Recent developments on the removal of zinc from electric arc furnace dust by using microwave heating.

Electric arc furnace dust (EAFD) is a hazardous by-product of steel production. As global steel output increases, substantial amounts of EAFD are produced, which causes significant environmental issues. EAFD contains quantities of Fe and Zn, which could be reused as raw materials in the steelmaking process. However, zinc oxides can be reduced and vaporized during this process, forming zinc vapor that contaminates equipment surfaces and causes damage. Consequently, various pyrometallurgical methods have been proposed for zinc removal from EAFD. Due to the extensive usage of carbonaceous materials, these methods contribute to significant CO2, raising concerns about greenhouse gas emissions. Microwave heating offers an efficient, energy-saving, and environmentally friendly alternative to pyrometallurgical approaches. EAFD can generate heat under microwave irradiation without carbon addition, which means the CO2 emissions can be reduced by replacing the reductant in the microwave heating process. Furthermore, microwaves enhance zinc removal reactions to a certain extent, resulting in higher efficiency. Thus, employing microwave heating for EAFD processing has significant potential for future development. This paper reviews recent research on using microwave heating for zinc removal from EAFD, focusing on the heating behavior of EAFD in microwaves and the mechanisms of zinc removal. This review will be crucial for researchers working on processing EAFD using microwave heating and could help guide the development of more sustainable and efficient methods.

In Silico, In Vitro, and In Vivo Evaluation of Caffeine-Coated Nanoparticles as a Promising Therapeutic Avenue for AML through NF-Kappa B and TRAIL Pathways Modulation.

BACKGROUND: Advancements in nanoscience have led to a profound paradigm shift in the therapeutic applications of medicinally important natural drugs. The goal of this research is to develop a nano-natural product for efficient cancer treatment. METHODS AND RESULTS: For this purpose, mesoporous silica nanoparticles (MSNPs) were formulated, characterized, and loaded with caffeine to develop a targeted drug delivery system, i.e., caffeine-coated nanoparticles (CcNPs). In silico docking studies were conducted to examine the binding efficiency of the CcNPs with different apoptotic targets followed by in vitro and in vivo bioassays in respective animal models. Caffeine, administered both as a free drug and in nanomedicine form, along with doxorubicin, was delivered intravenously to a benzene-induced AML model. The anti-leukemic potential was assessed through hematological profiling, enzymatic biomarker analysis, and RT-PCR examination of genetic alterations in leukemia markers. Docking studies show strong inter-molecular interactions between CcNPs and apoptotic markers. In vitro analysis exhibits statistically significant antioxidant activity, whereas in vivo analysis exhibits normalization of the genetic expression of leukemia biomarkers STMN1 and S1009A, accompanied by the restoration of the hematological and morphological traits of leukemic blood cells in nanomedicine-treated rats. Likewise, a substantial improvement in hepatic and renal biomarkers is also observed. In addition to these findings, the nanomedicine successfully normalizes the elevated expression of GAPDH and mTOR induced by exposure to benzene. Further, the nanomedicine downregulates pro-survival components of the NF-kappa B pathway and upregulated P53 expression. Additionally, in the TRAIL pathway, it enhances the expression of pro-apoptotic players TRAIL and DR5 and downregulates the anti-apoptotic protein cFLIP. CONCLUSIONS: Our data suggest that MSNPs loaded with caffeine, i.e., CcNP/nanomedicine, can potentially inhibit transformed cell proliferation and induce pro-apoptotic TRAIL machinery to counter benzene-induced leukemia. These results render our nanomedicine as a potentially excellent therapeutic agent against AML.

A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction.

Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.

Deep learning-based analysis of COVID-19 X-ray images: Incorporating clinical significance and assessing misinterpretation.

