Clinical and genetic investigation of 14 families with various forms of short stature syndromes.

Skeletal dysplasias are a heterogeneous group of disorders presenting mild to lethal defects. Several factors, such as genetic, prenatal, and postnatal environmental may contribute to reduced growth. Fourteen families of Pakistani origin, presenting the syndromic form of short stature either in the autosomal recessive or autosomal dominant manner were clinically and genetically investigated to uncover the underlying genetic etiology. Homozygosity mapping, whole exome sequencing, and Sanger sequencing were used to search for the disease-causing gene variants. In total, we have identified 13 sequence variants in 10 different genes. The variants in the HSPG2 and XRCC4 genes were not reported previously in the Pakistani population. This study will expand the mutation spectrum of the identified genes and will help in improved diagnosis of the syndromic form of short stature in the local population.

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.

Effect of Freeze-Thaw Cycles on the Shear Strength of Root-Soil Composite.

A large alpine meadow in a seasonal permafrost zone exists in the west of Sichuan, which belongs to a part of the Qinghai-Tibet Plateau, China. Due to the extreme climates and repeated freeze-thaw cycling, resulting in a diminishment in soil shear strength, disasters occur frequently. Plant roots increase the complexity of the soil freeze-thaw strength problem. This study applied the freeze-thaw cycle and direct shear tests to investigate the change in the shear strength of root-soil composite under freeze-thaw cycles. This study examined how freeze-thaw cycles and initial moisture content affect the shear strength of two sorts of soil: uncovered soil and root-soil composite. By analyzing the test information, the analysts created numerical conditions to foresee the shear quality of both sorts of soil under shifting freeze-thaw times and starting moisture levels. The results showed that: (1) Compared to the bare soil, the root-soil composite was less affected by freeze-thaw cycles in the early stage, and the shear strength of both sorts of soil was stabilized after 3-5 freeze-thaw cycles. (2) The cohesion of bare soil decreased more than that of root-soil composite with increasing moisture content. However, freeze-thaw cycles primarily influence soil cohesion more than the internal friction angle. The cohesion modification leads to changes in shear quality for both uncovered soil and root-soil composite. (3) The fitting equations obtained via experiments were used to simulate direct shear tests. The numerical results are compared with the experimental data. The difference in the soil cohesion and root-soil composite cohesion between the experiment data and the simulated result is 8.2% and 17.2%, respectively, which indicates the feasibility of the fitting equations applied to the numerical simulation of the soil and root-soil composite under the freeze-thaw process. The findings give potential applications on engineering and disaster prevention in alpine regions.

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.

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.

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.

Interactions of urbanisation, climate variability, and infectious disease dynamics: insights from the Coimbatore district of Tamil Nadu.

Climate change and shifts in land use/land cover (LULC) are critical factors affecting the environmental, societal, and health landscapes, notably influencing the spread of infectious diseases. This study delves into the intricate relationships between climate change, LULC alterations, and the prevalence of vector-borne and waterborne diseases in Coimbatore district, Tamil Nadu, India, between 1985 and 2015. The research utilised Landsat-4, Landsat-5, and Landsat-8 data to generate LULC maps, applying the maximum likelihood algorithm to highlight significant transitions over the years. This study revealed that built-up areas have increased by 67%, primarily at the expense of agricultural land, which was reduced by 51%. Temperature and rainfall data were obtained from APHRODITE Water Resources, and with a statistical analysis of the time series data revealed an annual average temperature increase of 1.8 °C and a minor but statistically significant rainfall increase during the study period. Disease data was obtained from multiple national health programmes, revealing an increasing trend in dengue and diarrhoeal diseases over the study period. In particular, dengue cases surged, correlating strongly with the increase in built-up areas and temperature. This research is instrumental for policy decisions in public health, urban planning, and climate change mitigation. Amidst limited research on the interconnections among infectious diseases, climate change, and LULC changes in India, our study serves as a significant precursor for future management strategies in Coimbatore and analogous regions.

A Rare Case of Cytomegalovirus Colitis with Subsequent Non-tubercular Mycobacteria Immune Reconstitution Inflammatory Syndrome.

Cytomegalovirus (CMV) colitis occurs commonly in immunocompromised patients with high mortality. CMV infection has also been reported in immunocompetent individuals and it has a varied clinical presentation. When HIV-infected patients are started on antiretroviral therapy (ART) there is a reconstitution of the immune system which results in the paradoxical worsening of existing conditions or development of new disease conditions known as immune reconstitution inflammatory syndrome (IRIS). In the setting of IRIS one of the most common infections to occur is non-tubercular mycobacteria (NTM). The infection generally develops when the CD4 count is < 50 cells/µL. Here we present a rare case of CMV colitis followed by NTM infection in the setting of IRIS, its management, and treatment outcomes.

