Causes of variability in prevalence rates of communicable diseases among school students in Pakistan

Objective: The aim of this study is to find out the causes of variability in prevalence rates of communicable diseases among school going students of Multan, in Pakistan. Methodology: This case control trial was conducted in private and Govt. schools present in urban locality, Multan. This study was completed in 7 months (May 2016 to November 2016) and parents of all the students under trial were asked for informed consent. A total number of 100% (n=186) were taken in this study and online source Openepi.com was used for calculating sample size. All the students were selected by lottery method. Computer software SPSS version 23.2 was used for complete data entry and analysis. All the descriptive variables like onset of action and age were presented as SD and mean. Statistical test ANOVA was applied to find the significance among all groups. Chi square test was applied for analysis of continuous stats among groups. P value 0.05 was to be considered as significant. Results: There were 100% (n=186) children, in this study. The mean age of the children was 7.79±1.55 years. (Range: 4 to 10years). There were 41.9% (n=78) children between 4-7 years and 58.1% (n=108) children between 8-10 years. There were 43.5% (n=81) males and 56.5% (n=105) females. There were 58.6% (n=109) children studied in government schools and 41.4% (n=77) studied in private schools. 44.6% (n=83) children belonged to urban areas and 55.4% (n=103) belonged to rural areas. Communicable diseases were noted, in the children, as TB 3.2% (n=6), diarrhea 32.3% (n=60), malaria 7% (n=13), flu 43% (n=80) and scabies 14.5% (n=27). Association was found between communicable disease with gender (p=0.049) and stratified age (p=0.000) except school type (p=0.915) and locality (p=0.221), according to chi-square. Conclusion: The whole study reveals that the prevalence of diarrhea, TB, malaria, flu and scabies were higher in females comparative to male students. The communicable diseases prevalence was also higher in Govt. Schools than private sector. Keywords: Variability, prevalence, correlation, communicable and school going

In vitro susceptibility of typhoidal Salmonellae against newer antimicrobial agents: a search for alternate treatment options

OBJECTIVES: To determine the minimum inhibitory concentrations (MICs) of ceftriaxone, azithromycin, pefloxacin, cefipime and imipenem for Salmonella Typhi (S. Typhi) and Paratyphi. METHODS: One hundred and fifty four isolates of Salmonella Typhi and S. Paratyphi A, B and C growing in blood culture were selected. MICs of ceftriaxone, azithromycin, pefloxacin, cefipime and imipenem were performed by agar dilution method as recommended by clinical laboratory standard institutes. RESULTS: MIC90 of azithromycin and pefloxacin was 8 microg/ml, cefipime was 0.06 microg/ml and imipenem was 0.5 microg/ml. None of the strains were found to be resistant to ceftriaxone but 3 isolates showed higher MIC value of 2 microg/ml. CONCLUSION: Azithromycin appears a suitable alternate for the treatment of typhoid in the community. Imipenem and cefipime are good options in complicated cases to be treated in hospital settings. Pefloxacin cannot be used as MICs are higher. Presence of isolates with higher MIC of ceftriaxone is serious and stresses upon continuous laboratory surveillance to guide clinicians appropriately

Mixed salmonella infection: a case series from Pakistan

Enteric fever remains a major health problem in the developing world, including Pakistan. Poor sanitation and hygienic conditions are the major predisposing factors. Salmonella infection with different strains in the same patient has rarely been reported previously. We are reporting two cases of bacteraemia with simultaneous detection of two strains of Salmonella in a single episode of infection. In both the cases, 2 different serotypes of Salmonella were causing bacteraemia leading to fever. In highly endemic area, one must be aware of mixed Salmonella infections as inappropriate diagnosis of such infections may lead to treatment failure

IDENTIFYING MOLECULAR FUNCTIONS OF DYNEIN MOTOR PROTEINS USING EXTREME GRADIENT BOOSTING ALGORITHM WITH MACHINE LEARNING

The majority of cytoplasmic proteins and vesicles move actively primarily to dynein motor proteins, which are the cause of muscle contraction. Moreover, identifying how dynein are used in cells will rely on structural knowledge. Cytoskeletal motor proteins have different molecular roles and structures, and they belong to three superfamilies of dynamin, actin and myosin. Loss of function of specific molecular motor proteins can be attributed to a number of human diseases, such as Charcot-Charcot-Dystrophy and kidney disease.  It is crucial to create a precise model to identify dynein motor proteins in order to aid scientists in understanding their molecular role and designing therapeutic targets based on their influence on human disease. Therefore, we develop an accurate and efficient computational methodology is highly desired, especially when using cutting-edge machine learning methods. In this article, we proposed a machine learning-based superfamily of cytoskeletal motor protein locations prediction method called extreme gradient boosting (XGBoost). We get the initial feature set All by extraction the protein features from the sequence and evolutionary data of the amino acid residues named BLOUSM62. Through our successful eXtreme gradient boosting (XGBoost), accuracy score 0.8676%, Precision score 0.8768%, Sensitivity score 0.760%, Specificity score 0.9752% and MCC score 0.7536%.  Our method has demonstrated substantial improvements in the performance of many of the evaluation parameters compared to other state-of-the-art methods. This study offers an effective model for the classification of dynein proteins and lays a foundation for further research to improve the efficiency of protein functional classification

