risk post-RYGB. The risk model's area under the curve (AUC) was 0.79 [95% confidence interval (CI): 0.63-0.81], showing good discriminatory power, achieving a sensitivity of 0.60 and a specificity of 0.91. Without training, the same model performed satisfactorily in patients with laparoscopic sleeve gastrectomy with an AUC of 0.63 (95%CI: 0.62-0.64) and endoscopic sleeve gastroplasty with an AUC of 0.76 (95%CI: 0.75-0.78). CONCLUSION: This simple risk model uses only six variables to assist clinicians in the preoperative risk stratification of RYGB patients, offering insights into factors that heighten the risk of VTE events.

The Canadian VirusSeq Data Portal & Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology.

The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform public health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). In addition, the Portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. From inception to execution, the portal was developed with a conscientious focus on strong data governance principles and practices. Extensive efforts ensured a commitment to Canadian privacy laws, data security standards, and organizational processes. This Portal has been coupled with other resources like Viral AI and was further leveraged by the Coronavirus Variants Rapid Response Network (CoVaRR-Net) to produce a suite of continually updated analytical tools and notebooks. Here we highlight this Portal, including its contextual data not available elsewhere, and the 'Duotang', a web platform that presents key genomic epidemiology and modeling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the Portal (COVID-MVP, CoVizu), are all open-source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.

Seroepidemiology and associated risk factors of brucellosis in small ruminants of district Khanewal, Pakistan.

Objectives: Keeping in view the economic and veterinary public health importance of brucellosis, this research was conducted to determine its seroprevalence and associated risk determinants in small ruminants in district Khanewal, Southern Punjab, Pakistan. Materials and Methods: Two-stage cluster sampling technique was used for sampling, and the sample size was calculated using C-survey 2.0. Accordingly, sera samples (n = 392) were collected from small ruminants in the study area from October 2022 to July 2023. All the samples were tested for the presence of anti-Brucella antibodies by Rose Bengal Plate Test (RBPT), followed by confirmation of all the samples using an enzyme linked immunosorbent assay (ELISA) kit (ID.vet®, France; sensitivity and specificity=100%, each). Results: The seropositivity rate of brucellosis was 7.14% [n = 28/392; 95% confidence interval (CI) = 4.87%-10.12%] by RBPT, whereas the results of ELISA showed an overall seroprevalence rate of 7.40% (n = 29/392; 95% CI = 5.11%-10.37%) in the study population. Univariate analysis of risk factors revealed that abortion history (AH), retained fetal membranes (RFMs), repeat breeding, flock size (FS), educational status of farmers (ESFs), awareness about brucellosis (AB), and farm hygiene had a significant association with the seroprevalence of brucellosis (p < 0.05). The multivariate analysis using a binary logistic regression model revealed that variables including tehsil, FS, AH, RFM, ESF, AB, and farming system were significant factors (p < 0.05) associated with brucellosis in the target population. Conclusion: Brucellosis is prevalent in small ruminants in Khanewal, Pakistan. The disease burden can be reduced by improving the reproductive health of animals, farm hygiene, and farmers' awareness about the diseases. Further studies are needed on a larger scale to devise stringent disease control strategies to avoid losses associated with brucellosis at regional, national, and global levels.

Security risk models against attacks in smart grid using big data and artificial intelligence.

The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend against cyber threats. Therefore, a more flexible approach is required to assess data sets and identify hidden risks. This is possible for vast amounts of data due to recent developments in artificial intelligence, machine learning, and deep learning. Due to adaptable base behavior models, machine learning can recognize new and unexpected attacks. Security will be significantly improved by combining new and previously released data sets with machine learning and predictive analytics. Artificial Intelligence (AI) and big data are used to learn more about the current situation and potential solutions for cybersecurity issues with smart grids. This article focuses on different types of attacks on the smart grid. Furthermore, it also focuses on the different challenges of AI in the smart grid. It also focuses on using big data in smart grids and other applications like healthcare. Finally, a solution to smart grid security issues using artificial intelligence and big data methods is discussed. In the end, some possible future directions are also discussed in this article. Researchers and graduate students are the audience of our article.

