Prognostic value of European LeukemiaNet 2022 criteria and genomic clusters using machine learning in older adults with acute myeloid leukemia.

This study aimed to validate the new European Leukemia Net (ELN) 2022 criteria for genetic risk stratification in older adults with acute myeloid leukemia (AML) and to determine the most likely set of clusters of similar cytogenetic and mutation properties correlated with survival outcomes in three treatment groups: intensive chemotherapy (IC), hypomethylating agents (HMA) alone, and HMA plus venetoclax (HMA/VEN). The study included 279 patients (aged ≥60 years) who received IC (N=131), HMA (N=76), and HMA/VEN (N=72) between July 2017 and October 2021. No significant differences were observed in survival among the groups according to ELN 2022 risk stratification. Unsupervised hierarchical clustering analysis identified nine genomic clusters (C1-9) with varying survival outcomes depending on treatment type. For example, C4 (predominant for core binding factor-AML) displayed a favorable prognosis in the IC group, but not in the HMA or HMA/VEN groups. The HMA/VEN group had better outcomes than the HMA group in many clusters (C1, 2, 3, and 5); however, the addition of VEN to HMA or IC did not improve the survival outcomes compared with those of HMA alone in C7 and C9 (predominant for -5, del(5q), -7, -17/abn(17p), complex karyotypes, and mutated TP53). The study highlights the limitations of ELN genetic risk stratification in older adults with AML. It emphasizes the need for a more comprehensive approach that considers co-occurring somatic mutations to guide treatment selection in older adults with AML.

Cell-Free Protein Synthesis of Metalloproteins.

Metalloproteins, proteins containing metal atoms or clusters within their structures, are critical for various biological functions across all domains of life. More than hundreds of different types have been discovered, which conduct various roles such as transportation of O2, catalyzing chemical reactions, sensing environmental changes, and relaying electrons. Metalloprotein molecules incorporate a variety of metal atoms, coordinated to specific amino acid residues that affect their conformation and functionality. The process of metal incorporation typically occurs during or post-protein folding, often requiring chaperones for metal ion delivery and quality control. Progress in understanding metal incorporation and metalloprotein functionality has been enhanced by cell-free protein synthesis (CFPS) methods that offer direct control over the synthesis environment. This chapter reviews the diverse applications of CFPS methods in metalloprotein research, encompassing structure-function studies, protein engineering, and creation of artificial metalloproteins. Examples demonstrating the utility and advances brought about by CFPS in synthetic biology, electrochemistry, and drug discovery are highlighted. Despite remarkable progress, challenges remain in optimizing and advancing the CFPS methods, underscoring the need for future explorations in this transformative approach to metalloprotein study and engineering.

ML-based sequential analysis to assist selection between VMP and RD for newly diagnosed multiple myeloma.

Optimal first-line treatment that enables deeper and longer remission is crucially important for newly diagnosed multiple myeloma (NDMM). In this study, we developed the machine learning (ML) models predicting overall survival (OS) or response of the transplant-ineligible NDMM patients when treated by one of the two regimens-bortezomib plus melphalan plus prednisone (VMP) or lenalidomide plus dexamethasone (RD). Demographic and clinical characteristics obtained during diagnosis were used to train the ML models, which enabled treatment-specific risk stratification. Survival was superior when the patients were treated with the regimen to which they were low risk. The largest difference in OS was observed in the VMP-low risk & RD-high risk group, who recorded a hazard ratio of 0.15 (95% CI: 0.04-0.55) when treated with VMP vs. RD regimen. Retrospective analysis showed that the use of the ML models might have helped to improve the survival and/or response of up to 202 (39%) patients among the entire cohort (N = 514). In this manner, we believe that the ML models trained on clinical data available at diagnosis can assist the individualized selection of optimal first-line treatment for transplant-ineligible NDMM patients.

Evaluation of Robotic Systems on Cytotoxic Drug Preparation: A Systematic Review and Meta-Analysis.

