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.

The Effect of Subcutaneous Dexamethasone to Reduce Edema and Ecchymosis in Rhinoplasty Patients.

BACKGROUND: Rhinoplasty is one of the most common types of cosmetic surgery undertaken. In most rhinoplasty patients, an osteotomy is used to reshape the nasal pyramid. The most common complications following osteotomy are edema and ecchymosis. Edema and ecchymosis have a significant effect on a patients' satisfaction with surgery and their return to social activities. For this purpose, various methods have been used to reduce edema and ecchymosis, including intravenous injection of corticosteroids, cold compresses, and tranexamic acid. OBJECTIVE: To reduce edema and ecchymosis in rhinoplasty patients by administering a subcutaneous injection of dexamethasone and thereby prevent unwanted systemic side effects of corticosteroid treatments. METHOD: We conduct a hospital-based nonrandomised study of rhinoplasty patients, with their informed consent treated over the course of one year. Dexamethasone was injected on one side of consenting patient's face immediately before surgery and the results were compared with the opposite side that was not injected. The face images of patients were taken on the front view on the first, third, seventh, and fourteenth days following the treatment. The grade of edema and ecchymosis encountered in each patient was determined by three ENT specialists. The degree of edema and ecchymosis was compared on the injected and noninjected sides and the findings were statistically analysed. The nonrandomised study considered 42 rhinoplasty patients. The mean age of patients was 27.9 years and their age ranged between 17 and 52 years. For 20 patients (47.6%), injection was performed on the right side, and for 22 patients (52.3%), injection was performed on the left side. Findings. The statistical analysis of patient outcomes reveals that a supraperiosteal injection of dexamethasone was not effective in reducing edema and ecchymosis after rhinoplasty.

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.