Enteric Fever as an Antecedent to Development of Miller-Fisher Syndrome and Possible Role of COVID-19 Vaccination

Summary: Guillain-Barre Syndrome is an immune-mediated demyelinating disorder. Miller-Fisher Syndrome is an uncommon subtype of GBS. It is characterized by findings of ophthalmoplegia, ataxia, and areflexia. Here we present the case of Miller-Fisher Syndrome following an episode of typhoidal diarrhea. The presentation was of rapidly progressing weakness beginning in the lower extremity with diplopia. Examination revealed diminished reflexes. CSF testing revealed albuminocytologic dissociation which was later supported by neurophysiological testing. The patient was treated with intravenous immunoglobulins (IVIG). We conclude that Miller-Fisher syndrome should be considered in the diagnostic workup of patients presenting with new sensorimotor deficits following diarrheal illnesses and/or COVID-19 mRNA vaccination. Early recognition is essential given the propensity of GBS to cause life-threatening respiratory failure and prompt IVIG administration is associated with a better prognosis. Keywords: Enteric Fever, Miller-Fisher Syndrome, COVID-19, Vaccinatio

Relationship between Social Media Marketing and Consumer Buying Behavior

The social media has become an integral part of our lives with the introduction of 3G, 4G technology in Pakistan it has become possible for people to stay connected from anywhere any time. The purpose of this study is to find out that if any relationship between social media marketing and consumer buying behavior exist if their existence affected each other in any significant way. For this purpose an online survey was conducted and 100 people responded who were active users of social media in the region of Peshawar an unstructured/ structured questionnaire was designed to collect information from the respondents. The research findings and results confirms that there is a positive relationship between social media marketing and consumer buying behavior as well as that social media can be used as an effective marketing tool in region of Peshawar

Energy efficient parallel configuration based six degree of freedom machining bed

The process of material removal from a workpiece to obtain the desired shape is termed machining. Present-day material removal technologies have high spindle speeds and thus allow quick material removal. These high-speed spindles are highly exposed to vibrations and, as a result, the accuracy of the final workpiece’s dimensions is compromised. To overcome this problem, the motion of the tool is restricted, and multiple degrees of freedom are given through the motion of the workpiece in different axes. A machining bed configured as a parallel manipulator capable of giving six degrees of freedom (DOF) to the workpiece is proposed in this regard. However, the proposed six DOF machining bed should be energy efficient to avoid an increase in machining cost. The benefit of using the proposed configuration is a reduction in dimensional error and computational time which, as a result, reduces the energy utilization, vibrations, and machining time in practice. This paper presents kinematics, dynamics and energy efficiency models, and the development of the proposed configuration of the machining bed. The energy efficiency model is derived from the dynamics model. The models are verified in simulation and experimentally. To minimize error and computation time, a PID controller is also designed and tested in simulation as well as experimentally. The resulting energy efficiency is also analyzed. The results verify the efficacy of the proposed configuration of the machining bed, minimizing position error to 2% and reducing computation time by 27%, hence reducing the energy consumption and enhancing the energy efficiency by 60%

Enhancing cricket performance analysis with human pose estimation and machine learning

Producción CientíficaCricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes eight strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep. The study uses the MediaPipe library to extract features from videos and several machine learning and deep learning algorithms, including random forest (RF), support vector machine, k-nearest neighbors, decision tree, linear regression, and long short-term memory to predict the strokes. The study achieves an outstanding accuracy of 99.77% using the RF algorithm, outperforming the other algorithms used in the study. The k-fold validation of the RF model is 95.0% with a standard deviation of 0.07, highlighting the potential of computer vision and machine learning techniques for predicting batsman strokes in cricket. The study’s results could help improve coaching techniques and enhance batsmen’s performance in cricket, ultimately improving the game’s overall quality

Risk Factors and Secondary Infections in Dengue Hemorrhagic Fever Patients

Background: Dengue hemorrhagic fever (DHF) is a fatal manifestation of dengue disease. DHF’s risk factors profile holds significance importance in the clinical practice and efficient care plan are required during dengue disease flare-up. The aim of this study was to investigate the risk factors for pathogenesis of dengue disease and dengue hemorrhagic fever. Methods: In this descriptive cross-sectional study, data was obtained from 256 patients with diagnoses of Dengue hemorrhagic fever (DHF). Comprehensive history, physical assessment and biochemical estimations were recorded. Patients were followed to identify and assess the risk factors for DHF. The Statistical Package of Social Sciences for analysis of data. Stratification of residence and socioeconomic status to see effect of these on result variable by applying chi square test. p value of <0.05 was taken as significant. Results: Among the 256 patients, the mean age of the age (Mean±SD) of study population was 28.4±12.1 years, 162 (63.28%) were less than 40 years of age and 94 (36.72%) were ≥40 years. The males were 181 (70.70%) and females were 75 (29.30%). The frequency of risk factors was observed to be 26 (10.8%) patients had Diabetes Mellitus, 55(21.5%) hypertension, 25(9.8%) hyperlipidemia. Secondary infection occurred in 192 (75%) but results were insignificant (p>0.05). All diseases were common in participants who belonged to the urban area. Conclusion: Secondary infection was most common risk factor in patients with DHF and found mostly in less than 40 age than older patients. Whereas, males were predominately affected more than the females (p<0.05). Keywords: Severe Dengue, Dengue Hemorrhagic Fever, Risk Factor