Diabetes has become one of the common health issues in people of all age groups. The disease is responsible for many difficulties in lifestyle and is represented by imbalance in hyperglycemia. If kept untreated, diabetes can raise the chance of heart attack, diabetic nephropathy, and other disorders. Early diagnosis of diabetes helps to maintain a healthy lifestyle. Machine learning is a capability of machine to learn from past pattern and occurrences and converge with experience to optimise and give decision. In the current research, the authors have employed machine learning techniques and used multi-criteria decision-making approach in Pima Indian diabetes dataset. To classify the patients, they examined several different supervised and unsupervised predictive models. After detailed analysis, it has been observed that the supervised learning algorithms outweigh the unsupervised algorithms due to the output class being a nominal classified domain.

A teenage girl presented with symptoms of itching and hives on contact with water for 3 years. On specific provocative testing and ruling out underlying systemic conditions, she was diagnosed with aquagenic urticaria. Following failed attempts to control her symptoms with second-generation antihistamines, she was started on monthly subcutaneous injections of the monoclonal antibody, omalizumab. The patient demonstrated significant improvement, with symptoms being well-controlled for 2 years now. She enjoys a good quality of life and can take a shower without developing itching or hives.

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Acute spinal cord injury (SCI) results in catastrophic neurological impairment. We aimed to examine the temporal profile and severity correlation of biomarkers, and their relationship with the American Spinal Injury Association Scale (AIS) improvement in human SCI. 15 SCI and 10 non-SCI healthy subjects were classified according to the initial and discharge AIS grade. Serial cerebrospinal fluid (CSF) and serum samples were collected. Spectrin breakdown products (SBDP) 150, SBDP145, glial fibrillary acidic protein (GFAP), and GFAP breakdown product (GBDP) 38/44K were found to be elevated in the acute phase CSF samples in SCI patients on immunoblotting. SBDP150, ubiquitin C-terminal hydrolase-L1 (UCH-L1), GFAP, S100B, neurofilament light chain protein (NF-L), Tau & interleukin (IL) -6 were elevated in the acute phase CSF and serum samples on ELISA. CSF SBDP150, UCH-L1, GFAP, S100B and Tau were seen to peak on day 1 after injury, while CSF IL-6 and NF-L peaked on day 5. Serum SBDP150, IL-6, S100B, GFAP, UCHL-1 and Tau peaked on day 1, while serum NF-L peaked on day 5 post-injury. CSF alpha II-spectrin, SBDP150/145, and GBDP 44-38K levels (by immunoblots), CSF SBDP150, S100B, GFAP, UCHL-1 and Tau (ELISA) and serum UCHL-1 and Tau (ELISA) at specific time points showed SCI severity-correlation. CSF SBDP150, GFAP, and Tau and serum UCHL-1 and Tau (ELISA) were seen to have the best correlation with the severity at discharge. Receiver Operating Characteristic Curve analysis showed that CSF and serum biomarkers (SBDP150, IL-6, S100B, GFAP, NF-L, UCHL-1 and Tau) were associated with the severity of SCI.