Background and Objectives: With the increased prevalence of patients with cancer, the demand for preparing cytotoxic drugs was increased by health-system pharmacists. To reduce the workload and contamination of work areas in pharmacies, compounding robots preparing cytotoxic drugs have been introduced, and the use of the robots has been expanded in recent years. As reports on the comprehensive and quantitative evaluation of compounding robots remain lacking, a systematic review and meta-analysis were conducted to provide descriptive and quantitative evaluations of the accuracy of preparing injectable cytotoxic drugs. Materials and Methods: A systematic review and meta-analysis were conducted using published studies up to 2020. To identify eligible studies, PubMed, EMBASE, and Cochrane Library were used. All studies reporting the outcomes relevant to drug-compounding robots such as accuracy, safety, and drug contamination were included. Outcomes from included studies were descriptively summarized. Drug contamination by the robot was quantitatively analyzed using the odds ratio (OR) with a 95% confidence interval (CI). The risk of bias was assessed using the Risk of Bias Assessment tool for Non-randomized Studies (RoBANS). Results: A total of 14 compounding robot studies were eligible for review and 4 studies were included in the meta-analysis. Robotic compounding showed failure rates of 0.9-16.75%, while the accuracy range was set at 5%. Two studies reported that robotic compounding needed more time than manual compounding, two reported that robotic compounding needed less time, and one just reported preparation time without a control group. In a meta-analysis regarding the contamination of the compounding area, manual compounding was associated with lower contamination, although the result was not statistically significant (OR 4.251, 95% CI 0.439-51.772). For the contamination of infusion bags, the robot was associated with lower contamination (OR 0.176, 95% CI 0.084-0.365). Conclusions: Robotic compounding showed better accuracy than manual compounding and, without control groups, showed a high accuracy rate and also reduced the risk of drug contamination and compounding workload. The preparation time of the robot was not consistent because the type of robot and introduced system were different. In conclusion, robotic compounding showed mixed results compared to the manual compounding of drugs, so the system should be introduced considering the risks and benefits of robots.

Recent advances in utilization of ferredoxins for biosynthesis of valuable compounds.

Ferredoxin (Fd) is a small metalloprotein holding one or two Fe-S clusters in its inner shell. Like many other metalloproteins, Fd is redox active and involved in electron transfer during cellular metabolism. The electrons from reduced Fd are mostly used to regenerate NADPH under physiological conditions. Increasing number of attempts have been reported, however, where Fd delivers electrons to enable biosynthesis of valuable compounds. Various compounds ranging from H2 to vitamin D3 have been synthesized successfully using electrons mediated by Fd molecules. In this review, we provide an overview of the engineering studies utilizing Fd for biosynthesis of targeted molecules. The emphasis is on the role and activity of Fd as well as the methods used to improve the rate of electron transfer. Both microbial and electrochemical biosynthesis technologies are described and compared with respect to productivity and the compound being produced. In addition to the ferredoxins from the microbial organisms, artificially designed de novo types are described, highlighting the potential of the emerging computational methods used in metabolic and protein engineering. We believe that the recent advances in utilization of Fd for biosynthesis can result in breakthrough innovation across the biotechnology industry.

Optimization of the wood pellet supply during the continued increase of the renewable energy's proportion in the energy portfolio.

The share of renewable energy (RE) in the energy portfolio has been increasing steadily during the past decades. While the trend contributes in lowering the emission of greenhouse gases, it presents new challenges in terms of costs and intermittency. This study evaluates enhanced utilization of biomass energy as a viable solution and presents the mathematical framework for evaluating the costs associated with supplying the required amount of wood pellets. The framework addresses the uncertainty in the future price of pellets, as well as variability in the shipping and storage cost with respect to the supplier and type of facility. A case study based on the latest RE plan of the Korean government shows how the costs and net CO2 emission can change when the biomass energy is used to provide energy during the intermittency caused by the RE technologies. The results of the study suggest that a substantial difference in costs can occur depending on the supply strategy and that the biomass energy has the potential to resolve the intermittency issue while realizing the South Korean RE plan.

Predicting Dynamic Clinical Outcomes of the Chemotherapy for Canine Lymphoma Patients Using a Machine Learning Model.