The multifaceted potential of fenugreek seeds: From health benefits to food and nanotechnology applications.

The present lifestyle, dietary patterns, psychological pressure, environmental factors, and the widespread exploitation of processed substances in food production and farming have collectively contributed to a substantial expediting in the development of various health problems. Globally, researchers have been seeking natural pharmaceutical substances with the potential to be employed in treating lifestyle-related diseases or delaying their onset. Fenugreek seeds have gained significant attention in various fields, including health, nutrition, and cutting-edge nanotechnology applications, due to their versatile qualities. The current investigation offers a comprehensive discussion of the nutritional composition and therapeutic potential of fenugreek seeds, with an emphasis on their plentiful reservoir of bioactive compounds. This seed demonstrates promising medicinal potential in addressing a wide range of health issues. Significantly, these findings indicate noteworthy properties, such as antidiabetic, antioxidant, anti-obesity, hypocholesterolemic, anticancer, and cardioprotective effects. Moreover, the components of fenugreek seeds are important in the development of a multitude of foods, which is the reason why they are used extensively in the area of food research. In addition to their nutritional value, their exploration of nanotechnology reveals a promising domain, utilizing the distinctive characteristics of seeds for many purposes, such as nanoparticle synthesis and oil for edible films and nanoemulsions. This review article focuses on a comprehensive analysis of fenugreek seeds, examining their wide-ranging applications in the fields of health, nutrition, food, and nanotechnology.

Predicting the risk of early intensive care unit admission for patients hospitalized with acute pancreatitis using supervised machine learning.

Background: Acute pancreatitis (AP) is a complex and life-threatening disease. Early recognition of factors predicting morbidity and mortality is crucial. We aimed to develop and validate a pragmatic model to predict the individualized risk of early intensive care unit (ICU) admission for patients with AP. Methods: The 2019 Nationwide Readmission Database was used to identify patients hospitalized with a primary diagnosis of AP without ICU admission. A matched comparison cohort of AP patients with ICU admission within 7 days of hospitalization was identified from the National Inpatient Sample after 1:N propensity score matching. The least absolute shrinkage and selection operator (LASSO) regression was used to select predictors and develop an ICU acute pancreatitis risk (IAPR) score validated by 10-fold cross-validation. Results: A total of 1513 patients hospitalized for AP were included. The median age was 50.0 years (interquartile range: 39.0-63.0). The three predictors that were selected included hypoxia (area under the curve [AUC] 0.78), acute kidney injury (AUC 0.72), and cardiac arrhythmia (AUC 0.61). These variables were used to develop a nomogram that displayed excellent discrimination (AUC 0.874) (bootstrap bias-corrected 95% confidence interval 0.824-0.876). There was no evidence of miscalibration (test statistic = 2.88; P = 0.09). For high-risk patients (total score >6 points), the sensitivity was 68.94% and the specificity was 92.66%. Conclusions: This supervised machine learning-based model can help recognize high-risk AP hospitalizations. Clinicians may use the IAPR score to identify patients with AP at high risk of ICU admission within the first week of hospitalization.

Corrigendum: A metagenomic analysis of mosquito virome collected from different animal farms at Yunnan-Myanmar border of China.

[This corrects the article DOI: 10.3389/fmicb.2020.591478.].

Acute pancreatitis associated with pembrolizumab-induced hypertriglyceridemia.

