Computational Exploration of Bio-Degradation Patterns of Various Plastic Types.

Plastic materials are recalcitrant in the open environment, surviving for longer without complete remediation. The current disposal methods of used plastic material are inefficient; consequently, plastic wastes are infiltrating the natural resources of the biosphere. The mixed composition of urban domestic waste with different plastic types makes them unfavorable for recycling; however, natural assimilation in situ is still an option to explore. In this research work, we have utilized previously published reports on the biodegradation of various plastics types and analyzed the pattern of microbial degradation. Our results demonstrate that the biodegradation of plastic material follows the chemical classification of plastic types based on their main molecular backbone. The clustering analysis of various plastic types based on their biodegradation reports has grouped them into two broad categories of C-C (non-hydrolyzable) and C-X (hydrolyzable). The C-C and C-X groups show a statistically significant difference in their biodegradation pattern at the genus level. The Bacilli class of bacteria is found to be reported more often in the C-C category, which is challenging to degrade compared to C-X. Genus enrichment analysis suggests that Pseudomonas and Bacillus from bacteria and Aspergillus and Penicillium from fungi are potential genera for the bioremediation of mixed plastic waste. The lack of uniformity in reporting the results of microbial degradation of plastic also needs to be addressed to enable productive growth in the field. Overall, the result points towards the feasibility of a microbial-based biodegradation solution for mixed plastic waste.

Hematopoietic stem cells from poor and good mobilizers are qualitatively equivalent.

BACKGROUND: Marrow damage from chemo- and radiation therapies has been suggested to affect quality and quantity of hematopoietic stem cell (HSC) products. We tested the hypothesis that CD34+ cells (HSCs) from low mobilizers are qualitatively inferior to HSCs from high mobilizers. STUDY DESIGN AND METHODS: HSC quality was defined by proportion of primitive HSC subsets (CD34+CD38-, CD34+HLA-DR-, and CD34+ in G0 stage of cell cycle), the proportion of HSCs that express CXCR4 and CD26 homing proteins, and days to neutrophil and platelet (PLT) engraftments after transplant. HSC content and CD34 subsets analyses were performed using flow cytometry following the ISHAGE protocol. We evaluated the HSC quantity and quality of 139 autologous filgrastim-mobilized HSC products. Patients were categorized into low, moderate, and high mobilizers if their total HSC collection was less than 3 × 10(6), 3 × 10(6) or more and less than 5 × 10(6), and 5 × 10(6)/kg or more, respectively. RESULTS: The median number of primitive CD34 subsets increases with increasing HSC numbers and this association was significant (p = 0.001). However, when the ratios of the primitive CD34 subsets to total HSC counts were compared among the mobilization groups, the ratios were not significantly different. Coexpression of neither CD26 nor CXCR4 with CD34 antigen correlated with HSC mobilization. Evaluation of days to neutrophil engraftment among the mobilization groups did not show a significant difference (p = 0.1). However, days to PLT engraftment among the mobilization groups was significantly different (p = 0.05). CONCLUSION: The quality of HSCs from low mobilizers was comparable to HSCs from high mobilizers.

Revolutionizing enzyme engineering through artificial intelligence and machine learning.

The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.

A cadaveric study comparing the three approaches for ulnar nerve block at wrist.

BACKGROUND: Ulnar nerve blockade as a component of wrist block is a promising technique for adequate anesthesia and analgesia for different surgeries of the hand. Due to anatomical variations in the location of ulnar nerve under the flexor carpi ulnaris (FCU) a technique with good results and minimal complications are required. AIM: The aim of the following study is to compare the three techniques (volar, transtendinous volar [TTV] and ulnar) for ulnar nerve block at the wrist in human cadaveric wrists. MATERIALS AND METHODS: Our study was conducted using 40 cadaver wrists. After inserting standard hypodermic needles by three techniques for ulnar nerve blockade at the wrist, a detailed dissection of FCU was done. The mean distance from the tip of the needle to ulnar artery/nerve and number of instances in which the ulnar artery/nerve pierced were observed. RESULTS: Inter-group statistical significance was observed in measurement of the mean distance (mm) from the tip of the needle to the ulnar artery (volar [0.92 ± 0.11], TTV [3.96 ± 0.14] and ulnar [7.14 ± 0.08] approaches) and ulnar nerve (volar/TTV/ulnar approaches were 0.71 ± 0.12/3.61 ± 0.10/6.31 ± 0.49, respectively) (P = 0.001). Inadvertent intra-arterial/intraneural injections was seen with volar approach in 14 (35%) and 16 (40%) of the cadaveric wrists respectively, statistically significant with transtendinous and ulnar techniques of ulnar nerve block. CONCLUSION: TTV approach could be a better technique of choice for ulnar nerve blockade at the wrist because of its ease to practice, safer profile and minimum chances of inadvertent intra-arterial/intraneural injection with adequate anesthesia/analgesia.

Intubation with Airtraq laryngoscope in a morbidly obese patient.

In the present study, we report a case of successful endotracheal intubation using Airtraq(™) Laryngoscope (AQL) in a morbidly obese patient. A 35-year-old woman, morbidly obese (weight, 105 kg; height, 160 cm; BMI, 41 kg/m(2)), known hypertensive and diabetic, was admitted in the operating room for total abdominal hysterectomy under general anesthesia. The preoperative airway assessment anticipated both difficult bag-mask ventilation and intubation. Tracheal intubation using AQL was attempted after induction with propofol and relaxation with succinylcholine. Successful tracheal intubation was accomplished within 12 seconds of insertion of AQL into the oral cavity. The minimal hemodynamic response during this maneuver was advantageous in our patient.