COVID-19, pneumonia, and tuberculosis have had a significant effect on recent global health. Since 2019, COVID-19 has been a major factor underlying the increase in respiratory-related terminal illness. Early-stage interpretation and identification of these diseases from X-ray images is essential to aid medical specialists in diagnosis. In this study, (COV-X-net19) a convolutional neural network model is developed and customized with a soft attention mechanism to classify lung diseases into four classes: normal, COVID-19, pneumonia, and tuberculosis using chest X-ray images. Image preprocessing is carried out by adjusting optimal parameters to preprocess the images before undertaking training of the classification models. Moreover, the proposed model is optimized by experimenting with different architectural structures and hyperparameters to further boost performance. The performance of the proposed model is compared with eight state-of-the-art transfer learning models for a comparative evaluation. Results suggest that the COV-X-net19 outperforms other models with a testing accuracy of 95.19%, precision of 96.49% and F1-score of 95.13%. Another novel approach of this study is to find out the probable reason behind image misclassification by analyzing the handcrafted imaging features with statistical evaluation. A statistical analysis known as analysis of variance test is performed, to identify at which point the model can identify a class accurately, and at which point the model cannot identify the class. The potential features responsible for the misclassification are also found. Moreover, Random Forest Feature importance technique and Minimum Redundancy Maximum Relevance technique are also explored. The methods and findings of this study can benefit in the clinical perspective in early detection and enable a better understanding of the cause of misclassification.

A Novel Feature-Selection Algorithm in IoT Networks for Intrusion Detection.

The Internet of Things (IoT) and network-enabled smart devices are crucial to the digitally interconnected society of the present day. However, the increased reliance on IoT devices increases their susceptibility to malicious activities within network traffic, posing significant challenges to cybersecurity. As a result, both system administrators and end users are negatively affected by these malevolent behaviours. Intrusion-detection systems (IDSs) are commonly deployed as a cyber attack defence mechanism to mitigate such risks. IDS plays a crucial role in identifying and preventing cyber hazards within IoT networks. However, the development of an efficient and rapid IDS system for the detection of cyber attacks remains a challenging area of research. Moreover, IDS datasets contain multiple features, so the implementation of feature selection (FS) is required to design an effective and timely IDS. The FS procedure seeks to eliminate irrelevant and redundant features from large IDS datasets, thereby improving the intrusion-detection system's overall performance. In this paper, we propose a hybrid wrapper-based feature-selection algorithm that is based on the concepts of the Cellular Automata (CA) engine and Tabu Search (TS)-based aspiration criteria. We used a Random Forest (RF) ensemble learning classifier to evaluate the fitness of the selected features. The proposed algorithm, CAT-S, was tested on the TON_IoT dataset. The simulation results demonstrate that the proposed algorithm, CAT-S, enhances classification accuracy while simultaneously reducing the number of features and the false positive rate.

A Computer-Aided Diagnostic System to Identify Diabetic Retinopathy, Utilizing a Modified Compact Convolutional Transformer and Low-Resolution Images to Reduce Computation Time.

Diabetic retinopathy (DR) is the foremost cause of blindness in people with diabetes worldwide, and early diagnosis is essential for effective treatment. Unfortunately, the present DR screening method requires the skill of ophthalmologists and is time-consuming. In this study, we present an automated system for DR severity classification employing the fine-tuned Compact Convolutional Transformer (CCT) model to overcome these issues. We assembled five datasets to generate a more extensive dataset containing 53,185 raw images. Various image pre-processing techniques and 12 types of augmentation procedures were applied to improve image quality and create a massive dataset. A new DR-CCTNet model is proposed. It is a modification of the original CCT model to address training time concerns and work with a large amount of data. Our proposed model delivers excellent accuracy even with low-pixel images and still has strong performance with fewer images, indicating that the model is robust. We compare our model's performance with transfer learning models such as VGG19, VGG16, MobileNetV2, and ResNet50. The test accuracy of the VGG19, ResNet50, VGG16, and MobileNetV2 were, respectively, 72.88%, 76.67%, 73.22%, and 71.98%. Our proposed DR-CCTNet model to classify DR outperformed all of these with a 90.17% test accuracy. This approach provides a novel and efficient method for the detection of DR, which may lower the burden on ophthalmologists and expedite treatment for patients.

Cancerous Tumor Controlled Treatment Using Search Heuristic (GA)-Based Sliding Mode and Synergetic Controller.

