Abstract: In February 2019, following the annual taxon ratification vote, the order Bunyavirales was amended by creation of two new families, four new subfamilies, 11 new genera and 77 new species, merging of two species, and deletion of one species. This article presents the updated taxonomy of the order Bunyavirales now accepted by the International Committee on Taxonomy of Viruses (ICTV).

Abstract: Combination antiretroviral therapy (ART) has significantly reduced the morbidity and mortality resulting from HIV infection. ART is, however, unable to eradicate HIV, which persists latently in several cell types and tissues. Phylogenetic analyses suggested that the proliferation of cells infected before ART initiation is mainly responsible for residual viremia, although controversy still exists. Conversely, it is widely accepted that drug resistance mutations (DRMs) do not appear during ART in patients with suppressed viral loads. Studies based on sequence clustering have in fact indicated that, at least in developed countries, HIV-infected ART-naive patients are the major source of drug-resistant viruses. Analysis of longitudinally sampled sequences have also shown that DRMs have variable fitness costs, which are strongly influenced by the viral genetic background.

Abstract: Flutamide is an antiandrogen drug that blocks the action of testosterone by binding to the androgen receptor. This study was designed to determine the impact of the Trivedi Effect®-Energy of Consciousness Healing Treatment on the physicochemical and thermal properties of flutamide. The test sample was divided into two parts, i.e., control and treated sample. The control part was known as untreated sample, while the treated part remotely received the Biofield Energy Healing Treatment by a renowned Biofield Energy Healer, Alice Branton. The study showed that the particle size values were significantly increased by 15.82%(d10), 16.36%(d50), 1.05%(d90), and 5.10% {D (4, 3)}; thus, the specific surface area was significantly decreased by 14.56% in the treated sample compared with the control sample. The PXRD peak intensities and crystallite sizes were significantly altered ranging from 7.02% to 29.41% and -9.17% to 17.86%, along with 2.84% increase in the average crystallite size in the treated flutamide compared to the control sample. The residue weight was significantly decreased by 64.16%; however, the maximum thermal degradation temperature was increased by 10.16% in the treated sample compared to the control sample. The latent heat of the treated sample reduced by 9.37% compared with the control sample. The results revealed the significant alteration in the crystallinity, particle size and thermal stability of the treated sample as compared to the untreated sample. Thus, the Biofield Energy Treated flutamide might improve the flowability, and compatibility compared with the untreated sample, that may help in designing a better pharmaceutical formulation in terms of its performance against various diseases.

Abstract: Berberine chloride is an isoquinoline alkaloid that has antimicrobial activity against bacteria, viruses, chlamydia, fungi, protozoans, and helminths, etc. The study was designed with the aim to evaluate the influence of the Trivedi Effect®- Consciousness Energy Healing Treatment on the physicochemical and thermal properties of berberine chloride by using modern analytical techniques. For this study, the berberine chloride sample was divided into two parts among which, one part was named as control as no Biofield Energy Treatment was given to it; while the other part received the Consciousness Energy Healing Treatment remotely by a renowned Biofield Energy Healer, Alice Branton and named as the Biofield Energy Treated sample. The PXRD results revealed some changes in the Bragg’s angle of peaks along with -7.24% to 188.78% alterations in the peak intensities and -65.88% to 135.42% changes in the crystallite sizes of the treated sample. Also, there was 1.91% decrease in the average crystallite size of the treated sample compared to the control sample. The particle size of the treated sample was significantly reduced by 20.16% (d10), 17.21% (d50), 27.53% (d90), and 27.36% {D(4,3)}; along with 15.31% increase in the specific surface area compared with the control sample. The total weight loss was significantly reduced by 25.81%; however, the residue amount was significantly increased by 55.04% in the treated sample compared to the control sample. Besides, the DSC thermogram showed four peaks, in which the peak temperatures of the treated sample corresponding to 1st, 2nd, 3rd, and 4th peak were altered by -2.26%, 3.55%, -4.50%, and -0.69%, respectively. Moreover, the latent heat for 1st, 2nd, 3rd, and 4th peak of the treated sample were significantly reduced by 24.04%, 42.44%, 44.26%, and 47.78%, respectively, compared with the control sample. The results showed that the Trivedi Effect®-Consciousness Energy Healing Treatment might help in improving the solubility, absorption, and bioavailability of berberine chloride compared with the control sample. Hence, the Consciousness Energy Healing Treatment would be useful for designing the novel nutraceutical/pharmaceutical formulations of berberine chloride with improved drug profile for the treatment of various microbial diseases and disorders such as diarrhea, gastroenteritis, hypertension, tumor, malaria, hyperglycemia, inflammation, arrhythmia, infections, etc.

Abstract: Abstract Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution, and Energy-based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41?kcal/mol on a diverse training dataset consisting of 1102 mutations in 57 protein-protein complexes. It competes with existing state-of-the-art methods on two blind test datasets. Predictions for a new dataset of 487 mutations in 56 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC?<?0.42), although their combination does improve the predictions. Feature analysis for iSEE underlines the significance of evolutionary conservations for quantitative prediction of mutation effects. As an application example, we perform a full mutation scanning of the interface residues in the MDM2?p53 complex.