Tag: nevin manimala

Chemosphere. 2021 Jul 8;286(Pt 1):131479. doi: 10.1016/j.chemosphere.2021.131479. Online ahead of print.

ABSTRACT

The present work investigates for the first time the presence and isolation of the thermophilic fungi from hydrothermal spring situated at the locality of Guelma, in the Northeast of Algeria. The production of the thermostable proteases and the optimization of culture conditions under agro-wastes solid-state fermentation to achieve optimal production capacity were explored. A statistical experimental approach consisting of two designs was used to determine the optimum culture conditions and to attain the greatest enzyme production. Besides, different agricultural wastes were initially evaluated as a substrate, whereby wheat bran was selected for enzyme production by the isolate under solid-state conditions. The isolate thermophilic fungi were identified as Mycothermus thermophilus by sequencing the ITS region of the rDNA (NCBI Accession No: MK770356.1). Among the various screened variables: the temperature, the inoculum size, and the moisture were proved to have the most significant effects on protease activity. Employing two-level fractional Plackett-Burman and a Box-Behnken designs statistical approach helped in identifying optimum values of screened factors and their interactions. The analysis showed up 6.17-fold improvement in the production of proteases (~1187.03 U/mL) was achieved under the optimal conditions of moisture content 47%, inoculum 5 × 10⁵ spores/g, and temperature at 42 °C. These significant findings highlight the importance of the statistical design in isolation of Mycothermus thermophilus species from a specific location as well as identifying the optimal culture conditions for maximum yield.

PMID:34315081 | DOI:10.1016/j.chemosphere.2021.131479

Eur J Radiol. 2021 Jul 22;142:109867. doi: 10.1016/j.ejrad.2021.109867. Online ahead of print.

ABSTRACT

PURPOSE: We aimed to compare the diagnostic performance of PET/CT imaging performed with ⁶⁸Ga-DOTA-FAPI and ¹⁸FDG in detection of liver metastases in patients with gastrointestinal system (GIS) cancer.

METHODS: A total of 31 patients who underwent ⁶⁸Ga-DOTA-FAPI and ¹⁸F-FDG PET/CT examinations and diagnosed with GIS cancer (15 colorectal, 9 pancreas, 4 stomach and 3 other cancers) were included in the study. The presence of liver metastasis was decided based on histopathologic diagnosis, PET/CT, other radiologic examinations or tumor biomarker findings, and both PET/CT imaging findings were compared on the patient and lesion basis.

RESULTS: Of the 31 patients, 28 were found as true positive with ⁶⁸Ga-DOTA-FAPI-PET/CT and 17 with ¹⁸FDG-PET/CT. Of the 98 metastatic liver lesions determined according to our diagnostic criteria, 92 were found as true positive lesions with ⁶⁸Ga-DOTA-FAPI-PET/CT and 65 with ¹⁸FDG-PET/CT. There was a statistically significant difference between both imaging modalities in the patient and lesion based comparisons (p < 0.05). When semiquantitative values (SUVmax, mlr) obtained from the lesions were compared between the two imaging methods, mlr values showed statistically significant difference in all tumor subgroups (p < 0.05).

CONCLUSION: It was concluded that ⁶⁸Ga-DOTA-FAPI-PET/CT was superior over ¹⁸FDG-PET/CT in detection of liver metastases of GIS cancers and it can be a complementary method especially in negative cases with ¹⁸FDG-PET/CT.

PMID:34315086 | DOI:10.1016/j.ejrad.2021.109867

Clin Neurophysiol. 2021 Jul 3;132(9):2232-2239. doi: 10.1016/j.clinph.2021.06.012. Online ahead of print.

ABSTRACT

OBJECTIVE: To explore relationship between EEG theta activity and clinical data that imply the degree of genetic determination of epilepsy.

METHODS: Clinical data of interest were epilepsy diagnosis and positive / negative family history of epilepsy. Study groups were: idiopathic generalized epilepsy (IGE), focal epilepsy (FE); FE of unknown etiology (FEUNK), FE of postnatal-acquired etiology (FEPA); all patients with positive / negative family history of epilepsy (FAPALL, FANALL, respectively), disregarding of the syndrome; FAP patients with 1st degree affected relative (FAP1) and those with 2nd degree epileptic relative only (FAP2). Quantitative EEG analysis assessed amount of theta (3.5-7.0 Hz) activity in 180 seconds of artifact-free waking EEG background activity for each patient and group. Group comparison was carried out by nonparametric statistics.

