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Urinary bladder cancer is a major epidemiological problem that continues to grow each year. It opens avenues for investigative research for the identification of new disease markers and diagnostic techniques. In this pilot study, utility of non-invasive (1)H NMR spectroscopy has been evaluated for probing the metabolic perturbations occurring in non-muscle invasive urinary bladder cancer. (1)H NMR spectra of urine of bladder cancer patients and controls (healthy and urinary tract infection/bladder stone) (n = 103) were acquired at 400MHz. The non-overlapping resonances of citrate, dimethylamine, phenylalanine, taurine and hippurate were first identified and then quantitated by (1)H NMR spectra, with respect to an external reference sodium-3-trimethylsilylpropionate (TSP). The concentrations of these metabolites were then statistically analyzed. The cancer patients showed significant (p < 0.05) variations in concentration of hippurate and citrate as compared with healthy controls and benign controls. The significant elevation in concentration of taurine was observed in urine of bladder cancer patients, which was below the sensitivity limit of 400MHz in control cases. However, stages Ta, T1 and carcinoma in situ (CIS) cannot be differentiated on the basis of altered metabolite indices but their composition may reflect the biochemical alterations in metabolism of cancer cells.

Oral squamous cell carcinoma (SCC) represents more than 90% of all head and neck cancers as reported by Hermans (Cancer Imaging, 5(Spec No A), S52-S57, 2005), which draws attention of investigative research for novel predictive metabolic biomarkers to understand the malignancy induced biochemical perturbations occurring at molecular level. In the present work, proton HR-MAS NMR spectroscopic studies have been performed on resected human oral SCC tumor tissues, its neighboring margins and bed tissues (n = 159), obtained from 36 patients (n = 27 training set; n = 9 unknown test set), for the identification of metabolic fingerprints. The proton NMR spectra were then subjected to chemometric unsupervised PCA and supervised OSC-filtered PCA and PLS-DA multivariate analysis. Application of PLS-DA on orthogonally signal corrected training data-set (n = 120 tissue specimens; 27 patients) allowed [95% correct classification of malignant tissues from benign samples with [98% specificity and sensitivity. The OSC-PLS-DA model thus constructed was used to predict the class membership of unknown tissue specimens (n = 39) obtained from nine patients. These tissue samples were correctly predicted in its respective histological categories with 97.4% diagnostic accuracy. The regression coefficients obtained from OSC-filtered PLS-DA model indicated that malignant tissues had higher levels of glutamate, choline, phosphocholine, lactate, acetate, taurine, glycine, leucine, lysine, isoleucine and alanine, and lower levels of creatine and PUFA, representing altered metabolic processes (lipidogenesis, protein synthesis, and volume regulation) during tumor progression. Thus proton HR-MAS MR spectroscopy could efficiently identify the metabolic perturbations of malignant tumor from non-malignant bed and margins tissue specimens, which may be helpful in understanding the extent of tumor penetration in neighboring tissues. Keywords Squamous cell carcinoma Á HR-MAS NMR Á Orthogonal signal correction Á Principal component analysis Á Partial least square discriminant analysis Abbreviations HR-MAS High resolution magic angle spinning PCA Principal component analysis OSC Orthogonal signal correction PLS Partial least square NOESY Nuclear overhauser enhancement spectroscopy COSY Correlation spectroscopy CPMG Carr Purcell Meiboom Gill sequence HSQC Heteronuclear single quantum coherence SCC Squamous cell carcinoma Electronic supplementary material The online version of this article (

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A B S T R A C TTo assess the medico social demographics of acute myocardial infarction (AMI) in our community we studied 609 patients presenting between January 2008 to December 2008 with a detailed questionnaire in four centres of UP. Medical attention was sought late (> 6 hours) in 316 (51.6%), thrombolysis was obtained in 45.2% (275) and presentation was atypical in 16.3% (99). 36.2% (221) had pre-monitory symptoms of which 68% (150) ignored the same while of 32% (71) who did seek medical attention 47.9% (37) were brushed away as non-cardiac in origin. 20.3% (46/226) of hypertension, 23.2% (43/185) of diabetes and 83.4% (91/109) of hyperlipidaemia was diagnosed post event. We conclude that at least half of patients with AMI do not get definitive therapy, at least one in 10 patients do not have the classical symptoms, reasonable proportion are unaware of their risk factors, and a good majority have pre-monitory symptoms which get overlooked.

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Purpose: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. Methods: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. Summary: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.