First-line treatments of cancer do not always work, and even when they do, they cure the disease at unequal rates mostly owing to biological and clinical heterogeneity across patients. Accurate prediction of clinical outcome and survival following the treatment can support and expedite the process of comparing alternative treatments. We describe the methodology to dynamically determine remission probabilities for individual patients, as well as their prospects of progression-free survival (PFS). The proposed methodology utilizes the ex vivo drug sensitivity of cancer cells, their immunophenotyping results, and patient information, such as age and breed, in training machine learning (ML) models, as well as the Cox hazards model to predict the probability of clinical remission (CR) or relapse across time for a given patient. We applied the methodology using the three types of data obtained from 242 canine lymphoma patients treated by (L)-CHOP chemotherapy. The results demonstrate substantial enhancement in the predictive accuracy of the ML models by utilizing features from all the three types of data. They also highlight superior performance and utility in predicting survival compared to the conventional stratification method. We believe that the proposed methodology can contribute to improving and personalizing the care of cancer patients.

Selective ion transport through three-dimensionally interconnected nanopores of quaternized block copolymer membranes for energy harvesting application.

A concentration gradient in an aqueous solution is a promising source of energy that can be converted into electrical energy by an ion-exchange polymer membrane. In concentration-gradient energy harvesters, ion transport through nanoporous channels is an emerging approach to enhance the energy conversion efficiency. Since massive but selective ion transport could be realized through nanochannels, the theoretical calculations predicted that nanoporous membranes can extract significantly larger energy than the conventional non-structured membranes. In this regard, scientists in the field have attempted to produce nanoporous membranes on a macroscopic scale based on 1D, 2D, and 3D materials. However, the fabrication of nanoporous membranes is often accompanied by technical difficulties, which entails high production cost, low throughput, and poor scalability. In this study, we took advantage of the self-segregating properties of block copolymers (BCPs) to address these issues. In particular, the non-solvent-induced phase separation method has been utilized to produce three-dimensionally interconnected nanopores within BCP membranes. In addition, the neutral BCP nanopores' surface was modified with positive charges to allow selective diffusion of anions in concentration-gradient cells. By mounting the porous BCP membranes between two aqueous solutions with different concentrations, we studied the BCP-membrane-mediated energy-harvesting performance.

Investigation of the Ferredoxin's Influence on the Anaerobic and Aerobic, Enzymatic H2 Production.

Ferredoxins are metalloproteins that deliver electrons to several redox partners, including [FeFe] hydrogenases that are potentially a component of biological H2 production technologies. Reduced ferredoxins can also lose electrons to molecular oxygen, which may lower the availability of electrons for cellular or synthetic reactions. Ferredoxins thus play a key role in diverse kinds of redox biochemistry, especially the enzymatic H2 production catalyzed by [FeFe] hydrogenases. We investigated how the yield of anaerobic and aerobic H2 production vary among the four different types of ferredoxins that are used to deliver electrons extracted from NADPH within the synthetic, fermentative pathway. We also assessed the electron loss due to O2 reduction by reduced ferredoxins within the pathway, for which the difference was as high as five-fold. Our findings provide valuable insights for further improving biological H2 production technologies and can also facilitate elucidation of mechanisms governing interactions between Fe-S cluster(s) and molecular oxygen.

Predicting likelihood of in vivo chemotherapy response in canine lymphoma using ex vivo drug sensitivity and immunophenotyping data in a machine learning model.

We report a precision medicine platform that evaluates the probability of chemotherapy drug efficacy for canine lymphoma by combining ex vivo chemosensitivity and immunophenotyping assays with computational modelling. We isolated live cancer cells from fresh fine needle aspirates of affected lymph nodes and collected post-treatment clinical responses in 261 canine lymphoma patients scheduled to receive at least 1 of 5 common chemotherapy agents (doxorubicin, vincristine, cyclophosphamide, lomustine and rabacfosadine). We used flow cytometry analysis for immunophenotyping and ex vivo chemosensitivity testing. For each drug, 70% of treated patients were randomly selected to train a random forest model to predict the probability of positive Veterinary Cooperative Oncology Group (VCOG) clinical response based on input variables including antigen expression profiles and treatment sensitivity readouts for each patient's cancer cells. The remaining 30% of patients were used to test model performance. Most models showed a test set ROC-AUC > 0.65, and all models had overall ROC-AUC > 0.95. Predicted response scores significantly distinguished (P < .001) positive responses from negative responses in B-cell and T-cell disease and newly diagnosed and relapsed patients. Patient groups with predicted response scores >50% showed a statistically significant reduction (log-rank P < .05) in time to complete response when compared to the groups with scores <50%. The computational models developed in this study enabled the conversion of ex vivo cell-based chemosensitivity assay results into a predicted probability of in vivo therapeutic efficacy, which may help improve treatment outcomes of individual canine lymphoma patients by providing predictive estimates of positive treatment response.