Cancerous tumor cells divide uncontrollably, which results in either tumor or harm to the immune system of the body. Due to the destructive effects of chemotherapy, optimal medications are needed. Therefore, possible treatment methods should be controlled to maintain the constant/continuous dose for affecting the spreading of cancerous tumor cells. Rapid growth of cells is classified into primary and secondary types. In giving a proper response, the immune system plays an important role. This is considered a natural process while fighting against tumors. In recent days, achieving a better method to treat tumors is the prime focus of researchers. Mathematical modeling of tumors uses combined immune, vaccine, and chemotherapies to check performance stability. In this research paper, mathematical modeling is utilized with reference to cancerous tumor growth, the immune system, and normal cells, which are directly affected by the process of chemotherapy. This paper presents novel techniques, which include Bernstein polynomial (BSP) with genetic algorithm (GA), sliding mode controller (SMC), and synergetic control (SC), for giving a possible solution to the cancerous tumor cells (CCs) model. Through GA, random population is generated to evaluate fitness. SMC is used for the continuous exponential dose of chemotherapy to reduce CCs in about forty-five days. In addition, error function consists of five cases that include normal cells (NCs), immune cells (ICs), CCs, and chemotherapy. Furthermore, the drug control process is explained in all the cases. In simulation results, utilizing SC has completely eliminated CCs in nearly five days. The proposed approach reduces CCs as early as possible.

Non-edible plant seeds of Acacia farnesiana as a new and effective source for biofuel production.

Currently, the energy crisis is a hot topic for researchers because we are facing serious problems due to overpopulation and natural energy sources are vanishing day-by-day. To overcome the energy crisis, biofuel production from non-edible plant seeds is the best solution for the present era. In the present study, we select the non-edible seeds of Acacia farnesiana for biofuel production from different areas of Pakistan with better oil production results. Different kinds of analytical method, like the American Standard for Testing and Materials and techniques like Fourier transform infra-red spectroscopy, nuclear magnetic resonance spectroscopy, gas chromatography, and inductively coupled plasma optical emission spectrometry, were used to evaluate the chemical compositions. The maximum oil extraction rate (23%) was produced by petroleum ether. Potassium hydroxide exhibited the best conversion result of 96% fatty acid methyl ester. The transesterification method was used for the preparation of fatty acid methyl ester (96%) using potassium hydroxide and methanol. The viscosity and density of Acacia farnesiana seed oil biodiesel was comparable to American Standard for Testing Material biodiesel standards. By using gas chromatography-mass spectrometry, five fatty acids were detected comprising palmitic acid (6.85%), stearic acid (2.36%), oleic acid (12.13%), linoleic acid (46.85%), and α-linolenic acid (1.23%). This study concludes that Acacia farnesiana seed oil biodiesel could be an intriguing raw material for yielding Acacia farnesiana seed oil methyl ester as an alternative fuel source.

In vitro efficacy of polymer coated miltefosine drug against leishmania tropica.

Polymer based nanoparticles for drug delivery is an alternative approach to overcome drug resistance and drug toxicity especially for cutaneous leishmaniasis treatment. The present study shows synthesis and characterization of Miltefosine loaded chitosan nanoparticles (MFS-CNPs). The synthesized MFS-CNPs were experimented to evaluate the in vitro cytotoxicity and efficacy of the synthesized drug loaded nanoparticles by hemolysis assay and 3-(4, 5- dimethylthiazol-2-yl)-2,5-diphenyletetrazolium bromide (MTT) assay. MFS-CNPs were synthesized by ionic gelation method with sodium tripolyphosphate. The characterization of synthesized NPs was performed to observe the surface morphology, encapsulation efficacy, drug loading content, average size, and zeta potential. In vitro MTT assay was performed to calculate half maximal inhibitory concentration value of synthesized nanoparticles against promastigotes and axenic amastigotes of L. tropica. By using Scanning electron microscope, MFS-CNPs displayed spherical shape having a mean size of 70 nm along with high EE (97%), DLC (91%) and negative surface charge (- 28.0 mV). Dynamic light scattering shows the average size of NPs was 91.4 nm. Moreover, less than 5% hemolytic activity was observed in MFS-CNPs as compared to free MFS in different concentrations (100 µg/ml, 125 µg/ml, 150 µg/ml).It was observed that the effect of MFS-CNPs and free MFS on both forms of the parasite was dose and time dependent. However, the cytotoxic effects of MFS-CNPs were more salient than free MFS on both forms of L. tropica. Using MTT assay, free MFS presented low efficacy at higher concentrations (30 µg/ml) with 21.4 ± 1.3 and 20.5 ± 1.4 mean viability rate of the promastigotes and axenic amastigotes, respectively after 72 h incubation. While MFS-CNPs showed strong antileishmanial effects on both forms of L. tropica (11 ± 0.3 and 14 ± 0.8) mean viability rate after 72 h incubation at (30 µg/ml). When analyzed statistically by the software, Graph Pad Prism version 5, the IC50 value of MFS-CNPs (0.0218 ± 0.01 µg/ml) against promastigotes was effective than free MFS (0.3548 ± 0.17 µg/ml). Similarly, MFS-CNPs activity against axenic amastigotes (0.1008 ± 0.02 µg/ml) was potent than free MFS (0.5320 ± 0.21 µg/ml). Hence, MFS-CNPs exhibited significant antileishmanial activity in vitro. In conclusion, MFS-CNPs manifested enhanced in vitro Leishmanicidal and less hemolytic activity; however more studies are needed to support its efficacy in both animal and human cutaneous leishmaniasis.

