Optimization of Degenerate PCR Conditions for Reducing Error Rates in Detection of PKS and NRPS Gene groups in Actinomycetes

Background

The screen of Polyketide Synthase (PKS) and Nonribosomal Peptide Synthetase (NRPS) gene groups is a quick way to discover new therapeutic agents. However, errors in laboratory techniques cause a loss of touch with reality. This study aimed to evaluate the presence of PKS and NRPS gene groups in previously isolated strains by optimizing their specialized amplification by degenerate primers and indicating the evolutionary relationships with reference strains.

Methods

PKS-I, II, and NRPS genes PCR amplification was performed using three degenerate primer sets for 22 actinomycete strains with antibacterial activity. Annealing temperature and the amount of template DNA and primers were optimized. PCR products of PKS-I, II, and NRPS from three strains were sequenced after TA cloning. Besides, strains with high antibacterial activity were identified by biochemical features and partial 16S rDNA sequencing and hypothetically classified by a phylogenetic tree.

Results

High frequencies of PKS-I (86.4%), PKS-II (81.8%), and NRPS (95.4%) genes were found among the strains after optimization. Fourteen strains (64%) contained all of the genes, and 100% of strains had at least two genes. These numbers are pretty distinct in comparison with the previous researches. All of the sequenced strains were members of Streptomyces genus.

Conclusion

Our research showed that degenerate PCR strongly depends on the variation of annealing temperature and primer concentration, resulting in an unexpected shift in PCR outputs. The sequencing results confirmed the optimized conditions for specialized PCR of PKS-I, PKS-II, and NRPS gene groups.

Grayscale Image Statistical Attributes Effectively Distinguish the Severity of Lung Abnormalities in CT Scan Slices of COVID-19 Patients

Grayscale statistical attributes analysed for 513 extract images taken from pulmonary computed tomography (CT) scan slices of 57 individuals (49 confirmed COVID-19 positive; eight confirmed COVID-19 negative) are able to accurately predict a visual score (VS from 0 to 4) used by a clinician to assess the severity of lung abnormalities in the patients. Some of these attributes can be used graphically to distinguish useful but overlapping distributions for the VS classes. Using machine and deep learning (ML/DL) algorithms with twelve grayscale image attributes as inputs enables the VS classes to be accurately distinguished. A convolutional neural network achieves this with better than 96% accuracy (only 18 images misclassified out of 513) on a supervised learning basis. Analysis of confusion matrices enables the VS prediction performance of ML/DL algorithms to be explored in detail. Those matrices demonstrate that the best performing ML/DL algorithms successfully distinguish between VS classes 0 and 1, which clinicians cannot readily do with the naked eye. Just five image grayscale attributes can also be used to generate an algorithmically defined scoring system (AS) that can also graphically distinguish the degree of pulmonary impacts in the dataset evaluated. The AS classification illustrated involves less overlap between its classes than the VS system and could be exploited as an automated expert system. The best-performing ML/DL models are able to predict the AS classes with better than 99% accuracy using twelve grayscale attributes as inputs. The decision tree and random forest algorithms accomplish that distinction with just one classification error in the 513 images tested.

Grayscale image statistics of COVID‐19 patient CT scans characterize lung condition with machine and deep learning

Background

Grayscale image attributes of computed tomography (CT) of pulmonary scans contain valuable information relating to patients with respiratory ailments. These attributes are used to evaluate the severity of lung conditions of patients confirmed to be with and without COVID‐19.

Method

Five hundred thirteen CT images relating to 57 patients (49 with COVID‐19; 8 free of COVID‐19) were collected at Namazi Medical Centre (Shiraz, Iran) in 2020 and 2021. Five visual scores (VS: 0, 1, 2, 3, or 4) are clinically assigned to these images with the score increasing with the severity of COVID‐19‐related lung conditions. Eleven deep learning and machine learning techniques (DL/ML) are used to distinguish the VS class based on 12 grayscale image attributes.

