Understanding COVID-19 outcome: Exploring the prognostic value of soluble biomarkers indicative of endothelial impairment.

Host proteins released by the activated endothelial cells during SARS-CoV-2 infection are implicated to be involved in coagulation and endothelial dysfunction. However, the underlying mechanism that governs the vascular dysfunction and disease severity in COVID-19 remains obscure. The study evaluated the serum levels of Bradykinin, Kallikrein, SERPIN A, and IL-18 in COVID-19 (N-42 with 20 moderate and 22 severe) patients compared to healthy controls (HC: N-10) using ELISA at the day of admission (DOA) and day 7 post-admission. The efficacy of the protein levels in predicting disease severity was further determined using machine learning models. The levels of bradykinins and SERPIN A were higher (P ≤ 0.001) in both severe and moderate cases on day 7 post-admission compared to DOA. All the soluble proteins studied were found to elevated (P ≤ 0.01) in severe compared to moderate in day 7 and were positively correlated (P ≤ 0.001) with D-dimer, a marker for coagulation. ROC analysis identified that SERPIN A, IL-18, and bradykinin could predict the clinical condition of COVID-19 with AUC values of 1, 0.979, and 1, respectively. Among the models trained using univariate model analysis, SERPIN A emerged as a strong prognostic biomarker for COVID-19 disease severity. The serum levels of SERPIN A in conjunction with the coagulation marker D-dimer, serve as a predictive indicator for COVID-19 clinical outcomes. However, studies are required to ascertain the role of these markers in disease virulence.

Urinary Metabolomics Identified Metabolic Perturbations Associated with Gutka, a Smokeless Form of Tobacco.

Gutka, a form of smokeless tobacco, is widely used in the Indian subcontinent and in other regions of South Asia. Smokeless tobacco exposure is most likely to increase the incidence of oral cancer in the Indian population, and metabolic changes are a hallmark of cancer. The development of biomarkers for early detection and better prevention measures for smokeless tobacco users at risk of oral cancer can be aided by studying urinary metabolomics and offering insight into altered metabolic profiles. This study aimed to investigate urine metabolic alterations among smokeless tobacco users using targeted LC-ESI-MS/MS metabolomics approaches to better understand the effects of smokeless tobacco on human metabolism. Smokeless tobacco users' specific urinary metabolomics signatures were extracted using univariate, multivariate analysis and machine learning methods. Statistical analysis identified 30 urine metabolites significantly associated with metabolomic alterations in humans who chew smokeless tobacco. Receiver operator characteristic (ROC) curve analysis evidenced the 5 most discriminatory metabolites from each approach that could differentiate between smokeless tobacco users and controls with higher sensitivity and specificity. An analysis of multiple-metabolite machine learning models and single-metabolite ROC curves revealed discriminatory metabolites capable of distinguishing smokeless tobacco users from nonusers more effectively with higher sensitivity and specificity. Furthermore, metabolic pathway analysis depicted several dysregulated pathways in smokeless tobacco users, including arginine biosynthesis, beta-alanine metabolism, TCA cycle, etc. This study devised a novel strategy to identify exposure biomarkers among smokeless tobacco users by combining metabolomics and machine learning algorithms.

Integrated metabolomics and machine learning approach to predict hypertensive disorders of pregnancy.