O2 sensitivity and H2 production activity of hydrogenases-A review.

Hydrogenases are metalloproteins capable of catalyzing the interconversion between molecular hydrogen and protons and electrons. The iron-sulfur clusters within the enzyme enable rapid relay of electrons which are either consumed or generated at the active site. Their unparalleled catalytic efficiency has attracted attention, especially for potential use in H2 production and/or fuel cell technologies. However, there are limitations to using hydrogenases, especially due to their high O2 sensitivity. The subclass, called [FeFe] hydrogenases, are particularly more vulnerable to O2 but proficient in H2 production. In this review, we provide an overview of mechanistic and protein engineering studies focused on understanding and enhancing O2 tolerance of the enzyme. The emphasis is on ongoing studies that attempt to overcome O2 sensitivity of the enzyme while it catalyzes H2 production in an aerobic environment. We also discuss pioneering attempts to utilize the enzyme in biological H2 production and other industrial processes, as well as our own perspective on future applications.

System analysis and improved [FeFe] hydrogenase O2 tolerance suggest feasibility for photosynthetic H2 production.

Photosynthetic H2 production has been a compelling but elusive objective. Here we describe how coordinated bioreactor, metabolic pathway, and protein engineering now suggest feasibility for the sustainable, solar-powered production of a storable fuel to complement our expanding photovoltaic and wind based capacities. The need to contain and harvest the gaseous products provides decisive solar bioreactor design advantages by limiting O2 exposure to prolific, but O2-sensitive H2 producing enzymes-[FeFe] hydrogenases. CO2 supply and cell growth can also be limited so that most of the photosynthetic reduction capacity is directed toward H2 production. Yet, natural [FeFe] hydrogenases are still too O2 sensitive for technology implementation. We report the discovery of new variants and a new O2 tolerance mechanism that significantly reduce the sensitivity to O2 exposure without lowering H2 production rates or losing electrons to O2 reduction. Testing the improved hydrogenases with a biologically derived, light-dependent electron source provides evidence that this game changing technology has the potential for sustainable large-scale fuel production.

High-Throughput Screening of Catalytic H2 Production.

Hydrogenases, ferredoxins, and ferredoxin-NADP+ reductases (FNR) are redox proteins that mediate electron metabolism in vivo, and are also potential components for biological H2 production technologies. A high-throughput H2 production assay device (H2 PAD) is presented that enables simultaneous evaluation of 96 individual H2 production reactions to identify components that improve performance. Using a CCD camera and image analysis software, H2 PAD senses the chemo-optical response of Pd/WO3 thin films to the H2 produced. H2 PAD-enabled discovery of hydrogenase and FNR mutants that enhance biological H2 production is reported. From a library of 10 080 randomly mutated Clostridium pasteurianum [FeFe] hydrogenases, we found a mutant with nearly 3-fold higher H2 production specific activity. From a library of 400 semi-randomly mutated Oryza sativa FNR, the top hit enabled a 60 % increase in NADPH-driven H2 production rates. H2 PAD can also facilitate elucidation of fundamental biochemical mechanisms within these systems.

Characterization of [FeFe] Hydrogenase O2 Sensitivity Using a New, Physiological Approach.

