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IntroductionMycobacterium tuberculosis (Mtb) has the capability to dodge the immune system by escaping into alternate physiological forms by forming drug tolerant populations under the immune pressure in the host. New drugs are urgently needed to treat these non-replicating persisters. In the past, aminoglycoside antibiotics have played a pivotal role in TB chemotherapy.MethodsHere, we explored the therapeutic potential of a monosubstituted deoxystreptamine aminoglycoside, apramycin (APR) which is different in its chemical structure from the other clinically relevant aminoglycoside antibiotics that are all disubstituted, e.g., amikacin (AMI). We determined the APR MIC as 0.25-1 µg/ml for sensitive and multidrug-resistant Mtb (MDRTB), including amikacin (AMI) resistant strains.ResultsIn standard time-kill kinetic assays, the bactericidal activity of APR was similar to that of AMI demonstrating dose-dependent killing of planktonic Mtb. However, in biofilm and macrophage intracellular killing assays, APR appeared significantly more potent than AMI. Further, APR monotherapy was efficacious in a mouse chronic TB lung infection model (~0.92 log10 CFU/lung reduction). APR combination therapy with the current 1st line standard of care (SoC) antibiotic combination of isoniazid (H), rifampicin (R), ethambutol (E), and pyrazinamide (Z) was found to be additive (HREZ=1.88 vs. HREZ-APR=2.78 log10CFU/lung reduction).DiscussionThe results indicate the potential of apramycin-based combinations for the treatment of human tuberculosis.

Abstract Antimicrobial resistance is a global health threat that requires the development of new treatment concepts. These should not only overcome existing resistance but be designed to slow down the emergence of new resistance mechanisms. Targeted protein degradation, whereby a drug redirects cellular proteolytic machinery towards degrading a specific target, is an emerging concept in drug discovery. We are extending this concept by developing proteolysis targeting chimeras active in bacteria (BacPROTACs) that bind to ClpC1, a component of the mycobacterial protein degradation machinery. The anti-Mycobacterium tuberculosis (Mtb) BacPROTACs are derived from cyclomarins which, when dimerized, generate compounds that recruit and degrade ClpC1. The resulting Homo-BacPROTACs reduce levels of endogenous ClpC1 in Mycobacterium smegmatis and display minimum inhibitory concentrations in the low micro- to nanomolar range in mycobacterial strains, including multiple drug-resistant Mtb isolates. The compounds also kill Mtb residing in macrophages. Thus, Homo-BacPROTACs that degrade ClpC1 represent a different strategy for targeting Mtb and overcoming drug resistance.

India is the habitat of many freshwater fish species that make a significant contribution to endemism. However, the native species population is declining at a very fast rate due to pollution, habitat loss or degradation, new species introduction, hydrologic alterations, and overfishing. The minor carp, Labeo gonius, is one of the important fish species in the Indian river systems and holds enormous potential in culture fisheries. Therefore, the precise status of population structure is a prerequisite for the management of the species. In the present study, we assessed the potential of mtDNA ATPase 6/8 and D-loop regions as markers to determine the genetic diversity and phylogeography of L. gonius from Indian rivers. We have selected five major Indian rivers for investigation which could represent geographically distant populations of the studied fish. A total of 210 ATPase 6/8 sequences (842 bp) and 203 D-loop region sequences (683 bp) of L. gonius from the five major Indian rivers were submitted to NCBI. Results revealed that ATPase 6/8 has 17 haplotypes and 70 polymorphic sites with an overall haplotype diversity of 0.560 ± 0.036 and nucleotide diversity of 0.00347 ± 0.00. Furthermore, D-loop analysis revealed 58 haplotypes, 67 polymorphic sites, 0.954 ± 0.006 overall haplotype diversity, and 0.01914 ± 0.00 nucleotide diversity. The hierarchical AMOVA analysis indicated that a high proportion of the genetic variation (76.46% and 70.22% for the D-loop and ATPase 6/8 regions, respectively) was attributed to among-population variation under gene pool-1 (considering all the stocks from Ganga, Brahmaputra, Teesta, Narmada, and Mahanadi as a single pool). Furthermore, in gene pool-2, samples were divided into two groups: group 1 (Himalayan rivers)—Ganga, Brahmaputra, and Teesta; and group 2 (peninsular rivers)—Narmada and Mahanadi. Genetic variations in the D-loop and ATPase 6/8 regions were found among populations within groups at 70.65% and 53.48%, respectively. Haplotype network analysis revealed that there were significant differences in distributions and frequencies. The observed unique haplotypes might be useful for differentiating the stocks. In brief, the present study suggested that the D-loop and ATPase 6/8 regions are potential markers and represent a significant contribution to the genetic characterization of the minor carp L. gonius from major Indian rivers. Moreover, the L. gonius population along the east coast especially in the Himalayan rivers (Ganga, Brahmaputra, and Teesta river basin) is found to be genetically more diverse. The results would provide a scientific basis for the assessment of fisheries resources and management of this important species.