INTRODUCTION: Acute pancreatitis (AP) following drug-induced hypertriglyceridemia is a rare clinical phenomenon. Immune checkpoint inhibitors have revolutionized treatment for a variety of solid organ and hematological malignancies. Pembrolizumab is a programmed cell death receptor-1 (PD-1) inhibitor that has shown promising responses in many advanced cancers. However, a constellation of immune-related adverse events has also been described. There are reports of pembrolizumab-induced hypertriglyceridemia, but AP as a result of this side effect remains an exceedingly rare clinical sequela. CASE REPORT: We delineate a case of a patient with stage IVB non-small-cell lung cancer who developed progressive abdominal pain and nausea following administration of pembrolizumab for four months. Laboratory studies revealed increased serum lipase and triglyceride levels at 12,562 IU/L and 16,901 mg/dL, respectively. The diagnosis of AP was made based on the revised Atlanta classification criteria. After ruling out alternative causes, pembrolizumab-induced hypertriglyceridemia was considered the likely etiology of AP. MANAGEMENT AND OUTCOME: The patient was transferred to the medical intensive care unit for close monitoring. Treatment was initiated with intravenous fluids, pain medications, and an insulin infusion. However, her hypertriglyceridemia levels remained persistently elevated, necessitating therapeutic apheresis. She recovered well with no complications after triglyceride apheresis. DISCUSSION: AP following pembrolizumab-associated hypertriglyceridemia remains a rare clinicopathologic entity. Given the widespread clinical use of immune checkpoint inhibitors, knowledge of such rare adverse events is crucial. Evaluation of serum triglyceride levels before and after initiating pembrolizumab therapy may be mandated, especially in patients with metabolic comorbidities.

Emotion detection from handwriting and drawing samples using an attention-based transformer model.

Emotion detection (ED) involves the identification and understanding of an individual's emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person's emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).

A deep learning approach for Named Entity Recognition in Urdu language.

Named Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies.

ChestCovidNet: An Effective DL-based Approach for COVID-19, Lung Opacity, and Pneumonia Detection Using Chest Radiographs Images.

Currently used lung disease screening tools are expensive in terms of money and time. Therefore, chest radiograph images (CRIs) are employed for prompt and accurate COVID-19 identification. Recently, many researchers have applied Deep learning (DL) based models to detect COVID-19 automatically. However, their model could have been more computationally expensive and less robust, i.e., its performance degrades when evaluated on other datasets. This study proposes a trustworthy, robust, and lightweight network (ChestCovidNet) that can detect COVID-19 by examining various CRIs datasets. The ChestCovidNet model has only 11 learned layers, eight convolutional (Conv) layers, and three fully connected (FC) layers. The framework employs both the Conv and group Conv layers, Leaky Relu activation function, shufflenet unit, Conv kernels of 3×3 and 1×1 to extract features at different scales, and two normalization procedures that are cross-channel normalization and batch normalization. We used 9013 CRIs for training whereas 3863 CRIs for testing the proposed ChestCovidNet approach. Furthermore, we compared the classification results of the proposed framework with hybrid methods in which we employed DL frameworks for feature extraction and support vector machines (SVM) for classification. The study's findings demonstrated that the embedded low-power ChestCovidNet model worked well and achieved a classification accuracy of 98.12% and recall, F1-score, and precision of 95.75%.

Incorporating the neutrosophic framework into kernel regression for predictive mean estimation.

In traditional statistics, all research endeavors revolve around utilizing precise, crisp data for the predictive estimation of population mean in survey sampling, when the supplementary information is accessible. However, these types of estimates often suffer from bias. The major aim is to uncover the most accurate estimates for the unknown value of the population mean while minimizing the mean square error (MSE). We have employed the neutrosophic approach, which is the extension of classical statistics that deals with the uncertain, vague, and indeterminate information, and proposed a neutrosophic predictive estimator of finite population mean using the kernel regression. The proposed estimator does not yield a single numerical value but instead provides an interval range within which the population parameter is likely to exist. This approach enhances the efficiency of the estimators by offering an estimated interval that encompasses the unknown value of the population mean with the least possible mean squared error (MSE). The simulation-based efficiency of the proposed estimator is discussed using the Sine, Bump and real-time temperature data set of Islamabad by using symmetric (Gaussian) kernel. The proposed non-parametric neutrosophic estimator has shown more effective results under the various bandwidth selectors than the adapted neutrosophic estimators.

Multi-epitopes vaccine design for surface glycoprotein against SARS-CoV-2 using immunoinformatic approach.