Single-Solvent Fractionation and Electro-Spinning Neat Softwood Kraft Lignin.

This paper reports on the production of electro-spun nanofibers from softwood Kraft lignin without the need for polymer blending and/or chemical modification. Commercially available softwood Kraft lignin was fractionated using acetone. The acetone-soluble lignin (AcSL) had an ash content of 0.06 wt %, a weight average molecular weight of 4250 g·mol-1 along with the polydispersity index of 1.73. The corresponding values for as-received lignin (ARL) were 1.20 wt %, 6000 g·mol-1, and 2.22, respectively. The AcS was dissolved in a binary solvent consisting of acetone, and dimethyl sulfoxide (2:1, v/v) was selected for dissolving the AcSL. Conventional and custom-designed grounded electrode configurations were used to produce electro-spun neat lignin fibers that were randomly oriented or highly aligned, respectively. The diameter of the electro-spun fibers ranged from 1.12 to 1.46 µm. After vacuum drying at 140 °C for 6 h to remove the solvents and oxidation at 250 °C, the fibers were carbonized at 1000, 1200, and 1500 °C for 1 h. The carbonized fibers were unfused and void-free with an average diameter of 500 nm. Raman spectroscopy, scanning electron microscopy, and image analysis were used to characterize the carbonized fibers.

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.

Long-Term Use of Proton Pump Inhibitors Disrupts Intestinal Tight Junction Barrier and Exaggerates Experimental Colitis.

BACKGROUND: Proton pump inhibitors [PPIs] are widely used to treat a number of gastro-oesophageal disorders. PPI-induced elevation in intragastric pH may alter gastrointestinal physiology. The tight junctions [TJs] residing at the apical intercellular contacts act as a paracellular barrier. TJ barrier dysfunction is an important pathogenic factor in inflammatory bowel disease [IBD]. Recent studies suggest that PPIs may promote disease flares in IBD patients. The role of PPIs in intestinal permeability is not clear. AIM: The aim of the present study was to study the effect of PPIs on the intestinal TJ barrier function. METHODS: Human intestinal epithelial cell culture and organoid models and mouse IBD models of dextran sodium sulphate [DSS] and spontaneous enterocolitis in IL-10-/- mice were used to study the role of PPIs in intestinal permeability. RESULTS: PPIs increased TJ barrier permeability via an increase in a principal TJ regulator, myosin light chain kinase [MLCK] activity and expression, in a p38 MAPK-dependent manner. The PPI-induced increase in extracellular pH caused MLCK activation via p38 MAPK. Long-term PPI administration in mice exaggerated the increase in intestinal TJ permeability and disease severity in two independent models of DSS colitis and IL-10-/- enterocolitis. The TJ barrier disruption by PPIs was prevented in MLCK-/- mice. Human database studies revealed increased hospitalizations associated with PPI use in IBD patients. CONCLUSIONS: Our results suggest that long-term use of PPIs increases intestinal TJ permeability and exaggerates experimental colitis via an increase in MLCK expression and activity.

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.

Morbidity, mortality, and emerging drug resistance in Device-associated infections (DAIs) in intensive care patients at a 1000-bedded tertiary care teaching hospital.

Background: Device-associated infections (DAIs) such as ventilator associated pneumonia (VAP), central line-associated blood stream infection (CLABSI), and catheter-related urinary tract infection (CAUTI) are principal contributors to health hazard and a major preventable threat to patient safety. Robust surveillance of DAI delineates infections, pathogens, resistograms, and facilitates antimicrobial therapy, infection-control, antimicrobial stewardship, and improvement in quality of care. Methods: This prospective outcome surveillance study was conducted amongst 2067 ICU patients in a 1000-bedded teaching hospital. Clinical, laboratory, and environmental surveillance, as well as screening of health care professionals (HCPs) were conducted using the modified US Centers for Disease Control and Prevention-National Healthcare Safety Network definitions and methods. Morbidity, mortality, and health-care indices were analyzed and two-tier infection prevention and control was promulgated. Results: Mean occupancy was 95.34% for 2061 patients of 7381 patients/bed/ICU days. One hundred seventeen episodes of DAI occurred in 1258 patients of 12,882 device-days with mean device utilization ratio of 1.79. Mean rate of DAI was 7.40 per 1000 device days. Multiresistant Pseudomonas aeruginosa was most commonly followed by Acinetobacter. Mean all-cause mortality in ICU was 24.85%, whereas all-cause mortality after DAI was 9.79%. Methicillin-resistant Staphylococcus aureus prevalence was 38.46% amongst health-care professionals. Conclusion: Mean rates of VAP, CLABSI, and CAUTI were 20.69, 2.53, and 2.23 per 1000 device days comparable with Indian and global ICUs. Resolute conviction and sustained momentum in infection prevention and control is an essential step toward patient safety.

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.