Optimization of Single α-Phase for Promoting Ferromagnetic Properties of 44Fe-28Cr-22Co-3Mo-1Ti-2V Permanent Magnet with Varying Co Concentration for Energy Storage.

The thermal stability and structural, microstructural and magnetic properties of (40 + x) Fe-28Cr-(26 - x) Co-3Mo-1Ti-2V magnets with x = 0, 2, 4 addition in cobalt content were investigated and presented. The magnetic alloys were synthesized by vacuum arc melting and casting technique in a controlled argon atmosphere. Magnetic properties in the alloys were convinced by single-step isothermal field treatment and subsequent aging. The alloys were investigated for thermal stability, structural, microstructural and magnetic properties via differential thermal analysis (DTA), X-ray diffractometery (XRD), optical microscopy (OM), field emission scanning electron microscope (FESEM) and DC magnetometer. Metallurgical grains of size 300 ± 10 µm were produced in the specimens by casting and refined by subsequent thermal treatments. The magnetic properties of the alloys were achieved by refining the microstructure, the optimization of conventional thermomagnetic treatment to modified single-step isothermal field treatment and subsequent aging. The best magnetic properties achieved for the alloy 44Fe-28Cr-22Co-3Mo-0.9Ti-2V was coercivity Hc = 890 Oe (71 kA/m), Br = 8.43 kG (843 mT) and maximum energy product (BH)max = 3 MGOe (24 kJ/m3). The enhancement of remanence and coercivity enabled by the isothermal field treatment was due to the elongation of the ferromagnetic phase and step aging treatment due to the increase in the volume fraction. This work is interesting for spin-based electronics to be used for energy storage devices.

Noviherbaspirillum aridicola sp. nov., isolated from an arid soil in Pakistan.

Strain NCCP-691T was isolated from a soil sample collected from an arid soil in Karak, Khyber Pakhtunkhwa, Pakistan. Phenotypically, the cells were Gram-stain-negative, aerobic and motile rods. The organism was able to grow between 20-40 °C (optimum at 30-37 °C), at pH 5.5-8.0 (optimum at pH 7.0-7.2) and tolerated 0-1.5% NaCl (w/v) (optimum at 0-0.5). Based on 16S rRNA gene sequences, strain NCCP-691T formed a distinct phylogenetic clade with Noviherbaspirillum arenae, N. agri, N. denitrificans and N. autotrophicum (having sequence similarities of 99.0; 98.1; 98.0 and 97.7% respectively). Phylogenetic analyses based on the whole genome sequences confirmed that strain NCCP-691T should be affiliated to the genus Noviherbaspirillum. The average nucleotide identity values compared to other species of Noviherbaspirillum were below 95-96 % and digital DNA-DNA hybridization values were less than 70 %. Chemotaxonomic analyses showed that the strain had ubiquinone-8, as the only respiratory quinine. The major cellular fatty acids were summed feature 3 (C16 : 1 ω 7 c/C16 : 1 ω 6 c, 35.9 %), summed feature 8 (C18 : 1 ω 7 c/C18 : 1 ω 6 c, 26.9 %) and C16 : 0 (22.9 %) and the polar lipid profile was composed of diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine and phosphatidylcholine. The genomic DNA G+C content was 65.5 mol% (from draft genome). Genome analyses showed that strain NCCP-691T had terpene and arylpolyene biosynthetic genes clusters and genes related to resistance against heavy metals. Based on phylogenetic analyses, phenotypic features and genomic comparison, it is proposed that strain NCCP-691T is a novel species of the genus Noviherbaspirillum and the name Noviherbaspirillum aridicola sp. nov. is proposed. Type strain is NCCP-691T (=KCTC 52721T=CGMCC 1.13600T).