UBI-XGB: IDENTIFICATION OF UBIQUITIN PROTEINS USING MACHINE LEARNING MODEL

A recent line of research has focused on Ubiquitination, a pervasive and proteasome-mediated protein degradation that controls apoptosis and is crucial in the breakdown of proteins and the development of cell disorders, is a major factor.  The turnover of proteins and ubiquitination are two related processes. We predict ubiquitination sites; these attributes are lastly fed into the extreme gradient boosting (XGBoost) classifier. We develop reliable predictors computational tool using experimental identification of protein ubiquitination sites is typically labor- and time-intensive. First, we encoded protein sequence features into matrix data using Dipeptide Deviation from Expected Mean (DDE) features encoding techniques. We also proposed 2nd features extraction model named dipeptide composition (DPC) model. It is vital to develop reliable predictors since experimental identification of protein ubiquitination sites is typically labor- and time-intensive. In this paper, we proposed computational method as named Ubipro-XGBoost, a multi-view feature-based technique for predicting ubiquitination sites. Recent developments in proteomic technology have sparked renewed interest in the identification of ubiquitination sites in a number of human disorders, which have been studied experimentally and clinically.  When more experimentally verified ubiquitination sites appear, we developed a predictive algorithm that can locate lysine ubiquitination sites in large-scale proteome data. This paper introduces Ubipro-XGBoost, a machine learning method. Ubipro-XGBoost had an AUC (area under the Receiver Operating Characteristic curve) of 0.914% accuracy, 0.836% Sensitivity, 0.992% Specificity, and 0.839% MCC on a 5-fold cross validation based on DPC model, and 2nd 0.909% accuracy, 0.839% Sensitivity, 0.979% Specificity, and 0. 0.829% MCC on a 5-fold cross validation based on DDE model. The findings demonstrate that the suggested technique, Ubipro-XGBoost, outperforms conventional ubiquitination prediction methods and offers fresh advice for ubiquitination site identification

Color Doppler Misoprostol Response Study (CDMRS): An Evaluation Tool for Patients Awaiting Myomectomy

Uterine myomas (fibroids) are benign tumors of the uterus. Myomectomy, the surgical removal of myoma, is an important treatment option. The major complication associated with myomectomy is excessive bleeding. Many interventions have been used to reduce bleeding during myomectomy. Misoprostol produces uterine contraction, thereby reducing blood supply to the myometrium and in the myoma; it can be used as an alternative to uterine artery occlusion or paracervical tourniquet to reduce blood flow during myomectomy. The Color Doppler Misoprostol Response Study (CDMRS) is a study planned to assess the vascularity of the myoma in patients with fibroid uterus and note the changes after misoprostol administration. Materials and methods: A baseline study of all the patients was done prior to insertion of misoprostol or placebo, and the largest selected fibroid in the patients with uterine fibroids was evaluated for its volume and perfusion by Doppler ultrasound. The resistive index (RI) was measured prior to and after administration of 800 μg misoprostol (4 tablets) per rectal insertion, after 20 minutes, and reevaluated 40 minutes postinsertion. Results: Results from a t-test shows that the use of misoprostol significantly reduces the volume of fibroid from 0–20 minutes by t0–20 [mean difference = 40.3 cm3, confidence interval (CI) 30.6–49.9, p = 0.000) and t20–40 (mean difference = 36.2 cm3, 95% CI 30.7−41.6 cm3, p = 0.000). In the control group receiving four tablets of placebo no significant difference was noted in volume of the fibroid. Likewise, when we compared the RIs at different timings, the results were again in favor of misoprostol because the blood flow of myomas was substantially reduced. The RI increased from t0–20 (mean difference = 0.26, 95% CI 0.16 cm3–0.38 cm3, p = 0.000) and t20-40 (mean difference = 0.08, 95% CI 0.33−0.04 cm3, p = 0.000). In the control group receiving four tablets of placebo, no significant difference was noted in perfusion of the fibroid. Conclusion: In conclusion, we suggest that all patients scheduled for myomectomy have prior CDMRS to evaluate the degree of vascularity and to assess if they have an appropriate response to misoprostol administered rectally, so that there is minimal or no blood loss during surgery. This preoperative assessment will decrease physician apprehension, with less intraoperative blood loss and morbidity

Comparative Analysis of Feature Extraction Methods for Cotton Leaf Diseases Detection