Results

The convolutional neural network achieves 96.49% VS accuracy (18 errors from 513 images) successfully distinguishing VS Classes 0 and 1, outperforming clinicians’ visual inspections. An algorithmic score (AS), involving just five grayscale image attributes, is developed independently of clinicians’ assessments (99.81% AS accuracy; 1 error from 513 images).

Conclusion

Grayscale CT image attributes can be successfully used to distinguish the severity of COVID‐19 lung damage. The AS technique developed provides a suitable basis for an automated system using ML/DL methods and 12 image attributes.

Grayscale image statistics of CT scans can effectively classify lung abnormalities

Graphical trends of grayscale statistics distinguish visual assessments COVID‐19 classes

Machine/deep learning algorithms predict severity from image grayscale attributes

Algorithmic class systems can be established using just five grayscale attributes

Confusion matrices provide detailed insight to algorithm prediction capabilities

Production of calcite nanocrystal by a urease-positive strain of enterobacter ludwigii and study of its structure by SEM

The present research aimed at evaluating the effects of urease enzyme and increasing pH on calcite nanocrystal formation. Unlike some researches, the results showed that CaCO3 precipitation is not a general phenomenon among the bacteria and if a bacterium has not this ability, it will not be able to produce calcite even with an increase in pH. All urease-positive bacteria had this ability, while only some urease-negative bacteria were able to produce calcite. Production and characterization of nanocrystals on precipitating medium were shown primarily by light microscopy and then confirmed by X-ray diffraction (XRD) analysis. Crystallite particle size was determined using Scherrer formula that was sub-100-nm in all samples. Based on qualitative and quantitative studies, strain C8 was selected as the best calcite-producing strain. Phylogenetic analysis indicated that this isolate has 99 % similarity with Enterobacter ludwigii. 16S rRNA sequence of isolate was deposited in GenBank with accession number JX666242. The morphology and exact composition of nanocrystalline particles were determined using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). According to data obtained by SEM, we suggest that nanocrystals of CaCO3 adhere to bacteria and each other to form small aggregates and then complex crystalline networks to trap bacteria. Many holes are present in these crystalline networks that seem to be due to the aggregation of nanocrystals.

Increasing the antibacterial activity of gentamicin in combination with extracted polyphosphate from Bacillus megaterium

AIMS

The aim of this research was production of polyphosphate (poly P) and study on its antibacterial effects.

METHODS AND RESULTS

Poly P granules in the cells were observed with the help of Albert staining and extracted by Mussig-Zufika method. Thin layer chromatography and nuclear magnetic resonance spectroscopy ((31) P NMR) were used to characterize properties of these granules. Relation of phosphorus consumption and poly P production with growth was determined by the vanado-molybdate colorimetric method. Among the 60 strains of bacteria isolated from the environmental samples, strain G11 showed ability for the formation of high levels of poly P. Phylogenetic analysis showed that this isolate had 98% similarity with Bacillus megaterium. 16S rRNA sequence of isolate was deposited in GenBank with accession number JX115009. The average poly P chain length was 10·5 in this bacterium. The antimicrobial activity of bacterial extracted poly P was much better than chemical poly P, and its interaction with gentamicin increased the activity of this drug. The best synergistic activity of this interaction was observed for Corynebacterium glutamicum and Pseudomonas aeruginosa species. The highest adsorption of phosphorus occurred in stationary phase of growth curve, and then the amount of phosphorus increased in medium by degradation of stored poly P.

CONCLUSIONS

In this study, we isolated a high-level producer bacterium of poly P and extracted poly P by chemical treatment. In addition, we compared antimicrobial activity of chemical poly P with bacterial poly P and its interaction with gentamicin against both Gram-positive and Gram-negative bacteria.

SIGNIFICANCE AND IMPACT OF THE STUDY

Many studies have shown that bacteria are becoming resistant to gentamicin sulphate. In this study, we approved that Acinetobacter baumannii, a pathogenic gentamicin-resistant bacterium, is sensitive to bacterial poly P, and thus, this poly P can be substituted for gentamicin in treatment.