A review of COVID-19: Treatment strategies and CRISPR/Cas9 gene editing technology approaches to the coronavirus disease.

The new coronavirus SARS-CoV-2 pandemic has put the world on lockdown for the first time in decades. This has wreaked havoc on the global economy, put additional burden on local and global public health resources, and, most importantly, jeopardised human health. CRISPR stands for Clustered Regularly Interspaced Short Palindromic Repeats, and the CRISPR associated (Cas) protein (CRISPR/Cas) was identified to have structures in E. coli. The most modern of these systems is CRISPR/Cas. Editing the genomes of plants and animals took several years and cost hundreds of thousands of dollars until the CRISPR approach was discovered in 2012. As a result, CRISPR/Cas has piqued the scientific community's attention, particularly for disease diagnosis and treatment, because it is faster, less expensive, and more precise than previous genome editing technologies. Data from gene mutations in specific patients gathered using CRISPR/Cas can aid in the identification of the best treatment strategy for each patient, as well as other research domains such as coronavirus replication in cell culture, such as SARS-CoV2. The implications of the most prevalent driver mutations, on the other hand, are often unknown, making treatment interpretation difficult. For detecting a wide range of target genes, the CRISPR/Cas categories provide highly sensitive and selective tools. Genome-wide association studies are a relatively new strategy to discovering genes involved in human disease when it comes to the next steps in genomic research. Furthermore, CRISPR/Cas provides a method for modifying non-coding portions of the genome, which will help advance whole genome libraries by speeding up the analysis of these poorly defined parts of the genome.

The anti-tumor effects of the combination of microwave hyperthermia and lobaplatin against breast cancer cells in vitro and in vivo.

BACKGROUND: Breast cancer is the main lethal disease among females. The combination of lobaplatin and microwave hyperthermia plays a crucial role in several kinds of cancer in the clinic, but its possible mechanism in breast cancer has remained indistinct. METHODS: Mouse models were used to detect breast cancer progression. Cell growth was explored with MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulphonyl)-2H-tetrazolium) and colony formation assays. Cell migration and invasion were investigated with a transwell assay. Cell apoptosis was probed with flow cytometry. The expression of apoptosis-associated proteins was examined with Western blots. RESULT: Combination treatment decreased breast cancer cell viability, colony formation, cell invasion and metastasis. In addition, the treatment-induced breast cancer cell apoptosis and autophagy, activated the c-Jun N-terminal kinase (JNK) signaling pathway, suppressed the protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling pathway, and down-regulated IAP and Bcl-2 family protein expression. CONCLUSION: These results indicate that lobaplatin is an effective breast cancer anti-tumor agent. Microwave hyperthermia was a useful adjunctive treatment. Combination treatment was more efficient than any single therapy. The possible mechanism for this effect was mainly associated with activation of the JNK signaling pathway, inactivation of the AKT/mTOR signaling pathway and down-regulation of the Bcl-2 and IAP families.

Monitoring System-Based Flying IoT in Public Health and Sports Using Ant-Enabled Energy-Aware Routing.

In recent decades, the Internet of flying networks has made significant progress. Several aerial vehicles communicate with one another to form flying ad hoc networks. Unmanned aerial vehicles perform a wide range of tasks that make life easier for humans. However, due to the high frequency of mobile flying vehicles, network problems such as packet loss, latency, and perhaps disrupted channel links arise, affecting data delivery. The use of UAV-enabled IoT in sports has changed the dynamics of tracking and working on player safety. WBAN can be merged with aerial vehicles to collect data regarding health and transfer it to a base station. Furthermore, the unbalanced energy usage of flying things will result in earlier mission failure and a rapid decline in network lifespan. This study describes the use of each UAV's residual energy level to ensure a high level of safety using an ant-based routing technique called AntHocNet. In health care, the use of IoT-assisted aerial vehicles would increase operational performance, surveillance, and automation optimization to provide a smart application of flying IoT. Apart from that, aerial vehicles can be used in remote communication for treatment, medical equipment distribution, and telementoring. While comparing routing algorithms, simulation findings indicate that the proposed ant-based routing protocol is optimal.