[FeFe] hydrogenases catalyze rapid H2 production but are highly O2-sensitive. Developing O2-tolerant enzymes is needed for sustainable H2 production technologies, but the lack of a quantitative and predictive assay for O2 tolerance has impeded progress. We describe a new approach to provide quantitative assessment of O2 sensitivity by using an assay employing ferredoxin NADP+ reductase (FNR) to transfer electrons from NADPH to hydrogenase via ferredoxins (Fd). Hydrogenase inactivation is measured during H2 production in an O2-containing environment. An alternative assay uses dithionite (DTH) to provide reduced Fd. This second assay measures the remaining hydrogenase activity in periodic samples taken from the NADPH-driven reaction solutions. The second assay validates the more convenient NADPH-driven assay, which better mimics physiological conditions. During development of the NADPH-driven assay and while characterizing the Clostridium pasteurianum (Cp) [FeFe] hydrogenase, CpI, we detected significant rates of direct electron loss from reduced Fd to O2 However, this loss does not interfere with measurement of first order hydrogenase inactivation, providing rate constants insensitive to initial hydrogenase concentration. We show increased activity and O2 tolerance for a protein fusion between Cp ferredoxin (CpFd) and CpI mediated by a 15-amino acid linker but not for a longer linker. We suggest that this precise, solution phase assay for [FeFe] hydrogenase O2 sensitivity and the insights we provide constitute an important advance toward the discovery of the O2-tolerant [FeFe] hydrogenases required for photosynthetic, biological H2 production.

Public health, climate, and economic impacts of desulfurizing jet fuel.

In jurisdictions including the US and the EU ground transportation and marine fuels have recently been required to contain lower concentrations of sulfur, which has resulted in reduced atmospheric SO(x) emissions. In contrast, the maximum sulfur content of aviation fuel has remained unchanged at 3000 ppm (although sulfur levels average 600 ppm in practice). We assess the costs and benefits of a potential ultra-low sulfur (15 ppm) jet fuel standard ("ULSJ"). We estimate that global implementation of ULSJ will cost US$1-4bn per year and prevent 900-4000 air quality-related premature mortalities per year. Radiative forcing associated with reduction in atmospheric sulfate, nitrate, and ammonium loading is estimated at +3.4 mW/m(2) (equivalent to about 1/10th of the warming due to CO(2) emissions from aviation) and ULSJ increases life cycle CO(2) emissions by approximately 2%. The public health benefits are dominated by the reduction in cruise SO(x) emissions, so a key uncertainty is the atmospheric modeling of vertical transport of pollution from cruise altitudes to the ground. Comparisons of modeled and measured vertical profiles of CO, PAN, O(3), and (7)Be indicate that this uncertainty is low relative to uncertainties regarding the value of statistical life and the toxicity of fine particulate matter.

Silicon nanowire-based tunneling field-effect transistors on flexible plastic substrates.

A technique to implement silicon nanowire (SiNW)-based tunneling field-effect transistors (TFETs) on flexible plastic substrates is developed for the first time. The p-i-n configured Si NWs are obtained from an Si wafer using a conventional top-down CMOS-compatible technology, and they are then transferred onto the plastic substrate. Based on gate-controlled band-to-band tunneling (BTBT) as their working principle, the SiNW-based TFETs show normal p-channel switching behavior with a threshold voltage of -1.86 V and a subthreshold swing of 827 mV/dec. In addition, ambipolar conduction is observed due to the presence of the BTBT between the heavily doped p+ drain and n+ channel regions, indicating that our TFETs can operate in the n-channel mode as well. Furthermore, the BTBT generation rates for both the p-channel and n-channel operating modes are nearly independent of the bending state (strain = 0.8%) of the plastic substrate.

ZnO nanowire-based nano-floating gate memory with Pt nanocrystals embedded in Al(2)O(3) gate oxides.

The memory characteristics of ZnO nanowire-based nano-floating gate memory (NFGM) with Pt nanocrystals acting as the floating gate nodes were investigated in this work. Pt nanocrystals were embedded between Al(2)O(3) tunneling and control oxide layers deposited on ZnO nanowire channels. For a representative ZnO nanowire-based NFGM with embedded Pt nanocrystals, a threshold voltage shift of 3.8 V was observed in its drain current versus gate voltage (I(DS)-V(GS)) measurements for a double sweep of the gate voltage, revealing that the deep effective potential wells built into the nanocrystals provide our NFGM with a large charge storage capacity. Details of the charge storage effect observed in this memory device are discussed in this paper.