BACKGROUND: Hypertensive disorders of pregnancy account for 3% to 10% of maternal-fetal morbidity and mortality worldwide. This condition has been considered one of the leading causes of maternal deaths in developing countries, such as India. OBJECTIVE: This study aimed to discover hypertensive disorders of pregnancy-specific candidate urine metabolites as markers for hypertensive disorders of pregnancy by applying integrated metabolomics and machine learning approaches. STUDY DESIGN: The targeted urinary metabolomics study was conducted in 70 healthy pregnant controls and 133 pregnant patients having hypertension as cases. Hypertensive disorders of pregnancy-specific metabolites for disease prediction were further extracted using univariate and multivariate statistical analyses. For machine learning analysis, 80% of the data were used for training (79 for hypertensive disorders of pregnancy and 42 for healthy pregnancy) and validation (27 for hypertensive disorders of pregnancy and 14 for healthy pregnancy), and 20% of the data were used for test sets (27 for hypertensive disorders of pregnancy and 14 for healthy pregnancy). RESULTS: The statistical analysis using an unpaired t test revealed 44 differential metabolites. Pathway analysis showed mainly that purine and thiamine metabolism were altered in the group with hypertensive disorders of pregnancy compared with the healthy pregnancy group. The area under the receiver operating characteristic curves of the 5 most predominant metabolites were 0.98 (adenosine), 0.92 (adenosine monophosphate), 0.89 (deoxyadenosine), 0.81 (thiamine), and 0.81 (thiamine monophosphate). The best prediction accuracies were obtained using 2 machine learning models (95% for the gradient boost model and 98% for the decision tree) among the 5 used models. The machine learning models showed higher predictive performance for 3 metabolites (ie, thiamine monophosphate, adenosine monophosphate, and thiamine) among 5 metabolites. The combined accuracies of adenosine from all models were 98.6 in the training set and 95.6 in the test set. Moreover, the predictive performance of adenosine was higher than other metabolites. The relative feature importance of adenosine was also observed in the decision tree and the gradient boost model. CONCLUSION: Among other metabolites, adenosine and thiamine metabolites were found to differentiate participants with hypertensive disorders of pregnancy from participants with healthy pregnancies; hence, these metabolites can serve as a promising noninvasive marker for the detection of hypertensive disorders of pregnancy.

Cost-effectiveness of open versus arthroscopic rotator cuff repair.

BACKGROUND: Economic evaluation of surgical procedures is necessary in view of more expensive newer techniques emerging in an increasingly cost-conscious health care environment. This study compares the cost-effectiveness of open rotator cuff repair with arthroscopic repair for moderately size tears. MATERIALS AND METHODS: This was a prospective study of 30 consecutive patients, of whom 15 had an arthroscopic repair and 15 had an open procedure. Clinical effectiveness was assessed using Oxford and Constant shoulder scores. Costs were estimated from departmental and hospital financial data. RESULTS: At last follow-up, no difference Oxford and Constant shoulder scores was noted between the 2 methods of repair. There was no significant difference between the groups in the cost of time in the operating theater, inpatient time, amount of postoperative analgesia, number of postoperative outpatient visits, physiotherapy costs, and time off work. The incremental cost of each arthroscopic rotator cuff repair was pound675 ($1248.75) more than the open procedure. This was mainly in the area of direct health care costs, instrumentation in particular. DISCUSSION: Health care policy makers are increasingly demanding evidence of cost-effectiveness of a procedure. This study showed both methods of repair provide equivalent clinical results. CONCLUSION: Open cuff repair is more cost-effective than arthroscopic repair and is likely to have lower cost-utility ratio. In addition, the tariff for the arthroscopic procedure in some health care systems is same as open repair.

Compression forces generated by Mini bone screws--a comparative study done on bone model.

The compressive forces generated by the AO/ASIF 3.0 mm cannulated cancellous and 2.0 mm cortical screws, Mini-Acutrak and Herbert/Whipple small bone cannulated screws were measured in the laboratory with the use of simulated cancellous bone and a load cell washer as a means of quantifying their fixation capabilities. The Herbert/Whipple screw and the Mini-Acutrak screw were found to have nearly identical compression capabilities and provided more compression than the cortical screw. The AO/ASIF cannulated screw when used with a support screw demonstrated a compressive capacity twice that of the 2.0 mm cortical screw and higher than the headless Mini-Acutrak and Herbert/Whipple screws. The Mini-Acutrak screw produced about 70% of compression of the cancellous screw in spite of having a diameter almost half that of the cancellous screw. The Herbert/Whipple screw in spite of its larger size compared to the Mini-Acutrak produced almost the same amount of compression.

Upper-extremity deep-vein thrombosis following a clavicular fracture.

Upper-extremity deep-vein thrombosis (DVT) is not frequently reported in orthopaedic literature. We report a case of upper-limb DVT following an acute clavicular fracture that was treated conservatively with a figure-of-eight sling. Diagnosis of this condition requires a high index of suspicion, and treating it would prevent a potentially fatal pulmonary embolism. DVT following acute clavicle fracture was not reported in the past.