Zafaria cholistanensis gen. nov. sp. nov., a moderately thermotolerant and halotolerant actinobacterium isolated from Cholistan desert soil of Pakistan.

A Gram-staining positive, non-spore forming, non-pigmented and non-motile bacterium, designated strain NCCP-1664T, was isolated from Cholistan desert, Pakistan. Cells of strain NCCP-1664T were strictly aerobic, catalase positive and oxidase negative with a rod to coccus growth cycle and can grow at pH 6.0-9.0 (optimum pH 7-8) at 28-45 °C (optimum 37 °C) and could tolerate 0-16% NaCl (optimum 2%). Phylogenetic analyses based on 16S rRNA gene sequence revealed that strain NCCP-1664T belongs to the family Micrococcaceae and was related to members of the genus Arthrobacter having highest sequence similarities with Arthrobacter ginkgonis (98.9%), A. halodurans (97.7%) and A. oryzae (97.1%) and less than 97% with other related taxa. DNA-DNA relatedness values of strain NCCP-1664T with above mentioned type strains were found to be less than 54%, whereas digital DDH and average nucleotide identity (ANI) values with A. oryzae were 20.9 and of 74.3%, respectively. DNA G + C content of strain NCCP-1664T was 70.0 mol%. Chemotaxonomic data of strain NCCP-1664T showed the peptidoglycan type as A3α L-Lys-L -Ala; menaquinones as MK-9(H2) (67%), MK-8(H2) (32%) and MK-7(H2) (1%), major fatty acids as anteiso -C15:0 (51.2%), anteiso-C17:0 (9.6%) and C18:1ω9c (6.9%) and polar lipids profile comprising of diphosphatidylglycerol, phosphatidylglycerol, phosphatidylinositol, digalactosyldiacylglycerol, small amounts of monogalactosyldiacylglycerol, trimannosyldiacylglycerol and three unidentified lipids. The phylogenomic analyses along with chemotaxonomic data, physiological, biochemical characteristics allowed to describe it as representative of a novel genus, for which the name Zafaria cholistanensis gen. nov. sp. nov. is proposed with the type strain NCCP-1664T (= DSM 29936T = KCTC 39549T).

Fever-range hyperthermia promotes cGAS-STING pathway and synergizes DMXAA-induced antiviral immunity.

Background: Fever-range hyperthermia or fever-range temperature (hereafter FRT) improves survival and shortens disease duration in microbial infections. However, the mechanisms of these beneficial effects still remain elusive. We hypothesized that FRT might enhance cell responsiveness to infections by promoting cGAS-STING signaling to cause enhanced production of IFN-ß. Objective: To investigate the effect fever-range hyperthermia on cGAS-STING pathway. Methods: RAW 264.7 and cGAS-/- RAW 264.7 cells, stimulated with 5µg/ml herring testis DNA (htDNA), were heated to 39.5°C and analyzed for the expression of cGAS, STING, IFN-ß, and the synthesis of cGAMP and IRF3 phosphorylation. In vivo, wild type C57BL/6J mice were subjected to whole body hyperthermia (WBH) at 39.5°C. The mice were then challenged with influenza virus and analyzed for antiviral response in term of IFN-ß expression, body weight and survival. Results: We found that 39.5°C FRT upregulated the expression of cGAS and STING, and induced the synthesis of cGAMP and production of IFN-ß in htDNA-transfected RAW 264.7 cells more potently as compared to 37°C. Moreover, FRT+DMXAA-treated cells were better protected from vesicular stomatitis virus (VSV)-induced cytotoxicity in vitro in contrast to the nonprotected control (no FRT and DMXAA) or DMXAA treatment alone. In vivo, FRT at 39.5°C, co-administered with DMXAA, significantly induced the expression of IFN-ß, showed reduced weight loss mice and exhibited 25% more survival over the course of 14 days as compared to DMXAA treated mice 37°C. Conclusion: We conclude that fever-range hyperthermia promotes cGAS-STING pathway to cause increased expression of IFN-ß and mediate its antiviral effects.

