Pramod Kumar

Singh S, et al. Infect Chemother. 2025 Apr;57:e12. https://doi.org/10.3947/ic.2024.0139

Mahajan M, et al. Infect Chemother. 2023 Jun;55:e27. https://doi.org/10.3947/ic.2023.0014

Background: Coronavirus disease 2019 has emerged as a global pandemic causing millions of critical cases and deaths. Early identification of at-risk patients is crucial for planning triage and treatment strategies.Methods and findings: We performed this systematic review and meta-analysis to determine the pooled prognostic significance of procalcitonin in predicting mortality and severity in patients with COVID-19 using a robust methodology and clear clinical implications.Design: We used Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Cochrane Handbook for Systematic Reviews of Interventions guidelines. We included thirty-two prospective and retrospective cohort studies involving 13,154 patients.Results: The diagnostic odds ratio of procalcitonin for predicting mortality were estimated to be 11 (95% CI: 7 to 17) with sensitivity, specificity, and summary area under the curveof 0.83 (95% CI: 0.70 to 0.91), 0.69 (95% CI: 0.58 to 0.79), and 0.83 (95% CI: 0.79 to 0.86) respectively. While for identifying severe cases of COVID-19, the odds ratio was 8.0 (95% CI 5.0 to 12.0) with sensitivity, specificity, and summary area under the curve of 0.73 (95% CI 0.67 to 0.78), 0.74 (0.66 to 0.81), and 0.78 (95% CI 0.74 to 0.82) respectively.Conclusion: Procalcitonin has good discriminatory power for predicting mortality and disease severity in COVID-19 patients. Therefore, procalcitonin measurement may help identify potentially severe cases and thus decrease mortality by offering early aggressive treatment.

Compute operators/kernels (like Convolution, Relu, Gemm etc.) form the backbone of Deep Neural Network (DNN) models. With the advent of AI accelerators, there is a need to have specialized and optimized kernels for these operators to take full advantage of the features provided by the hardware. Kernels are mostly developed at High level languages like C, C++. They should work with almost the same accuracy and functionality as they would work on CPU/GPU machine. There are various ways listed below to check kernel accuracy and functionality. One of the common ways is to use Python interface provided by the different AI/ML frameworks. However, there are certain disadvantages like script level exposure which requires full stack integration and lack of multithreading because of python global interpreter lock. Another way is to develop a reference generator engine based on understanding of operators from frameworks. This strategy requires validating the correctness of the reference generator engine itself. Also, the generated reference could be at times in incompatible format and gets difficult to consume in the kernel validation code. In this paper we propose PyTeNet++ which stands for AI framework Pytorch[1], TensorFlow[2], Mxnet[3] using C++ APIs. The proposed solution is one of its kind and no such reference generator framework exists at the high-level language utilizing listed frameworks as backends. PyTeNet++ sits on top of C++ APIs exposed by these frameworks and helps to generate reference which could be seamlessly integrated with the kernel development/validation stack of the operators. It also helps to achieve maturity while kernel development. It exploits various benefits C++ provides like multithreading, low latency, high performance and OpenMP[4]. All these features are useful for faster kernel maturity. The smarter way of doing validation close to development environment helps in the early enablement (without full stack integration) of the DNN models, leading to quick maturity of the product and improved time-to-market.