Validasi Metode dan Penetapan Kadar Nitrit (NO2) pada Hasil Rebusan Sayuran Hijau (Kangkung, Brokoli, Seledri) Menggunakan Spektrofotometer UV-Vis

Telah dilakukan penelitian tentang penetapan kadar nitrit (NO2) pada beberapa sayuran yaitu kangkung, brokoli, dan seledri. Sayur kangkung diperoleh dari kebun warga daerah Kenten, Palembang, sedangkan sayur brokoli dan seledri diperoleh dari salah satu swalayan di kota Palembang. Kandungan nitrit ditentukan dari hasil air rebusan sampel sayuran dengan metode spektrofotometer UV-Vis. Dari hasil penelitian ini diperoleh nilai kandungan nitrit untuk hasil air rebusan sayuran dengan variasi waktu 5, 15, 20, 25, dan 30 menit. Untuk hasil air rebusan sayur kangkung diperoleh 0,664 mg/kg; 0,665 mg/kg; 0,685 mg/kg; 0,702 mg/kg; 0,710mg/kg. Untuk hasil air rebusan sayur brokoli diperoleh 0,646 mg/kg; 0,647 mg/kg; 0,650 mg/kg; 0,680 mg/kg; 0,704 mg/kg. Untuk hasil air rebusan sayur seledri diperoleh 0,718 mg/kg; 0,730 mg/kg; 0,818 mg/kg; 0,821 mg/kg; 0,849 mg/kg. Dari hasil perebusan pada sayuran kangkung, brokoli, seledri masih aman sesuai dengan ketentuan ADI (Acceptable Daily Intake)/ jumlah asupan harian menurut WHO (World Health Organization)

Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy.

Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in progressive non-traumatic compression of the cervical spinal cord. Spine surgeons must consider a large quantity of information relating to disease presentation, imaging features, and patient characteristics to determine if a patient will benefit from surgery for DCM. We applied a supervised machine learning approach to develop a classification model to predict individual patient outcome after surgery for DCM. Patients undergoing surgery for DCM as a part of the AOSpine CSM-NA or CSM-I prospective, multi-centre studies were included in the analysis. Out of 757 patients 605, 583, and 539 patients had complete follow-up information at 6, 12, and 24 months respectively and were included in the analysis. The primary outcome was improvement in the SF-6D quality of life indicator score by the minimum clinically important difference (MCID). The secondary outcome was improvement in the modified Japanese Orthopedic Association (mJOA) score by the MCID. Predictor variables reflected information about pre-operative disease severity, disease presentation, patient demographics, and comorbidities. A machine learning approach of feature engineering, data pre-processing, and model optimization was used to create the most accurate predictive model of outcome after surgery for DCM. Following data pre-processing 48, 108, and 101 features were chosen for model training at 6, 12, and 24 months respectively. The best performing predictive model used a random forest structure and had an average area under the curve (AUC) of 0.70, classification accuracy of 77%, and sensitivity of 78% when evaluated on a testing cohort that was not used for model training. Worse pre-operative disease severity, longer duration of DCM symptoms, older age, higher body weight, and current smoking status were associated with worse surgical outcomes. We developed a model that predicted positive surgical outcome for DCM with good accuracy at the individual patient level on an independent testing cohort. Our analysis demonstrates the applicability of machine-learning to predictive modeling in spine surgery

Infarct blood-brain-barrier (BBB) permeability normalized to contralateral BBB permeability stratified by time since stroke symptom onset.

<p>Error bars represent standard error of the mean. (*) p<0.05 Tukey's honest significant difference test.</p

Representative diffusion-weighted scan (above) and blood-brain-barrier permeability map (below) for 3 patients.

<p><b>These patients presented <6hrs (A), 6-48hrs (B), and >48hrs (C), following the onset of stroke symptoms.</b> The scale represents KPS in mL/100g/min.</p

General demographic and clinical characteristics of the sample population.

<p>General demographic and clinical characteristics of the sample population.</p

Blood-brain-barrier permeability within the infarct and a homologous region in the contralateral hemisphere stratified by time since stroke symptom onset.

<p>Error bars represent standard error of the mean. (*) p<0.05 two-tailed Students t-test.</p

Permeability-surface area product (KPS) and apparent diffusion coefficient (ADC) values within the infarct and contralateral regions stratified by time since stroke symptom onset.

<p>Permeability-surface area product (KPS) and apparent diffusion coefficient (ADC) values within the infarct and contralateral regions stratified by time since stroke symptom onset.</p

Neuroproteome Changes after Ischemia/Reperfusion Injury and Tissue Plasminogen Activator Administration in Rats: A Quantitative iTRAQ Proteomics Study

<div><p>The thrombolytic, recombinant tissue plasminogen activator (rt-PA) is the only approved therapy for acute ischemic stroke (AIS). When administered after AIS, rt-PA has many adverse pleiotropic actions, which are currently poorly understood. The identification of proteins showing differential expression after rt-PA administration may provide insight into these pleiotropic actions. In this study we used a 2D-LC MS/MS iTRAQ proteomic analysis, western blotting, and pathway analysis to analyze changes in protein expression 24-hours after rt-PA administration in the cortical brain tissue of 36 rats that underwent a sham or transient middle cerebral artery occlusion surgery. After rt-PA administration we reported alterations in the expressions of 18 proteins, many of which were involved in excitatory neurotransmitter function or cytoskeletal structure. The expression changes of GAD2 and EAAT1 were validated with western blot. The interactions between the identified proteins were analyzed with the IPA pathway analysis tool and three proteins: DPYSL2, RTN4, and the NF-kB complex, were found to have characteristics of being key proteins in the network. The differential protein expressions we observed may reflect pleiotropic actions of rt-PA after experimental stroke, and shine light on the mechanisms of rt-PA's adverse effects. This may have important implications in clinical settings where thrombolytic therapy is used to treat AIS.</p></div