Antimicrobial and antioxidant chlorinated azaphilones from mangrove Diaporthe perseae sp. isolated from the stem of Chinese mangrove Pongamia pinnata.

Many plants remain unexplored for their endophytic fungi that may possess potentially important phytochemicals. Consequently, we have focused to discover new natural products from endophytic fungus Diaporthe perseae sp. isolated from the stem of the Chinese mangrove Pongamia pinnata (L.) Pierre plant that led to the isolation of one new chlorinated isochromophilone G (1) along with six known azaphilones (2-7). The structures of the isolated compounds were elucidated by UV, NMR and Mass spectroscopic analysis. All the isolated compounds were screened for their antimicrobial and anti-oxidant activities.

Insights on comparative bacterial diversity between different arid zones of Cholistan Desert, Pakistan.

The present study was conducted to analyze bacterial diversity profile of Cholistan desert located in Pakistan. The study investigates the influence of physicochemical parameters of soil on distribution of different bacteria at all taxonomic levels and also study the distribution pattern between different desert environments, particularly rhizospheric and bulk desert sands. Species richness showed phyla Proteobacteria and Chloroflexi as the dominant OTUs in all the samples. Besides the two phyla, the rhizospheric soils with root remnants were dominated by Firmicutes, Deinococcus-Thermus, Actinobacteria and Acidobacteri, while phylum Thermotogae was present in significant quantity in rhizosheaths devoid of roots. In non-rhizospheric desert soils, a considerable number of OTUs belonged to phyla Proteobacteria, Chloroflexi, Bacteroidetes and Acidobacteria. An important finding from this study is that a bulk portion of the OTUs were assigned to unclassified taxa, indicating a large repertoire of unexplored taxa in the desert ecology of Pakistan. Distribution of taxonomic groups among various regions of the desert was collaborating well with the physicochemical parameters of the sites. The findings of this study establish the fundamental relationships between desert ecosystem, specific native plant and the total bacterial flora. This is the first study of microbial community analysis of any desert in Pakistan and thus, will serve as a future platform to explore further on desert ecosystem functioning by employing the ever-changing biotechnological tools.

Marmoricola caldifontis sp. nov., a novel actinobacterium isolated from a hot spring.

A Gram-staining-positive, aerobic, non-motile, non-spore-forming and coccoid-shaped actinobacterial strain, designated YIM 730233T, was isolated from a sediment sample, collected from a hot spring in Tibet, China. Colonies were brownish, circular, smooth and convex. Strain YIM 730233T was able to grow in the temperature range of 20-50 °C, pH 6.5-8.0 and in the presence of up to 1.0 % (w/v) NaCl. A comparison of the 16S rRNA gene sequence of strain YIM 730233T with sequences of type strains of most closely related species of Marmoricola showed highest sequence similarities to Marmoricola bigeumensis MSL-05T (98.3%) and Marmoricola pocheonensis Gsoil 818T (98.1%). The draft genome of strain YIM 730233T had a size of 4 806 234 bp with a DNA G+C content of 72.1 mol%. The major fatty acids (>10 %) of strain YIM 730233T mainly consisted of iso-C16 : 0, anteiso-C17 : 0 and C18 : 1 ω9c, typical of the genus Marmoricola. Strain YIM 730233T had LL-2,6-diaminopimelic acid as the diagnostic diamino acid in the cell wall. The predominant isoprenoid quinone was MK-8(H4). The polar lipids were diphosphatidylglycerol, phosphatidylglycerol, unidentified phospholipids and an unidentified lipid. DNA-DNA hybridizations between strain YIM 730233T and type strains of Marmoricola bigeumensis MSL-05T and Marmoricola pocheonensis Gsoil 818T resulted in similarity values of 21 and 19% respectively. Based on DNA-DNA hybridization results, together with the differentiating biochemical and chemotaxonomic features, showed that strain YIM 730233T represents a novel Marmoricola species, for which the name Marmoricola caldifontis sp. nov. (type strain YIM 730233T=KCTC 49192T=CGMCC 4.7521T), is proposed.