Recycling, reusing, and reducing electronic garbage (E-waste) may be the sole methods for managing E-waste in use today. In essence, there is no ideal method of managing E-waste. This process required more labor and resources. E-waste classification is fulfilled using fractional Henry gas optimization-based deep convolutional neural network (FHGO-based deep CNN) is shown in this manuscript. To predict the optimal path the E-waste images are conversed through the energy-aware FHGO routing algorithm. The feature extraction procedure is performed to cut out the features for example the gray level co-occurrence matrix (GLCM) feature, local Gabor binary pattern (LGBP) and histogram of oriented gradient (HOG) and the pre-processing phase is concluded with a median filter. To supplement the extracted feature size the data augmentation is fulfilled. Moreover, the E-waste classification is done based on deep CNN, which is trained using a FHGO algorithm. FHGO is exhibited by the merging of Henry gas solubility optimization (HGSO) algorithm, fractional calculus (FC). Comparing to the existing approaches like deep learning, tensor flow deep learning, Cuckoo search-based neural network and machine learning the accuracy of the proposed method is 19.49%, 18.05%, 12.77%, and 7.89% privileged. [ABSTRACT FROM AUTHOR]

At present, engineered cementitious composites (ECC) are rapidly replacing ordinary concrete in the building production due to its superior durability and mechanical qualities. Enormous researches have been done on ECC incorporating various fibers, industrial wastes, supplementary cementitious materials and super plasticizers. As far as fiber is concerned, poly vinyl alcohol fiber was widely explored. In this study, attempt has been made to involve alkali resistant glass fiber as a replacement for poly vinyl alcohol fiber. In addition, the high rate related with ECC as a result of the utilization of micro‐sized silica sand has restricted its extensive pertinences. Thus, this study recommends utilization of quarry dust to replace fine silica sand in the ECC at amounts ranging from 0% to 100% in order to establish a green, sustainable and cost‐effective ECC. The flowability and mechanical demeanor of the ECC mixtures in terms of the tensile, compressive and flexural properties were evaluated. It is arrived that 2% of volume fraction would be the optimum dosage by which better mechanical properties can be achieved. As a result of the present research findings, it is found that the quarry dust could be a better substitute for silica sand up to 75% without any compromise in compressive and flexural strengths. To attain higher tensile qualities, however, it might not be appropriate to replace more than 50% of the silica sand with quarry dust. Cost analysis of the ECC mixtures show that using quarry dust as aggregate is both cost‐effective and sustainable. [ABSTRACT FROM AUTHOR]

Vehicular Ad-Hoc Networks (VANETs) represent a crucial component of intelligent transportation systems (ITS), enabling vehicles to communicate with each other and with roadside infrastructure. Predicting traffic congestion in VANETs is essential for enhancing road safety, optimizing traffic flow, and improving overall transportation efficiency. Traditional machine learning methods have shown promise in this domain; however, they often fall short in handling the complex, high-dimensional data typical of VANETs. To address these challenges, this study employs Extreme Deep Learning Machines (EDRLM), an advanced deep learning technique, for traffic congestion prediction. The EDRLM framework leverages the strengths of deep neural networks and extreme learning machines, offering a robust and scalable solution for processing the dynamic and heterogeneous data in VANETs. By integrating feature extraction, selection, and prediction into a unified model, EDRLM can capture intricate patterns and temporal dependencies within traffic data. The proposed model is trained and validated using realworld VANET datasets, incorporating various traffic parameters such as vehicle speed, density, and inter-vehicular distances. Our experimental results demonstrate that EDRLM outperforms conventional machine learning algorithms in terms of prediction accuracy, computational efficiency, and robustness to noise and missing data. The model's ability to provide timely and precise congestion predictions can facilitate proactive traffic management strategies, including dynamic routing and adaptive traffic signal control, ultimately leading to reduced travel times and enhanced road safety. This study underscores the potential of EDRLM in transforming traffic management in VANETs, paving the way for more intelligent and adaptive ITS solutions. Future research directions include exploring hybrid models combining EDRLM with other advanced machine learning techniques and expanding the framework to accommodate emerging vehicular communication technologies such as 5G and Internet of Things (IoT) devices. [ABSTRACT FROM AUTHOR]

Abstract Background Vivax malaria is associated with significant morbidity and economic loss, and constitutes the bulk of malaria cases in large parts of Asia and South America as well as recent case reports in Africa. The widespread prevalence of vivax is a challenge to global malaria elimination programmes. Vivax malaria control is particularly challenged by existence of dormant liver stage forms that are difficult to treat and are responsible for multiple relapses, growing drug resistance to the asexual blood stages and host-genetic factors that preclude use of specific drugs like primaquine capable of targeting Plasmodium vivax liver stages. Despite an obligatory liver-stage in the Plasmodium life cycle, both the difficulty in obtaining P. vivax sporozoites and the limited availability of robust host cell models permissive to P. vivax infection are responsible for the limited knowledge of hypnozoite formation biology and relapse mechanisms, as well as the limited capability to do drug screening. Although India accounts for about half of vivax malaria cases world-wide, very little is known about the vivax liver stage forms in the context of Indian clinical isolates. Methods To address this, methods were established to obtain infective P. vivax sporozoites from an endemic region in India and multiple assay platforms set up to detect and characterize vivax liver stage forms. Different hepatoma cell lines, including the widely used HCO4 cells, primary human hepatocytes as well as hepatocytes obtained from iPSC’s generated from vivax patients and healthy donors were tested for infectivity with P. vivax sporozoites. Results Both large and small forms of vivax liver stage are detected in these assays, although the infectivity obtained in these platforms are low. Conclusions This study provides a proof of concept for detecting liver stage P. vivax and provide the first characterization of P. vivax liver stage forms from an endemic region in India.