Background: The recent COVID vaccinations have successfully reduced death and severity but did not stop the transmission of viruses by the emerging SARS-CoV-2 strain. There is a need for better and long-lasting dynamic vaccines for numerous prevailing strains and the evolving SARS-CoV-2 virus, necessitating the development of broad-spectrum strains being used to stop infection by reducing the spread rate and re-infection. The spike (S) glycoprotein is one of the proteins expressed commonly in the early phases of SARS-CoV-2 infection. It has been identified as the most immunogenic protein of SARS-CoV-2. Methods: In this study, advanced bioinformatics techniques have been exploited to design the novel multi-epitope vaccine using conserved S protein portions from widespread strains of SARS-CoV-2 to predict B cell and T cell epitopes. These epitopes were selected based on toxicity, antigenicity score and immunogenicity. Epitope combinations were used to construct the maximum potent multi-epitope construct with potential immunogenic features. EAAAK, AAY, and GPGPG were used as linkers to construct epitopes. Results: The developed vaccine has shown positive results. After the chimeric vaccine construct was cloned into the PET28a (+) vector for expression screening in Escherichia coli, the potential expression of the construct was identified. Conclusion: The construct vaccine performed well in computer-based immune response simulation and covered a variety of allelic populations. These computational results are more helpful for further analysis of our contract vaccine, which can finally help control and prevent SARS-CoV-2 infections worldwide.

Quantification of toxic heavy metals, trace elements and essential minerals contents in traditional herbal medicines commonly utilized in Khyber Pakhtunkhwa, Pakistan.

Traditional herbal medicines and health supplements have been empirically used to treat various disorders but most of them are not standardized and have not been experimentally validated for safety and efficacy. In the present study, various dosage forms of traditional herbal medicines prescribed for specific diseases were collected from local practitioners at different districts of Khyber Pakhtunkhwa, Pakistan. The collected samples were analyzed for heavy metal, trace elements, and minerals using atomic absorption spectroscopy. All the tested samples contained heavy metals, trace elements and minerals in different concentrations. All the samples were tested positive for the presence of toxic heavy metals such as arsenic (As), cadmium (Cd) and lead (Pb). The trace elements like cobalt (Co), iron (Fe), zinc (Zn) and chromium (Cr) were also detected in acceptable range. Similarly, the samples analyzed were rich in some of the essential minerals such as sodium (Na), magnesium (Mg) and calcium (Ca) which are necessary for the proper functioning of the body. The hazard quotient (HQ) values were measured for toxic heavy metals to determine their safe ranges for human body. The HQ values were above the permissible range for arsenic (As) in all detected samples while for cadmium (Cd) and lead (Pb), the values ware above in 50 % of the analyzed samples. The detection of toxic metals and their HQ values beyond the permissible limits in different dosage forms raised questions about their quality. This study suggests that evaluation of traditional herbal remedies for the metals contents and their standardization are strongly recommended for quality assurance and protection of public health.

Epidemiology of Brucellosis in Small Ruminants of Rural and Peri-Urban Areas of Multan, Pakistan.

Brucellosis is a widespread zoonotic disease of veterinary and public health importance with considerably higher prevalence in developing/underdeveloped countries. This study reports the prevalence and risk determinants of brucellosis in small ruminants of peri-urban and rural areas of district Multan, Southern Punjab, Pakistan. For this purpose, sera samples (n = 392) of small ruminants were collected and subjected to preliminary screening using commercially available RBPT reagents followed by serodetection of brucellosis using multispecies i-ELISA kit (ID.vet, France). All the ELISA positive samples were confirmed by PCR using genus-specific primers, and frequencies of Brucella species in positive samples were enumerated using species-specific primers. Results indicated seropositivity rates of 9.69, 9.95, and 10.20% in study population using RBPT reagents of IDEXX-USA, ID.Vet-France, and VRI-Pakistan, respectively, with a statistically nonsignificant difference (p > 0.05). Results of ELISA showed an overall seroprevalence rate of 7.14% in target population with a slightly higher rate in sheep (7.65%) as compared to goat (6.63%) population (p = 0.695; OR = 1.16, 95% CI = 0.53, 2.57). Results revealed that out of total positive samples, B. abortus was detected in 60.71% of seropositive samples and B. melitensis was detected in 14.28% of positive samples. It was revealed that risk factors including body condition scores, hygienic conditions of the housing facility, farming system, reproductive disorders, educational status of farmers, and awareness of farmers about brucellosis had significant association with brucellosis in small ruminants of study area (p < 0.05). Conversely, farm/herd size, locality, gender, age, weight, and parity showed a nonsignificant association (p > 0.05) with brucellosis. In conclusion, brucellosis is prevalent in small ruminants of Multan, Pakistan. It is recommended to devise and implement effective control strategies with a major focus on raising awareness about brucellosis in farmers for the containment of infection in the region.

