Bioprocess development today is slow and expensive compared to chemical process development. A drastic paradigm shift is necessary and possible by the consistent application of engineering strategies that are typically used in the process development phase already in the early product development. Aside from providing a consistent pathway, strategies such as statistical‐based design of experiments, fed‐batch, minibioreactors, new on‐line sensors, process modeling, and control tools in combination with automation of manual steps offer a higher success rate and the opportunity to find the optimum parameters and operation point. This also directly benefits the early phases of biomolecular screening and initial production of small amounts of the target molecule. The paper reviews the bioprocess developmental phases from a business perspective and the available systems and technologies.

Background: Massive transfusions are associated with a high mortality rate, but there is little evidence indicating when such efforts are futile. The purpose of this study was to identify clinical variables that could be used as futility indicators in massively transfused patients. Methods: We retrospectively analyzed 138 adult surgical patients at our institution receiving a massive transfusion (2016)(2017)(2018)(2019). Peak lactate and nadir pH within 24 h of massive transfusion initiation, along with other clinical variables, were assessed as predictors of the primary outcome, in-hospital mortality. Results:The overall rate of in-hospital mortality among our patient population was 52.9% (n = 73). Increasing lactate and decreasing pH were associated with greater mortality among massively transfused patients. Mortality rates were~2-fold higher for patients in the highest lactate category (≥10.0 mmol/L: 25 of 37; 67.6%) compared to the lowest category (0.0-4.9 mmol/L: 17 of 48; 35.4%) (p = .005), and~2.5-fold higher for patients in the lowest pH category (<7.00: 8 of 9; 88.9%) compared to the highest category (≥7.40: 8 of 23; 34.7%) (p = .016). Increasing age was also associated with higher mortality (≥65 years: 24 of 33; 72.7%) when compared to younger patients (18-64 years: 49 of 105; 46.7%) (p = .010).Conclusions: Peak lactate ≥10.0 mmol/L, nadir pH <7.00, and age ≥65 years were significantly associated with higher rates of in-hospital mortality among massively transfused patients. Incorporating these clinical parameters into a futility index for massive transfusions will be useful in situations where blood products are scarce and/or mortality may be unavoidable.

BACKGROUND: Providing bloodless medical care for patients who wish to avoid allogeneic transfusion can be challenging; however, previous studies have demonstrated favorable outcomes when appropriate methods are used. Here, we report one of the largest series of patients receiving bloodless care, along with the methods used to provide such care, and the resulting outcomes. METHODS: In a retrospective cohort study, 1111 adult inpatients (age ≥18 years) at a single institution who declined allogeneic transfusion for religious or personal reasons between June 2012 and June 2016 were included, and the patient blood management methods are described. Patient characteristics, laboratory data, and transfusion rates, as well as clinical outcomes (morbidity, mortality, and length of stay) were compared to all other patients in the hospital who received standard care, including transfusions if needed (n = 137,009). Medical and surgical patients were analyzed as subgroups. The primary outcome was composite morbidity (any morbid event: infectious, thrombotic, ischemic, renal, or respiratory). Secondary outcomes included individual morbid events, in-hospital mortality, length of stay, total hospital charges, and costs. RESULTS: The bloodless cohort had more females and a lower case mix index, but more preadmission comorbidities. Mean nadir hemoglobin during hospitalization was lower in the bloodless (9.7 ± 2.6 g/dL) compared to the standard care (10.1 ± 2.4 g/dL) group (P < .0001). Composite morbidity occurred in 14.4% vs 16.0% (P = .16) of the bloodless and standard care patients, respectively. Length of stay and in-hospital mortality were similar between the bloodless and standard care patients. After Bonferroni adjustment for multiple comparisons, hospital-acquired infection occurred less frequently in the bloodless compared to the standard care cohort (4.3% vs 8.3%) (P < .0001) in the medical patient subgroup, but not in the surgical subgroup. After propensity score adjustment in a multivariable model and adjustment for multiple comparisons, bloodless care was associated with less risk of hospital-acquired infection (OR, 0.56; 95% CI, 0.35–0.83; P = .0074) in the medical subgroup, but not in the surgical subgroup. Median total hospital charges (by 8.5%; P = .0017) and costs (by 8.7%; P = .0001) were lower in the bloodless compared to the standard care cohort, when all patients were included. CONCLUSIONS: Overall, adult patients receiving bloodless care had similar clinical outcomes compared to patients receiving standard care. Medical (but not surgical) bloodless patients may be at less risk for hospital-acquired infection compared to those receiving standard care. Bloodless care is cost-effective and should be considered as high-value practice.

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The heliostat field of Solar Central Receiver Systems takes up to 50% of the initial investment and can cause up to 40% of energetic loss in operation. Hence, it must be carefully optimized. Design procedures usually rely on particular heliostat distribution models. In this work, optimization of the promising biomimetic distribution model is studied. Two stochastic population-based optimizers are applied to maximize the optical efficiency of fields: a genetic algorithm, micraGA, and a memetic one, UEGO. As far as the authors know, they have not been previously applied to this problem. However, they could be a good option according to their structure. Additionally, a Brute-Force Grid is used to estimate the global optimum and a Pure-Random Search is applied as a baseline reference. Our empirical results show that many different configurations of the distribution model lead to very similar solutions. Although micraGA exhibits poor performance, UEGO achieves the best results in a reduced time and seems appropriate for the problem at hand.

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