Design and implementation of a versatile magnetic field mapper for 3D volumes.

Magnetic field mapping is an essential step in research, manufacturing, and maintenance. It helps one find out what's under the surface of any object and identify areas of high-flux density. Our magnetic field mapper is easy-to-use and has a user-friendly interface. It comprises a triple axis Hall effect-based magnetometer, meaning that it can measure the strength of magnetic fields in 3D. It is made up of three Hall sensors which are mounted on a triple-slot sensor probe which in turn is attached to translation stages allowing motion in three dimensions. The translation in X and Y motion (independent) is from - 50 to + 50  mm while in Z is from - 150 to + 150  mm with a nominal resolution of 0.1  mm. The height of the magnetometer is 1000 mm and it's originally designed for mapping the magnetic field of permanent magnet assemblies for low-field and mobile magnetic resonance scanners. Sometimes, you need to find out how strong the magnetic field in a particular region is or you may want to measure all the three components of the magnetic flux density to find out the homogeneity or isotropy of the field. In high resonant NMR spectroscopy, the magnetic field needs to be highly uniform and homogeneous and to assess such a field, we need a device which can measure the magnetic field, precisely, accurately and reproducibly. The proposed field mapper is useful in all of these situations. It is low cost and easy to manufacture. The maximum measurable magnetic field is ± 2 T. Furthermore, all the material required for building this device is easily accessible.

Clinical and biochemical indicators of disease severity and neurological findings in COVID-19: A study of King Edward Medical University (KEMU), Pakistan.

This study investigated the significance of difference between presence and absence of different neurological findings in COVID-19, in relation with the biochemistry. Various significant correlations in connection with the disease severity and clinical factors were also identified. 351 COVID-19 patients were included. Different laboratory/ clinical findings were investigated. Correlations Kendall's tau and Pearson Chi-Square were applied to find the correlations between severity and clinical findings. The Mann-Whitney Test was applied for a comparison between two types of neurological groups for each biochemistry parameter. Headache was reported in 28% and dizziness in 13% patients. The impaired smell and impaired taste were reported in 28.5% and 36.2% patients, respectively. The muscle pain was present in 39% patients. 80% patients had low lymphocytes & 70% had high neutrophils. 54.5% were found with high ALP. LDH was elevated in 73%. Severity was found significantly correlated with decreased oxygen saturation, age and raised levels of urea, creatinine and LDH. The groups (with/without CNS involvement) were statistically different in ALP, groups (with/without PNS involvement) in WBC, lymphocytes, neutrophils, ALP, urea, creatinine, CK, CKMB and LDH and groups (with/without MSK involvement) in WBC. Oxygen saturation, age, urea, creatinine and LDH are significant indicators of disease severity in COVID-19. The altered levels of different biochemistry can impact the neurological states of COVID-19 patients.

Assessment and prediction of restless leg syndrome (RLS) in patients with diabetes mellitus type II through artificial intelligence (AI).

This study aimed to diagnose the incidence of restless leg syndrome (RLS) in patients with diabetes mellitus (DM) type-2, thorough artificial intelligence based multilayer perceptron (MLP). 300 cases of diabetes mellitus type-2, of age between 18-80 years were included. Point-biserial correlation/Pearson Chi-Square correlations were conducted between RLS and risk factors. We trained a backpropagation MLP via. supervised learning algorithm to predict clinical outcome for RLS. Majority of the patients were having hypertension (63%) and with peripheral neuropathy (69%). Two mostly reported scaled parameters were: 18% 'tiredness' and 14%, 'impact on mood'. A significant correlation was found in RLS with smoking, hypertension and chronic renal failure (CRF). MLP model achieved more than 95% accuracy in predicting the outcome with cross entropy error 0.5%. Following scaled symptomatic variables: 'need/urge to move' (100%) achieved the highest normalized importance, followed by 'relief by moving' (85.7%), 'sleep disturbance' (62%) and 'impact on mood' (51.3%). Artificial intelligence based models can help physicians to identify the pre diagnose RLS, so that active measures can be taken in time to avoid further complications.