Nevin Manimala

PLoS One. 2022 Mar 9;17(3):e0264374. doi: 10.1371/journal.pone.0264374. eCollection 2022.

ABSTRACT

Acinetobacter baumannii is an opportunistic gram-negative bacteria typically attributed to hospital-associated infection. It could also become multidrug-resistant (MDR), extensively drug-resistant (XDR), and pan drug-resistant (PDR) during a short period. Although A. baumannii has been documented extensively, complete knowledge on the antibiotic-resistant mechanisms and virulence factors responsible for pathogenesis has not been entirely elucidated. This study investigated the drug resistance pattern and characterized the genomic sequence by de novo assembly of PDR A. baumannii strain VJR422, which was isolated from a catheter-sputum specimen. The results showed that the VJR422 strain was resistant to any existing antibiotics. Based on de novo assembly, whole-genome sequences showed a total genome size of 3,924,675-bp. In silico and conventional MLST analysis of sequence type (ST) of this strain was new ST by Oxford MLST scheme and designated as ST1890. Moreover, we found 10,915 genes that could be classified into 45 categories by Gene Ontology (GO) analysis. There were 1,687 genes mapped to 34 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The statistics from Clusters of Orthologous Genes (COG) annotation identified 3,189 genes of the VJR422 strain. Regarding the existence of virulence factors, a total of 59 virulence factors were identified in the genome of the VJR422 strain by virulence factors of pathogenic bacteria databases (VFDB). The drug-resistant genes were investigated by searching in the Comprehensive Antibiotic Resistance Database (CARD). The strain harbored antibiotic-resistant genes responsible for aminoglycoside, β-lactam-ring-containing drugs, erythromycin, and streptogramin resistance. We also identified resistance-nodulation-cell division (RND) and the major facilitator superfamily (MFS) associated with the antibiotic efflux pump. Overall, this study focused on A. baumannii strain VJR422 at the genomic level data, i.e., GO, COG, and KEGG. The antibiotic-resistant genotype and phenotype as well as the presence of potential virulence associated factors were investigated.

PMID:35263355 | DOI:10.1371/journal.pone.0264374

PLoS Comput Biol. 2022 Mar 9;18(3):e1009960. doi: 10.1371/journal.pcbi.1009960. Online ahead of print.

ABSTRACT

We present a novel algorithm, implemented in the software ARGinfer, for probabilistic inference of the Ancestral Recombination Graph under the Coalescent with Recombination. Our Markov Chain Monte Carlo algorithm takes advantage of the Succinct Tree Sequence data structure that has allowed great advances in simulation and point estimation, but not yet probabilistic inference. Unlike previous methods, which employ the Sequentially Markov Coalescent approximation, ARGinfer uses the Coalescent with Recombination, allowing more accurate inference of key evolutionary parameters. We show using simulations that ARGinfer can accurately estimate many properties of the evolutionary history of the sample, including the topology and branch lengths of the genealogical tree at each sequence site, and the times and locations of mutation and recombination events. ARGinfer approximates posterior probability distributions for these and other quantities, providing interpretable assessments of uncertainty that we show to be well calibrated. ARGinfer is currently limited to tens of DNA sequences of several hundreds of kilobases, but has scope for further computational improvements to increase its applicability.

PMID:35263345 | DOI:10.1371/journal.pcbi.1009960

PLoS Biol. 2022 Mar 9;20(3):e3001579. doi: 10.1371/journal.pbio.3001579. Online ahead of print.

ABSTRACT

Understanding how antibiotic use drives resistance is crucial for guiding effective strategies to limit the spread of resistance, but the use-resistance relationship across pathogens and antibiotics remains unclear. We applied sinusoidal models to evaluate the seasonal use-resistance relationship across 3 species (Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae) and 5 antibiotic classes (penicillins, macrolides, quinolones, tetracyclines, and nitrofurans) in Boston, Massachusetts. Outpatient use of all 5 classes and resistance in inpatient and outpatient isolates in 9 of 15 species-antibiotic combinations showed statistically significant amplitudes of seasonality (false discovery rate (FDR) < 0.05). While seasonal peaks in use varied by class, resistance in all 9 species-antibiotic combinations peaked in the winter and spring. The correlations between seasonal use and resistance thus varied widely, with resistance to all antibiotic classes being most positively correlated with use of the winter peaking classes (penicillins and macrolides). These findings challenge the simple model of antibiotic use independently selecting for resistance and suggest that stewardship strategies will not be equally effective across all species and antibiotics. Rather, seasonal selection for resistance across multiple antibiotic classes may be dominated by use of the most highly prescribed antibiotic classes, penicillins and macrolides.

