Stroke in sickle cell disease: case report

Sickle cell disease is an inherited blood disorder that affects red blood cells. It is characterized by polymerization of haemoglobin, erythrocyte stiffening, and subsequent vaso-occlusions. These can lead to microcirculation obstructions, tissue ischemia, infarction and acute stroke. Transient ischemic attack, Ischaemic stroke, haemorrhagic stroke, silent cerebral infarction, headache, Moyamoya disease, neuropathic pain, and neurocognitive impairment are neurological complications of sickle cell disease. Here we report a case of ischemic stroke in a patient of sickle cell disease. For early diagnosis and proper management of sickle cell disease neurological complications require specialised haematological and neurological expertise. The newly used medications under ongoing research will be the hope to overcome this devastating disease and its complications

SPEECH CONTROLLED ROBOCAR

The main goal of this paper is to introduce “hearing” sensor and also the speech synthesis to the robotic car such that it is capable to interact with human through Spoken Natural Language (NL). Speech recognition (SR) is a prominent technology, which helps us to introduce “hearing” as well as Natural Language (NL) interface through Speech for the interaction. The most challenging part of the entire system is designing and interfacing various stages together. Our approach was to get the analog voice signal being digitized. The frequency and pitch of words be stored in a memory. These stored words will be used for matching with the words spoken. When the match is found, the system outputs the address of stored words. Hence we have to decode the address and according to the address sensed, the car will perform the required task. Since we wanted the car to be wireless, we used RF module. The address was decoded using microcontroller (DSPIC30F) and then applied to RF module. This together with driver circuit at receivers end made complete intelligent systems

Effect of Village-wide Use of Long-Lasting Insecticidal Nets on Visceral Leishmaniasis Vectors in India and Nepal: A Cluster Randomized Trial

Visceral leishmaniasis (VL) is a vector-borne disease causing at least 60,000 deaths each year amongst an estimated half million cases, and until recently there have been no significant initiatives to reduce this burden. However, in 2005, the governments of India, Bangladesh and Nepal signed a memorandum of understanding at the World Health Assembly in Geneva for the elimination of the disease by 2015. In the absence of an effective vaccine, the program will rely on the active detection and prompt treatment of cases throughout the endemic region, combined with a recurrent indoor residual spraying (IRS) of all villages at risk. Vector control programs based on IRS are notorious for failing to maintain comprehensive spray coverage over time owing to logistical problems and lack of compliance by householders. Long-lasting insecticidal nets (LNs) have been postulated as an alternative or complement to IRS. Here we describe how comprehensive coverage of LN in trial communities reduced the indoor density of sand flies by 25% compared to communities without LNs. This provides an indication that LNs could be usefully deployed as a component of the VL control program in the Indian subcontinent

Multiple Analytical Approaches Reveal Distinct Gene-Environment Interactions in Smokers and Non Smokers in Lung Cancer