RESULTS: Differences of theta activity were: FAPALL > FANALL (p = 0.01); FAP1 > FAP2 (p = 0.2752). IGE > FE (p = 0.02); FEUNK > FEPA (p = 0.07).

CONCLUSIONS: This was the first attempt to explore and quantitatively ascertain relationship between an EEG variable and clinical data that imply greater or lesser degree of genetic determination in epilepsy.

SIGNIFICANCE: Theta activity is endophenotype that bridges the gap between epilepsy susceptibility genes and clinical phenotypes. Amount of theta activity is indicative of degree of genetic determination of the epilepsies.

PMID:34315064 | DOI:10.1016/j.clinph.2021.06.012

Chemosphere. 2021 Jul 23;286(Pt 1):131660. doi: 10.1016/j.chemosphere.2021.131660. Online ahead of print.

ABSTRACT

Biochar mediated pollutant removal is gaining attention because of high efficiency of the process. However, effective recycling avenues of the pollutant-saturated biochars are scarce in the knowledge base; while such materials can be a new source of long-range contamination. Therefore, potential of vermitechnology for eco-friendly recycling of pollutant-loaded biochar was assessed by using arsenic-saturated native (NBC) and exfoliated (EBC) biochars as feedstocks for the first time. Interestingly, the bioavailable arsenic fractions (water soluble and exchangeable) considerably reduced by 22-44 % with concurrent increment (~8-15 %) of the recalcitrant (residual and organic bound) fractions in the biochar-based feedstocks. Consequently, ~2-3 folds removal of the total arsenic was achieved through vermicomposting. The earthworm population growth (2.5-3 folds) was also highly satisfactory in the biochar-based feedstocks. The results clearly imply that Eisenia fetida could compensate the arsenic-induced stress to microbial population and greatly augmented microbial biomass, respiration and enzyme activity by 3-12 folds. Moreover, biochar-induced alkalinity was significantly neutralized in the vermibeds, which remarkably balanced the TOC level and nutrient (N, P, and K) availability particularly in EBC + CD vermibeds. Overall, the nutrient recovery potential and arsenic removal efficiency of vermitechnology was clearly exhibited in NBC/EBC + CD (12.5:87.5) feedstocks. Hence, it is abundantly clear that vermitechnology can be a suitable option for eco-friendly recycling of pollutant-saturated sorbing agents, like biochars.

PMID:34315078 | DOI:10.1016/j.chemosphere.2021.131660

J Neurol Sci. 2021 Jul 21;428:117584. doi: 10.1016/j.jns.2021.117584. Online ahead of print.

ABSTRACT

The majority of radiology studies in neurodegenerative conditions infer group-level imaging traits from group comparisons. While this strategy is helpful to define phenotype-specific imaging signatures for academic use, the meaningful interpretation of single scans of individual subjects is more important in everyday clinical practice. Accordingly, we present a computational method to evaluate individual subject diffusion tensor data to highlight white matter integrity alterations. Fifty white matter tracts were quantitatively evaluated in 132 patients with amyotrophic lateral sclerosis (ALS) with respect to normative values from 100 healthy subjects. Fractional anisotropy and radial diffusivity alterations were assessed individually in each patient. The approach was validated against standard tract-based spatial statistics and further scrutinised by the assessment of 78 additional data sets with a blinded diagnosis. Our z-score-based approach readily detected white matter degeneration in individual ALS patients and helped to categorise single subjects with a ‘blinded diagnosis’ as likely ‘ALS’ or ‘control’. The group-level inferences from the z-score-based approach were analogous to the standard TBSS output maps. The benefit of the z-score-based strategy is that it enables the interpretation of single DTI datasets as well as the comparison of study groups. Outputs can be summarised either visually by highlighting the affected tracts, or, listing the affected tracts in a text file with reference to normative data, making it particularly useful for clinical applications. While individual diffusion data cannot be visually appraised, our approach provides a viable framework for single-subject imaging data interpretation.

PMID:34315000 | DOI:10.1016/j.jns.2021.117584

Forensic Sci Int Genet. 2021 Jul 17;54:102564. doi: 10.1016/j.fsigen.2021.102564. Online ahead of print.