PMID:35263322 | DOI:10.1371/journal.pbio.3001579

Health Promot Chronic Dis Prev Can. 2022 Mar;42(3):104-112. doi: 10.24095/hpcdp.42.3.04.

ABSTRACT

INTRODUCTION: Due to the unprecedented impact of COVID-19, there is a need for research assessing pandemic-related challenges and stressors. The current study aimed to assess key concerns and general well-being among members of Canada’s Defence Team, including Canadian Armed Forces personnel and members of the Department of National Defence (DND) Public Service.

METHODS: The COVID-19 Defence Team Survey was administered electronically to Defence Team staff in April and May of 2020 and was completed by 13 688 Regular Force, 5985 Reserve Force and 7487 civilian DND Public Service personnel. Along with demographic information, the survey included assessments of work arrangement, pandemic-related concerns, general well-being and social and organizational support. Weighted data (to ensure representation) were used in all analyses.

RESULTS: The majority of respondents were working from home, with a small minority unable to work due to restrictions. Though many concerns were endorsed by a substantial proportion of respondents, the most prevalent concerns were related to the health and well-being of loved ones. The majority of respondents reported their partner, family, supervisors, friends, colleagues and children provided general support. Half of the civilian defence staff and one-third of military respondents reported a decline in mental health. Women, younger respondents, those with dependents and, in some cases, those who were single without children were at risk of lower well-being.

CONCLUSION: The pandemic has negatively impacted a substantial portion of the Defence Team. When responding to future crises, it is recommended that leaders of organizations provide additional supports to higher-risk groups and to supervisors who are ideally positioned to support employees during challenging times.

PMID:35262312 | DOI:10.24095/hpcdp.42.3.04

Probl Endokrinol (Mosk). 2022 Jan 27;68(1):94-100. doi: 10.14341/probl12849.

ABSTRACT

BACKGROUND: Melatonin is the main hormone of the pineal gland. By regulating circadian rhythms and being an immune regulator and antioxidant, this hormone takes part in the work of the ovaries: its high concentrations block apoptosis and neutralize reactive oxygen species involved in folliculogenesis, ovulation, egg maturation and corpus luteum formation.

AIM: To study melatonin status and its relationship with menstrual dysfunction and sleep disorders in obese women of reproductive age.

MATERIALS AND METHODS: In a one-stage comparative study, women 18-35 years old took part: 30 patients with obesity and menstrual disorders of an inorganic nature and 30 healthy women in the comparison group with normal weight and regular menstrual cycle. All participants underwent a questionnaire to identify somnological disorders, and the level of melatonin in saliva and 6-sulfatoxymelatonin in urine was also investigated.

RESULTS: In the group of patients with obesity (n=30), various sleep disorders were encountered in 47% of cases (p=0.003), including more often obstructive sleep apnea syndrome was recorded (30% of cases), and a correlation was found between the indicators of the questionnaire survey of subjective sleep characteristics and body mass index of patients (r=0.450, p=0.030) compared with a group of healthy women with normal weight (n=30). In the main group, the level of melatonin in saliva was statistically significantly lower than in the control: median 12.6 pg / ml and 25.5 pg / ml, respectively (p=0.008), the same pattern was recorded for 6-sulfatoxymelatonin: 14, 72 pg / ml and 31.12 pg / ml, respectively.

CONCLUSION: Patients with obesity and menstrual dysfunction are more likely to suffer from various sleep disorders and have lower levels of melatonin in saliva and 6-sulfatoxymelatonin in urine.

PMID:35262300 | DOI:10.14341/probl12849

Probl Endokrinol (Mosk). 2022 Jan 11;68(1):27-39. doi: 10.14341/probl12825.

ABSTRACT

BACKGROUND: For a comprehensive assessment of the effect of surgery in patients with primary hyperparathyroidism (PHPT), as well as for monitoring the condition of patients after treatment, it sounds reasonable to evaluate quality of life (QoL) and symptoms in PHPT patients before and after surgery.

