Industry 4.0 Technologies for the Manufacturing and Distribution of COVID-19 Vaccines.

BACKGROUND: The evolutionary stages of manufacturing have led us to conceptualize the use of Industry 4.0 for COVID-19 (coronavirus disease 2019), powered by Industry 4.0 technologies. Using applications of integrated process optimizations reliant on digitized data, we propose novel intelligent networks along the vaccine value chain. Vaccine 4.0 may enable maintenance processes, streamline logistics, and enable optimal production of COVID-19 vaccines. VACCINE 4.0 FRAMEWORK: The challenge in applying Vaccine 4.0 includes the requirement of large-scale technologies for digitally transforming manufacturing, producing, rolling-out, and distributing vaccines. With our framework, Vaccine 4.0 analytics will target process performance, process development, process stability, compliance, quality assessment, and optimized maintenance. The benefits of digitization during and post the COVID-19 pandemic include first, the continual assurance of process control, and second, the efficacy of big-data analytics in streamlining set parameter limits. Digitization including big data-analytics may potentially improve the quality of large-scale vaccine production, profitability, and manufacturing processes. The path to Vaccine 4.0 will enhance vaccine quality, improve efficacy, and compliance with data-regulated requirements. DISCUSSION: Fiscal and logistical barriers are prevalent across resource-limited countries worldwide. The Vaccine 4.0 framework accounts for expected barriers of manufacturing and equitably distributing COVID-19 vaccines. With amalgamating big data analytics and biometrics, we enable the identification of vulnerable populations who are at higher risk of disease transmission. Artificial intelligence powered sensors and robotics support thermostable vaccine distribution in limited capacity regions, globally. Biosensors isolate COVID-19 vaccinations with low or limited efficacy. Finally, Vaccine 4.0 blockchain systems address low- and middle-income countries with limited distribution capacities. CONCLUSION: Vaccine 4.0 is a viable framework to optimize manufacturing of vaccines during and post the COVID-19 pandemic.

Outcomes of intravascular ultrasound versus optical coherence tomography guided percutaneous coronary angiography: A meta regression-based analysis.

BACKGROUND: Studies comparing clinical outcomes with intravascular ultrasound (IVUS) versus optical coherence tomography (OCT) guidance for percutaneous coronary intervention (PCI) in patients presenting with coronary artery disease, including stable angina or acute coronary syndrome, are limited. METHODS: We performed a detailed search of electronic databases (PubMed, Embase, and Cochrane) for randomized controlled trials and observational studies that compared cardiovascular outcomes of IVUS versus OCT. Data were aggregated for the primary outcome measure using the random-effects model as pooled risk ratio (RR). The primary outcome of interest was major adverse cardiac events (MACE), cardiac mortality, and all-cause mortality. Secondary outcomes included myocardial infarction (MI), stent thrombosis (ST), target lesion revascularization (TLR), and stroke. RESULTS: A total of seven studies met the inclusion criteria, comprising 5917 patients (OCT n = 2075; IVUS n = 3842). OCT-PCI versus IVUS-guided PCI comparison yielded no statistically significant results for all the outcomes; MACE (RR 0.78; 95% confidence interval [CI], 0.57-1.09; p = 0.14), cardiac mortality (RR 0.97; 95% CI, 0.27-3.46; p = 0.96), all-cause mortality (RR 0.74; 95% CI, 0.39-1.39; p = 0.35), MI (RR 1.27; 95% CI, 0.52-3.07; p = 0.60), ST (RR 0.70; 95% CI, 0.13-3.61; p = 0.67), TLR (RR 1.09; 95% CI, 0.53-2.25; p = 0.81), and stroke (RR 2.32; 95% CI, 0.42-12.90; p = 0.34). Furthermore, there was no effect modification on meta-regression including demographics, comorbidities, lesion location, lesion length, and stent type. CONCLUSIONS: In this meta-analysis, OCT-guided PCI was associated with no difference in clinical outcomes compared with IVUS-guided PCI.

Glycated Haemoglobin (HbA1c) Variations in Nondiabetics With Nutritional Anemia.

OBJECTIVES: Diabetes is prevalent in the Indian population, to the extent that the diabetes burden matches that of nutritional anemia. We aimed to determine the effects of iron and vitamin B12 deficiency anemia on glycated haemoglobin (HbA1c) concentrations in individuals without diabetes. MATERIAL AND METHODS: The study comprises 100 patients with iron deficiency anemia, 100 with vitamin B12 deficiency anemia, and 100 healthy volunteers as a control group. Each of the first two groups was subdivided into two groups depending on the severity of anemia based on Hb levels. We treated with iron replenishment in the iron deficiency group and B12 replenishment in the B12 deficiency group for three months. We noted HbA1c levels before and after the therapy. Data were entered into the SPSS package. For comparing pre and post-therapy levels, we used the Paired 't' test. RESULTS: The mean HbA1c before treatment were 6.1% ± 0.23% and 5.5% ± 0.24%, and the values after treatment were 5.1% ± 0.14% and 4.6% ± 0.2% in severe iron deficiency anemia subgroup and mild to moderate subgroup, respectively. The mean HbA1c in the iron-deficiency anemia control group was 5.2% ± 0.2%. The mean HbA1c levels before treatment were 5.9% ± 0.3% and 5.6% ± 0.19%, and after treatment were 5.0% ± 0.15% and 4.9% ± 0.16% in severe and mild to moderate B12 deficiency anemia, respectively. The mean HbA1c in the vitamin B12 deficiency anemia control group was 5.1% ± 0.2%. CONCLUSION: HbA1c in both types of anemia subjects showed a significant decrease with appropriate therapy. Physicians should consider rechecking patient haemoglobin values and correcting a patient's anemia before determining the patient's glycemic status using HbA1c to avoid misinterpretation of their diabetes status.