ABSTRACT

This study examines the potential of hair shaft proteomic analysis to delineate genetic relatedness. Proteomic profiling and amino acid sequence analysis provide information for quantitative and statistically-based analysis of individualization and sample similarity. Protein expression levels are a function of cell-specific transcriptional and translational programs. These programs are greatly influenced by an individual’s genetic background, and are therefore influenced by familial relatedness as well as ancestry and genetic disease. Proteomic profiles should therefore be more similar among related individuals than unrelated individuals. Likewise, profiles of genetically variant peptides that contain single amino acid polymorphisms, the result of non-synonymous SNP alleles, should behave similarly. The proteomically-inferred SNP alleles should also provide a basis for calculation of combined paternity and sibship indices. We test these hypotheses using matching proteomic and genetic datasets from a family of two adults and four siblings, one of which has a genetic condition that perturbs hair structure and properties. We demonstrate that related individuals, compared to those who are unrelated, have more similar proteomic profiles, profiles of genetically variant peptides and higher combined paternity indices and combined sibship indices. This study builds on previous analyses of hair shaft protein profiling and genetically variant peptide profiles in different real-world scenarios including different human hair shaft body locations and pigmentation status. It also validates the inclusion of proteomic information with other biomolecular substrates in forensic hair shaft analysis, including mitochondrial and nuclear DNA.

PMID:34315035 | DOI:10.1016/j.fsigen.2021.102564

Forensic Sci Int. 2021 Jul 20;326:110912. doi: 10.1016/j.forsciint.2021.110912. Online ahead of print.

ABSTRACT

In the branch of forensic science known as firearm evidence identification, various similarity scores have been proposed to compare firearm marks. Some similarity score comparisons, for example, congruent matching cells (CMC) method, are based on pass-or-fail tests. The CMC method compares the pairwise topography images of breech face impressions, from which the similarity score is derived for quantifying their topography similarity. For an image pair, the CMC method determines a certain number of correlated cell pairs. Next, each correlated pair is determined to be a congruent match cell (CMC) pair, or not based on several identification parameters. The number of CMC pairs as a threshold is required so that the two images of surface topographies can be either identified as matching or determined to be non-matching. To reliably estimate error rates or evaluate likelihood ratio (LR), the key is to find an appropriate probability distribution for the frequency distribution of the observed CMC results. This paper discusses four statistical models for CMC measurements, which are binomial and three binomial-related probability distributions. In previous studies, for a sequence of binomial distributed or other binomial-related distributed random variables (r.v.), the number of Bernoulli trials N for each r.v. is assumed to be the same. However, in practice, N(the number of cell pairs in an image pair) varies from one r.v. (or one image pair) to another. In that case, the term, frequency function, of the CMC results is not appropriate. In this paper, the generalized frequency function is introduced to depict the behavior of the CMC values and its limiting distribution is provided. Based on that, nonlinear regression models are used to estimate the model parameters. The methodology is applied to a set of actual CMC values of fired cartridge cases.

PMID:34314987 | DOI:10.1016/j.forsciint.2021.110912

J Psychiatr Res. 2021 Jul 22;142:40-47. doi: 10.1016/j.jpsychires.2021.07.036. Online ahead of print.

ABSTRACT

Co-occurring posttraumatic stress disorder and alcohol use disorder (PTSD/AUD) is associated with poorer psychosocial functioning than either disorder alone; however, it is unclear if psychosocial functioning improves in treatment for PTSD/AUD. This study examined if psychosocial functioning improved in integrated treatments for PTSD/AUD, and if changes in PTSD severity and percentage heavy drinking days (PHDD) during treatment were associated with functioning outcomes. 119 veterans with PTSD/AUD randomized to receive either Concurrent Treatment of PTSD and Substance Use Disorders using Prolonged Exposure or Seeking Safety completed measures of functioning (Medical Outcomes Survey SF-36), PTSD (Clinician Administered PTSD Scale for DSM-5), and alcohol use (Timeline Follow-Back) at baseline, posttreatment, 3- and 6-month follow-ups. Our findings suggest that psychosocial functioning improved to a statistically significant degree with no significant differences between conditions. Reductions in PTSD severity during treatment were associated with psychosocial functioning improvements, whereas reductions in PHDD were associated with improvement in role impairment at posttreatment. Although psychosocial functioning improves to a statistically significant degree in interventions designed to treat PTSD/AUD, these improvements do not represent clinically meaningful improvements in patients’ abilities to navigate important roles. Findings underscore the need to study how to best treat psychosocial functioning impairment in PTSD/AUD.