AIM: The aim of this study was to assess changes in the QoL and symptoms in patients with PHPT after surgery.

MATERIALS AND METHODS: During prospective observational study, patients filled out QoL questionnaires and evaluated the presence and severity of their symptoms prior to parathyroidectomy (PTE) and 3, 12 months after surgery. Statistical analysis included the following methods: Student’s t-test or Wilcoxon’s non-parametric test, the generalized estimating equations (GEE), correlation analysis, χ2 and McNemar tests.

RESULTS: The study included 72 patients (mean age 52 years, 97.2% female) with symptomatic (68.1%) and asymptomatic (31.9%) PHPT. Before surgery patients with PHPT exhibited significantly decreased role functioning, physical and social well-being, and vitality. Half of PHPT patients experienced moderate-to-severe symptoms such as weakness, fatigue, loss of concentration, mood changes, as well as joint and bone pain; the association between symptoms experienced and the extent of QoL impairment before surgery was shown. Three months after PTE improvement in both physical and psychological components of QoL was shown. Positive QoL changes were demonstrated in patients with both symptomatic and asymptomatic PHPT and they preserved for 12 months after surgery. Also within 12 months after PTE significant decrease in PHPT-associated symptoms such as weakness, fatigue, loss of concentration and mood changes was found.

CONCLUSION: The results obtained demonstrate efficacy of PTE from the patient’s perspective and confirm the value of QoL assessment in PHPT patients in management of this patients’ population both for decision making and for evaluation of benefits of surgery and the degree of recovery of patients at long term follow-up.

PMID:35262295 | DOI:10.14341/probl12825

J Sep Sci. 2022 Mar 9. doi: 10.1002/jssc.202100864. Online ahead of print.

ABSTRACT

Pre-packed columns have been increasingly used in process development and biomanufacturing thanks to their ease of use and consistency. Traditionally, packing quality is predicted through rate models, which require extensive calibration efforts through independent experiments to determine relevant mass transfer and kinetic rate constants. Here we propose machine learning as a complementary predictive tool for column performance. A machine learning algorithm, extreme gradient boosting, was applied to a large data set of packing quality (plate height and asymmetry) for pre-packed columns as a function of quantitative parameters (column length, column diameter, particle size) and qualitative attributes (backbone and functional mode). The machine learning model offered excellent predictive capabilities for the plate height and the asymmetry (90% and 93%, respectively), with packing quality strongly influenced by backbone (∼70% relative importance) and functional mode (∼15% relative importance), well above all other quantitative column parameters. The results highlight the ability of machine learning to provide reliable predictions of column performance from simple, generic parameters, including strategic qualitative parameters such as backbone and functionality, usually excluded from quantitative considerations. Our results will guide further efforts in column optimization, e.g. by focusing on improvements of backbone and functional mode to obtain optimised packings. This article is protected by copyright. All rights reserved.

PMID:35262290 | DOI:10.1002/jssc.202100864

Plant Genome. 2022 Mar 8:e20197. doi: 10.1002/tpg2.20197. Online ahead of print.

ABSTRACT

Sweet corn (Zea mays L.) is consistently one of the most highly consumed vegetables in the United States, providing a valuable opportunity to increase nutrient intake through biofortification. Significant variation for carotenoid (provitamin A, lutein, zeaxanthin) and tocochromanol (vitamin E, antioxidants) levels is present in temperate sweet corn germplasm, yet previous genome-wide association studies (GWAS) of these traits have been limited by low statistical power and mapping resolution. Here, we employed a high-quality transcriptomic dataset collected from fresh sweet corn kernels to conduct transcriptome-wide association studies (TWAS) and transcriptome prediction studies for 39 carotenoid and tocochromanol traits. In agreement with previous GWAS findings, TWAS detected significant associations for four causal genes, β-carotene hydroxylase (crtRB1), lycopene epsilon cyclase (lcyE), γ-tocopherol methyltransferase (vte4), and homogentisate geranylgeranyltransferase (hggt1) on a transcriptome-wide level. Pathway-level analysis revealed additional associations for deoxy-xylulose synthase2 (dxs2), diphosphocytidyl methyl erythritol synthase2 (dmes2), cytidine methyl kinase1 (cmk1), and geranylgeranyl hydrogenase1 (ggh1), of which, dmes2, cmk1, and ggh1 have not previously been identified through maize association studies. Evaluation of prediction models incorporating genome-wide markers and transcriptome-wide abundances revealed a trait-dependent benefit to the inclusion of both genomic and transcriptomic data over solely genomic data, but both transcriptome- and genome-wide datasets outperformed a priori candidate gene-targeted prediction models for most traits. Altogether, this study represents an important step toward understanding the role of regulatory variation in the accumulation of vitamins in fresh sweet corn kernels.

