Metabolic alterations play a crucial role in glioma development and progression and can be detected even before the appearance of the fatal phenotype. We have compared the circulating metabolic fingerprints of glioma patients versus healthy controls, for the first time, in a quest to identify a panel of small, dysregulated metabolites with potential to serve as a predictive and/or diagnostic marker in the clinical settings. High-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS-NMR) was used for untargeted metabolomics and data acquisition followed by a machine learning (ML) approach for the analyses of large metabolic datasets. Cross-validation of ML predicted NMR spectral features was done by statistical methods (Wilcoxon-test) using JMP-pro16 software. Alanine was identified as the most critical metabolite with potential to detect glioma with precision of 1.0, recall of 0.96, and F1 measure of 0.98. The top 10 metabolites identified for glioma detection included alanine, glutamine, valine, methionine, N-acetylaspartate (NAA), γ-aminobutyric acid (GABA), serine, α-glucose, lactate, and arginine. We achieved 100% accuracy for the detection of glioma using ML algorithms, extra tree classifier, and random forest, and 98% accuracy with logistic regression. Classification of glioma in low and high grades was done with 86% accuracy using logistic regression model, and with 83% and 79% accuracy using extra tree classifier and random forest, respectively. The predictive accuracy of our ML model is superior to any of the previously reported algorithms, used in tissue- or liquid biopsy-based metabolic studies. The identified top metabolites can be targeted to develop early diagnostic methods as well as to plan personalized treatment strategies.

Severe acute respiratory syndrome called COVID-19, was declared as global health emergency and a pandemic due to its worldwide distribution and frightful spread. Patients are presented with severe respiratory illness along with thrombotic disorders. Elevated d-dimer level (>2000ng/ml) is a potentialpredictive biomarker of the disease outcome and prognosis. The objective of the present study isto find the association ofhigh d-dimer levels and mortality rate in COVID-19 patients to establish the optimal cutoff value for use in clinical setting. Methods: Present study enrolled 318COVID-19 patients admitted to Mayo Hospital, Lahore, Pakistan and confirmed by RT-PCR. On admission d-dimer levelof enrolled patients was measured by fluorescence immuno assay and reported in ng/ml. The enrolled subjects were divided in groupsbased on their age, gender, on admission d-dimer levels (<2000ng/ml and >2000ng/ml), outcome (survivors, non-survivors) and variant (α, β, and γ). Wilcoxon test was used to check the d-dimer level difference in survivor and non-survivor group. Results:81%patients (257/316) died and were categorized as non-survivors while 19% (61/318) were discharged after recovery and were categorized as survivors. Mean d-dimer levelfor survivor group was 2070ng/ml (±3060ng/ml) whereas for non-survivor group was 8010ng/ml (±5404ng/ml) and mean difference was statistically significant (p<0.05).D-dimer level washighest (upto 20,000ng/ml) in second wave(β-variant) as compared to other two wavesand caused highest number of deaths (n=163). Conclusion: Present study reports the d-dimer levels (>2000ng/ml)are strongly associated withhigh mortality rate in COVID-19 patients.

Spinal Cord injury (SCI) is a serious public health problem as it not only causes serious functional impairment in the individual but also affects the family and social circle of the patient. The main objective of the study was to investigate the level of functional independence in different levels of SCI patients in Pakistani population. We hypothesized that different levels of SCI experience different levels of functional independence. Methods: An exploratory cross-sectional survey was designed, and data was collected from Lahore General Hospital, Ghurki Hospital, and Jinnah Hospital, Lahore, Pakistan. 52 patients suffering from acute spinal cord injury were enrolled in study by using convenient sampling technique. Overall health status of patients was measured using functional independence measure (FIM) tool.  Results: Total 52 patients were assessed in this study. Out of which 50% injuries were reported at cervical level, 15% injuries were reported at thoracic level and 34% were reported at lumbar level.  Percentage of males suffering from SCI (62%) was higher than female (38%). The lowest functional independence level was recorded for cervical injury (FIM score: 40), moderate for thoracic injury (FIM score: 84) and maximum for lumbar injury (FIM score: 102). Conclusion: Within the studied population, the percentage of cervical injuries was more than thoracic and lumbar. Gender proportion in traumatic spinal cord injury showed that men were more prone to injury as compared to female. However, functional independence was associated with level of SCI injury as cervical injuries patients were least independent while lumbar injury patients had high functional independence.

Metabolic alterations, crucial indicators of glioma development, can be used for detection of glioma before the appearance of fatal phenotype. We have compared the circulating metabolic fingerprints of glioma (n=26) and healthy controls (n=16) to identify a panel of biomarkers for detection of glioma. HRMAS-NMR spectra was obtained from two study groups and data was analysed by ML as well as chemometric methods (PCA and PLSDA). A panel of 38 metabolites was identified by three ML algorithms (logistics regression, extra tree classifier, &amp; random forest), Wilcoxon test (p&lt;0.05), and PLSDA (VIP score&gt;1) which can serve as diagnostic biomarker of glioma.