PMID:34314993 | DOI:10.1016/j.jpsychires.2021.07.036

Med Image Anal. 2021 Jul 18;73:102164. doi: 10.1016/j.media.2021.102164. Online ahead of print.

ABSTRACT

[Background] Electromagnetically Navigated Bronchoscopy (ENB) is currently the state-of-the art diagnostic and interventional bronchoscopy. CT-to-body divergence is a critical hurdle in ENB, causing navigation error and ultimately limiting the clinical efficacy of diagnosis and treatment. In this study, Visually Navigated Bronchoscopy (VNB) is proposed to address the aforementioned issue of CT-to-body divergence. [Materials and Methods] We extended and validated an unsupervised learning method to generate a depth map directly from bronchoscopic images using a Three Cycle-Consistent Generative Adversarial Network (3cGAN) and registering the depth map to preprocedural CTs. We tested the working hypothesis that the proposed VNB can be integrated to the navigated bronchoscopic system based on 3D Slicer, and accurately register bronchoscopic images to pre-procedural CTs to navigate transbronchial biopsies. The quantitative metrics to asses the hypothesis we set was Absolute Tracking Error (ATE) of the tracking and the Target Registration Error (TRE) of the total navigation system. We validated our method on phantoms produced from the pre-procedural CTs of five patients who underwent ENB and on two ex-vivo pig lung specimens. [Results] The ATE using 3cGAN was 6.2 +/- 2.9 [mm]. The ATE of 3cGAN was statistically significantly lower than that of cGAN, particularly in the trachea and lobar bronchus (p < 0.001). The TRE of the proposed method had a range of 11.7 to 40.5 [mm]. The TRE computed by 3cGAN was statistically significantly smaller than those computed by cGAN in two of the five cases enrolled (p < 0.05). [Conclusion] VNB, using 3cGAN to generate the depth maps was technically and clinically feasible. While the accuracy of tracking by cGAN was acceptable, the TRE warrants further investigation and improvement.

PMID:34314953 | DOI:10.1016/j.media.2021.102164

J Environ Manage. 2021 Jul 24;297:113344. doi: 10.1016/j.jenvman.2021.113344. Online ahead of print.

ABSTRACT

Although the effect of digital elevation model (DEM) and its spatial resolution on flood simulation modeling has been well studied, the effect of coarse and finer resolution image and DEM data on machine learning ensemble flood susceptibility prediction has not been investigated, particularly in data sparse conditions. The present work was, therefore, to investigate the performance of the resolution effects, such as coarse (Landsat and SRTM) and high (Sentinel-2 and ALOS PALSAR) resolution data on the flood susceptible models. Another motive of this study was to construct very high precision and robust flood susceptible models using standalone and ensemble machine learning algorithms. In the present study, fifteen flood conditioning parameters were generated from both coarse and high resolution datasets. Then, the ANN-multilayer perceptron (MLP), random forest (RF), bagging (B)-MLP, B-gaussian processes (B-GP) and B-SMOreg algorithms were used to integrate the flood conditioning parameters for generating the flood susceptible models. Furthermore, the influence of flood conditioning parameters on the modelling of flood susceptibility was investigated by proposing an ROC based sensitivity analysis. The validation of flood susceptibility models is also another challenge. In the present study, we proposed an index of flood vulnerability model to validate flood susceptibility models along with conventional statistical techniques, such as the ROC curve. Results showed that the coarse resolution based flood susceptibility MLP model has appeared as the best model (area under curve: 0.94) and it has predicted 11.65 % of the area as very high flood susceptible zones (FSz), followed by RF, B-MLP, B-GP, and B-SMOreg. Similarly, the high resolution based flood susceptibility model using MLP has predicted 19.34 % of areas as very high flood susceptible zones, followed by RF (14.32 %),B-MLP (14.88 %), B-GP, and B-SMOreg. On the other hand, ROC based sensitivity analysis showed that elevation influences flood susceptibility largely for coarse and high resolution based models, followed by drainage densityand flow accumulation. In addition, the accuracy assessment using the IFV model revealed that the MLP model outperformed all other models in the case of a high resolution imageThe coarser resolution image’s performance level is acceptable but quite low. So, the study recommended the use of high resolution images for developing a machine learning algorithm based flood susceptibility model. As the study has clearly identified the areas of higher flood susceptibility and the dominant influencing factors for flooding, this could be used as a good database for flood management.

PMID:34314957 | DOI:10.1016/j.jenvman.2021.113344