Genotypic analysis of hepatitis E virus (HEV) from sporadic symptomatic cases in Pakistan.

Pakistan is the fifth most populous nation in the world and faces several challenges, including devastating floods, sub-optimal sanitary conditions, clustered accommodations, and unregulated cross-border movements. These drastic population shifts make it vulnerable to the efficient spread of the Hepatitis E virus (HEV). The current study analyzed the genotypic characteristics and variants of the Hepatitis E virus circulating in the population of Pakistan. A total of 75 ELISA-IgM positive samples were collected from three metropolitan cities: Lahore, Peshawar, and Karachi, and subjected to viral RNA extraction. The amplification of the HEV RNA-dependent RNA polymerase (RdRp) region was done using Nested PCR and degenerate primers. Out of the total, 40% of the samples were positive for HEV RNA. Sequencing and phylogenetic analysis identified the new HEV isolates as Subtype 1 g, a subtype within an existing HEV genotype 1. This shift warrants investigation into its impact on clinical manifestation and disease severity. Importantly, this study marks the first HEV subtype analysis in Pakistan, contributing valuable insights into subtype diversity and prevalence in the region.

A study on the epidemiology of brucellosis in bovine population of peri-urban and rural areas of district Multan, southern Punjab, Pakistan.

BACKGROUND: Brucellosis is a zoonotic disease caused by a bacterial pathogen belonging to the genus Brucella. It is one of the most frequent bacterial zoonoses globally but unfortunately, it is still considered as a neglected disease in the developing world. Keeping in view, this study was conducted to determine the prevalence and risk determinants of brucellosis in large ruminants of peri-urban and rural areas of district Multan-Pakistan. For this purpose, blood samples (n = 490) were collected from the cattle (n = 245) and buffalo (n = 245) population of the study area and subjected to preliminary screening of brucellosis using local and imported RBPT reagents. All the samples were further analyzed using commercially available multi-specie indirect ELISA kit followed by their confirmation by PCR using genus and species-specific primers. Data obtained from lab analysis and questionnaires were subjected to statistical analysis for Pearson Chi-square, Odds Ratio and Confidence intervals (95%). RESULTS: The results showed that the maximum seropositivity was recorded with local RBPT reagent (VRI, Pakistan; 12.45%; 95%CI = 9.72-15.65%) followed by RBPT-IDEXX (12.24%; 95%CI = 9.52-15.45%) and RBPT-ID.vet (11.84%; 95%CI = 9.18-14.95%) however statistical difference was non-significant (P = 0.956). The ELISA results showed an overall seroprevalence rate of 11.22% (95%CI = 8.59-14.33%) with comparatively higher rate in cattle (12.65%; 95%CI = 8.82-17.44%) as compared to buffaloes (9.80%; 95%CI = 6.49-14.15%). The PCR analysis confirmed the presence of genus Brucella in all seropositive samples whereas frequency of B. abortus and B. melitensis in seropositive samples was 80% and 20%, respectively. The co-existence of both species was also observed in 5.45% samples. The statistical analysis showed a significant association of bovine brucellosis with herd size, breed, reproductive disorders, mode of insemination, educational status and farmers' awareness about brucellosis (P < 0.05). Conversely, locality, age, weight, gender, pregnancy status, parity and puberty status had no associations with brucellosis (P > 0.05). CONCLUSION: In conclusion, brucellosis is prevalent in large ruminants of district Multan, Pakistan. It is suggested to devise and implement stringent policies for the effective control and prevention of brucellosis in the region. Further, the current situation also warrants the need to strengthen interdisciplinary coordination among veterinarians and physicians in one health perspective to ensure and strengthen the human and animal health care systems in the region.