PMID:35262278 | DOI:10.1002/tpg2.20197

J Appl Clin Med Phys. 2022 Mar 9:e13583. doi: 10.1002/acm2.13583. Online ahead of print.

ABSTRACT

PURPOSE: To develop a 3D-Unet dose prediction model to predict the three-dimensional dose distribution of volumetric modulated arc therapy (VMAT) for cervical cancer and test the dose prediction performance of the model in endometrial cancer to explore the feasibility of model generalization.

METHODS: One hundred and seventeen cases of cervical cancer and 20 cases of endometrial cancer treated with VMAT were used for the model training, validation, and test. The prescribed dose was 50.4 Gy in 28 fractions. Eight independent channels of contoured structures were input to the model, and the dose distribution was used as the output of the model. The 3D-Unet prediction model was trained and validated on the training set (n = 86) and validation set (n = 11), respectively. Then the model was tested on the test set (n = 20) of cervical cancer and endometrial cancer, respectively. The results between clinical dose distribution and predicted dose distribution were compared in the following aspects: (a) the mean absolute error (MAE) within the body, (b) the Dice similarity coefficients (DSCs) under different isodose volumes, (c) the dosimetric indexes including the mean dose (D_mean ), the received dose of 2 cm³ (D_2cc) , the percentage volume of receiving 40 Gy dose of organs-at-risk (V₄₀ ), planning target volume (PTV) D_98% , and homogeneity index (HI), (d) dose-volume histograms (DVHs).

RESULTS: The model can accurately predict the dose distribution of the VMAT plan for cervical cancer and endometrial cancer. The overall average MAE and maximum MAE for cervical cancer were 2.43 ± 3.17% and 3.16 ± 4.01% of the prescribed dose, respectively, and for endometrial cancer were 2.70 ± 3.54% and 3.85 ± 3.11%. The average DSCs under different isodose volumes is above 0.9. The predicted dosimetric indexes and DVHs are equivalent to the clinical dose for both cervical cancer and endometrial cancer, and there is no statistically significant difference.

CONCLUSION: A 3D-Unet dose prediction model was developed for VMAT of cervical cancer, which can predict the dose distribution accurately for cervical cancer. The model can also be generalized for endometrial cancer with good performance.

PMID:35262273 | DOI:10.1002/acm2.13583

J Appl Clin Med Phys. 2022 Mar 9:e13582. doi: 10.1002/acm2.13582. Online ahead of print.

ABSTRACT

This study presents a methodology to develop an X-ray technique chart for portable chest and abdomen imaging which utilizes patient data available in the modality worklist (MWL) to reliably achieve a predetermined exposure index (EI) at the detector for any patient size. The method assumes a correlation between the patients’ tissue equivalent thickness and the square root of the ratio of the patient’s weight to height. To assess variability in detector exposures, the EI statistics for 75 chest examinations and 99 abdominal portable X-ray images acquired with the new technique chart were compared to those from a single portable unit (chest: 3877 images; abdomen: 200 images) using a conventional technique chart with three patient sizes, and to a stationary radiography room utilizing automatic exposure control (AEC) (chest: 360 images; abdomen: 112 images). The results showed that when using the new technique chart on a group of portable units, the variability in EI was significantly reduced (p < 0.01) for both AP chest and AP abdomen images compared to the single portable using a standard technique chart with three patient sizes. The variability in EI for the images acquired with the new chart was comparable to the stationary X-ray room with an AEC system (p > 0.05). This method could be used to streamline the entire imaging chain by automatically selecting an X-ray technique based on patient demographic information contained in the MWL to provide higher quality examinations to clinicians by eliminating outliers. In addition, patient height and weight can be used to estimate the patients’ tissue equivalent thickness.

PMID:35262265 | DOI:10.1002/acm2.13582