Cotton leaf diseases must be accurately detected and classified to reduce plant diseases and output losses. Feature extraction strategies for automated cotton leaf disease diagnosis are compared in this study. The research uses HOG, SIFT, SURF, GLCM, and Gabor wavelets filter feature extractor to extract features. We gathered and preprocessed 2400 cotton leaf images of healthy and diseases, Angular Leaf Spot, Bacterial Blight, Cotton curl leaf disease (CLCuD), as well as Alternaria Disease. K-means clustering separates leaf areas and improves feature extraction in image segmentation. Discriminative features are extracted using the mentioned methods, and Support Vector Machine (SVM) classifier is employed for disease identification. The comparative analysis based on Accuracy, Precision, and Sensitivity reveals the Gabor Wavelet Filter Feature Extractor as the top performer, achieving 92% accuracy on the test dataset containing bacterial blight, curl virus, alternaria, and healthy leaves. While HOG, SIFT, SURF, and GLCM methods also perform well, they are outperformed by the Gabor Wavelet method. This study shows Gabor wavelet-based features can accurately identify and classify cotton leaf illnesses, helping farmers fight plant diseases. The results underscore the need of choosing proper feature extraction methods for autonomous plant disease diagnostic systems

Fuzzy Logic-Based Identification of Railway Wheelset Conicity Using Multiple Model Approach

The deterioration of railway wheel tread causes unexpected breakdowns with increasing risk of operational failure leading to higher maintenance costs. The timely detection of wheel faults, such as wheel flats and false flanges, leading to varying conicity levels, helps network operators schedule maintenance before a fault occurs in reality. This study proposes a multiple model-based novel technique for the detection of railway wheelset conicity. The proposed idea is based on an indirect method to identify the actual conicity condition by analyzing the lateral acceleration of the wheelset. It in fact incorporates a combination of multiple Kalman filters, tuned on a particular conicity level, and a fuzzy logic identification system. The difference between the actual conicity and its estimated version from the filters is calculated, which provides the foundation for further processing. After preprocessing the residuals, a fuzzy inference system is used that identifies the actual conicity of the wheelset by assessing the normalized rms values from the residuals of each filter. The proposed idea was validated by simulation studies to endorse its efficacy

Analyzing ML-Based IDS over Real-Traffic

The rapid growth of computer networks has caused a significant increase in malicious traffic, promoting the use of Intrusion Detection Systems (IDSs) to protect against this ever-growing attack traffic. A great number of IDS have been developed with some sort of weaknesses and strengths. Most of the development and research of IDS is purely based on simulated and non-updated datasets due to the unavailability of real datasets, for instance, KDD '99, and CIC-IDS-18 which are widely used datasets by researchers are not sufficient to represent real-traffic scenarios. Moreover, these one-time generated static datasets cannot survive the rapid changes in network patterns. To overcome these problems, we have proposed a framework to generate a full feature, unbiased, real-traffic-based, updated custom dataset to deal with the limitations of existing datasets. In this paper, the complete methodology of network testbed, data acquisition and attack scenarios are discussed. The generated dataset contains more than 70 features and covers different types of attacks, namely DoS, DDoS, Portscan, Brute-Force and Web attacks. Later, the custom-generated dataset is compared to various available datasets based on seven different factors, such as updates, practical-to-generate, realness, attack diversity, flexibility, availability, and interoperability. Additionally, we have trained different ML-based classifiers on our custom-generated dataset and then tested/analyzed it based on performance metrics. The generated dataset is publicly available and accessible by all users.  Moreover, the following research is anticipated to allow researchers to develop effective IDSs and real traffic-based updated datasets

High rate of non-susceptibility to metronidazole and clindamycin in anaerobic isolates: data from a clinical laboratory from Karachi, Pakistan

Due to increasing resistance amongst anaerobic pathogens periodic surveillance of resistance has been recommended in regional/local settings. Anaerobic antimicrobial susceptibility testing is not routinely performed in many laboratories in Pakistan, hence absence of local data may lead to inappropriate empirical therapy in serious cases. 121 clinically significant anaerobic strains (26/121; 21% bacteremic isolates) were isolated and saved from 2010 to 2011. Susceptibility testing against metronidazole, clindamycin, co-amoxiclav, meropenem, piperacillin/tazobactam, linezolid and gatifloxacin was performed by determining minimum inhibitory concentrations (MICs). A high proportion of non-susceptible strains to metronidazole (10% of 121 isolates) and clindamycin (12% of 121 isolates) was seen, most noticeable in Bacteroides fragilis. Three Bacteroides species strains were non-susceptible to both metronidazole and clindamycin. One strain of Clostridium species was fully resistant to metronidazole and had intermediate resistance to clindamycin. No resistance to any of the other tested antibiotics was seen. Resistance to metronidazole was higher in bacteremic vs. non bacteremic isolates (p = value 0.07). In our setting where there is a high usage of empirical metronidazole and clindamycin for the treatment of serious anaerobic infections clinicians should be aware of increased resistance to these agents. Periodic surveillance of resistance to anti-anaerobic drugs especially metronidazole and clindamycin should be performed to generate antibiogram and guide appropriate empiric therapy