Protein extracted from Moringa oleifera Lam. Leaves: Bio-evaluation and characterization as suitable plant-based meat-protein alternative.

This study aimed to isolate and characterize moringa leaf protein (MLP) via HPLC and evaluate its consumption's effects through rat model. Four groups of Albino Wistar rats (n = 25 each) along with a control group (n = 25) were acclimatized. The isolated MLP was added to the basal diet (casein; control) in various percentages (25, 50, 75, 100%) for a 21-day experimental period. On three intervals (1st, 11th, 21st days), blood samples were collected and subjected for hematological and biochemical examination (Renal Function Test (RFT), Liver Function Test (LFT)). MLP contained a variety of essential and non-essential amino acids in substantial amounts. The Protein Efficiency Ratio (PER) of 50% MLP-treated group was the highest (1.72) among MLP treatments. Increases in feed intake and weight were observed in treated rats compared to the control. The hematological profile of the rats revealed increases in Hemoglobin (Hb) (7.9-14.0%), White Blood Cell (WBC) (35.9-51.5%), Red Blood Cell (RBC) (17.1-22.2%), Hematocrit (HCT) (13.1-22.9%), and platelets levels (36.5-40.6%) from day 1. Protein isolates decreased liver parameters but resulted in non-significant changes in liver and kidney functions in rats. Further investigation is needed to determine the safe daily intake of MLP.

NS2B-D55E and NS2B-E65D Variations are Responsible for Differences in NS2B-NS3 Protease Activities Between Japanese Encephalitis Virus Genotype I and III in Fluorogenic Peptide Model.

Japanese Encephalitis Virus (JEV) NS2B-NS3 is a protein complex composed of NS3 proteases and a NS2B cofactor. The N-terminal protease domain (180 residues) of NS3 (NS3(pro)) interacts directly with a central 40-amino acid hydrophilic domain of NS2B (NS2B(H)) to form an active serine protease. In this study, the recombinant NS2B(H)-NS3(pro) proteases were prepared in E. coli and used to compare the enzymatic activity between genotype I (GI) and III (GIII) NS2B-NS3 proteases. The GI NS2B(H)-NS3(pro) was able to cleave the sites at internal C, NS2A/NS2B, NS2B/NS3 and NS3/NS4A junctions that were identical to the sites proteolytically processed by GIII NS2B(H)-NS3(pro). Analysis of the enzymatic activity of recombinant NS2B(H)-NS3(pro) proteases using a model of fluorogenic peptide substrate revealed that the proteolytical processing activity of GIII NS2B(H)-NS3(pro) was significantly higher than that of GI NS2B(H)-NS3(pro). There were eight amino acid variations between GI and GIII NS2B(H)-NS3(pro), which may be responsible for the difference in enzymatic activities between GI and GIII proteases. Therefore, recombinant mutants were generated by exchanging NS2B(H) and NS3(pro) domains between GI and GIII NS2B(H)-NS3(pro) and subjected to protease activity analysis. Substitution of NS2B(H) significantly altered the protease activities, as compared to the parental NS2B(H)-NS3(pro), suggesting that NS2B(H) played an essential role in regulation of NS3(pro) protease activity. To further identify the amino acids responsible for the difference in protease activities, multiple substitution mutants including the individual and combined mutations at the variant residue 55 and 65 of NS2B(H) were generated and subjected to protease activity analysis. Replacement of NS2B-55 and NS2B-65 of GI to GIII significantly increased the enzymatic activity of GI NS2B(H)-NS3(pro) protease, whereas mutation of NS2B-55 and NS2B-65 of GIII to GI remarkably reduced the enzymatic activity of GIII NS2B(H)-NS3(pro) protease. Overall, these data demonstrated that NS2B-55 and NS2B-65 variations in hydrophilic domain of NS2B co-contributed to the difference in NS2B(H)-NS3(pro) protease activities between GI and GIII. These observations gain an insight into the role of NS2B in regulation of NS3 protease activities, which is useful for understanding the replication of JEV GI and GIII viruses.