Complex disease such as cancer results from interactions of multiple genetic and environmental factors. Studying these factors singularly cannot explain the underlying pathogenetic mechanism of the disease. Multi-analytical approach, including logistic regression (LR), classification and regression tree (CART) and multifactor dimensionality reduction (MDR), was applied in 188 lung cancer cases and 290 controls to explore high order interactions among xenobiotic metabolizing genes and environmental risk factors. Smoking was identified as the predominant risk factor by all three analytical approaches. Individually, CYP1A1*2A polymorphism was significantly associated with increased lung cancer risk (OR = 1.69;95%CI = 1.11–2.59,p = 0.01), whereas EPHX1 Tyr113His and SULT1A1 Arg213His conferred reduced risk (OR = 0.40;95%CI = 0.25–0.65,p<0.001 and OR = 0.51;95%CI = 0.33–0.78,p = 0.002 respectively). In smokers, EPHX1 Tyr113His and SULT1A1 Arg213His polymorphisms reduced the risk of lung cancer, whereas CYP1A1*2A, CYP1A1*2C and GSTP1 Ile105Val imparted increased risk in non-smokers only. While exploring non-linear interactions through CART analysis, smokers carrying the combination of EPHX1 113TC (Tyr/His), SULT1A1 213GG (Arg/Arg) or AA (His/His) and GSTM1 null genotypes showed the highest risk for lung cancer (OR = 3.73;95%CI = 1.33–10.55,p = 0.006), whereas combined effect of CYP1A1*2A 6235CC or TC, SULT1A1 213GG (Arg/Arg) and betel quid chewing showed maximum risk in non-smokers (OR = 2.93;95%CI = 1.15–7.51,p = 0.01). MDR analysis identified two distinct predictor models for the risk of lung cancer in smokers (tobacco chewing, EPHX1 Tyr113His, and SULT1A1 Arg213His) and non-smokers (CYP1A1*2A, GSTP1 Ile105Val and SULT1A1 Arg213His) with testing balance accuracy (TBA) of 0.6436 and 0.6677 respectively. Interaction entropy interpretations of MDR results showed non-additive interactions of tobacco chewing with SULT1A1 Arg213His and EPHX1 Tyr113His in smokers and SULT1A1 Arg213His with GSTP1 Ile105Val and CYP1A1*2C in nonsmokers. These results identified distinct gene-gene and gene environment interactions in smokers and non-smokers, which confirms the importance of multifactorial interaction in risk assessment of lung cancer

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Efficacy of chemo-radiotherapy versus radiotherapy alone in the treatment of esophageal carcinoma

Background: The treatment of esophageal carcinoma may demand multiple approaches including combination of radiotherapy and chemotherapy, particularly cases which are considered unresectable, such as upper third esophageal cancers, locally advanced middle and lower third cancers. Methods: This was a prospective, randomized, open-label, single-center study conducted between December 2014 and July 2016. Patients of either sex aged more than 18 years with the confirmed diagnosis of previously untreated advanced esophageal carcinoma were included in the study. Eligible patients were randomized to receive one of the treatments (chemo-radiotherapy [cisplatin] or radiotherapy alone). Response criteria included dysphasia free survival (DySF), disease free survival (DFS), and overall survival (OS). Tolerability was also assessed. Results: A total of 31 patients (chemo-radiotherapy, n=13; radiotherapy alone, n=18) were enrolled in this study. At one year, the probability of remaining dysphagia free was 40% and 20%, respectively for chemo-radiotherapy and radiotherapy alone groups; and the probability of OS was 64% versus 21%, respectively. The median DFS was 12 months and 5 months for chemo-radiotherapy and radiotherapy alone group, respectively. There were no significant differences in both the groups in EBRT, total treatment duration and duration of EBRT. No patient reported thrombocytopenia or nephrotoxicity. Conclusions: Concurrent chemo-radiotherapy with cisplatin can improve dysphasia and OS in patients with esophageal carcinoma

Generation of optimal velocity trajectory for real-time predictive control of a multi-mode PHEV

The advancement in vehicle-to-vehicle and vehicle- to-infrastructure technologies makes it possible for vehicles to obtain the real-time information related to transportation and traffic infrastructure. This paper presents the development of an optimal velocity generation algorithm that leverages the availability of traffic and road information. The objective of this optimization problem is to generate a velocity trajectory within a prediction horizon to reduce tractive force while monitoring the overall travel time required for the trip. The developed algorithm reduces energy consumption by avoiding wasteful driving maneuvers and utilizes the opportunities to recuperate kinetic energy with regenerative braking capability. This non-linear constrained optimization algorithm is implemented by an automatic control and dynamic optimization (ACADO) toolkit for real-time execution. The energy reduction is observed in the evaluation results obtained with a vehicle model for the 2nd generation of GM Chevrolet Volt, developed at Michigan Technological University. An experimentally validated vehicle dynamic model is used for the assessment of energy consumption and vehicle performance