Genomic insights into in-ICU emergence of last-resort antimicrobial resistance in a rare, carbapenem resistant, ST16 Klebsiella pneumoniae strain from Jodhpur, India.

OBJECTIVES: To investigate the genomic differences between two extensively drug resistant, ST16 strains of Klebsiella pneumoniae recovered from patients in the same ICU, one of which was colistin resistant. METHODS: Antimicrobial susceptibilities of the isolates were determined using VITEK-2. Hybrid assemblies for both strains were generated using Oxford Nanopore and Illumina technologies. The sequence type, capsule type, O-locus type, antimicrobial resistance determinants and plasmids carried by the isolates were inferred from the genome sequence. The phylogenetic placement, antimicrobial resistance, and virulence determinants of the isolates relative to a collection (n = 871) of ST16 isolates were assessed. RESULTS: Both BC16, a colistin-resistant blood stream isolate and U23, a colistin-sensitive urinary isolate displayed near-identical antimicrobial resistance profiles and genome sequences with varying plasmid profiles. The BC16 genome only had 21 SNPs relative to U23 and belonged to the same capsule, O-antigen locus and multi-locus sequence types. The mgrB locus in BC16 was disrupted by an IS5 element. Phylogenetically, U23 and BC16 were placed on a clade with 4 strains belonging to K-type K48 and O-type O2a as opposed to majority (n = 807) of the strains (K-type K51 and O-type O3b). CONCLUSIONS: BC16 was a colistin resistant derivative of U23, which evolved colistin resistance by an IS5-mediated disruption of the mgrB locus, likely during treatment of the index patient with colistin in the ICU. The strains belong to a rare subtype of ST16 with unique capsular and O-antigen types underscoring the utility of genomic surveillance networks and open-access genomic surveillance data in tracking problem clones.

Predicting the optimum harvesting dates for different exotic apple varieties grown under North Western Himalayan regions through acoustic and machine vision techniques.

Acoustic system and machine vision were used to evaluate the effects of different harvest dates on the quality and sensory attributes of exotic apple varieties of North Western Himalayan. Gala Redlum (V1) was harvested at 110 (H1), 120 (H2) and 130 (H3) Days from Full Bloom (DFFB); Red Velox (V2) and Super Chief (V3) were harvested at 130 (H1), 140 (H2) and 150 (H3) DFFB. Highest acoustic coefficient (21.13) and firmness (20.72 lbs) recorded at first harvest date (H1) decreased significantly (p ≤0.05) (19.86 to 17.90 lbs) at second harvest (H2) and (17.77 to 16.80 lbs) at third harvest date. Highest starch iodine rating (3.72); anthocyanin content (24.81 mg/100 g); total soluble solids (12.10 %); total sugars (8.75 %) were recorded at H3 in all the varieties. For Gala Redlum (V1) 130 DFFB and for Red Velox (V2) and Super Chief (V3) 150 DFFB were predicted as suitable harvesting dates for table consumption.

Fabrication of a Vitamin B12-Loaded Carbon Dot/Mixed-Ligand Metal Organic Framework Encapsulated within the Gelatin Microsphere for pH Sensing and In Vitro Wound Healing Assessment.

Bacterial invasion is a serious concern during the wound healing process. The colonization of bacteria is mainly responsible for the pH fluctuation at the wound site. Therefore, the fabrication of a proper wound dressing material with antibacterial activity and pH monitoring ability is necessary to acquire a fast healing process. Therefore, this work is dedicated to designing a vitamin B12-loaded gelatin microsphere (MS) decorated with a carbon dot (CD) metal-organic framework (MOF) for simultaneous pH sensing and advanced wound closure application. The resultant MS portrayed a high specific surface area and a hierarchically porous structure. Furthermore, the surface of the resultant MS contained numerous carboxyl groups and amine groups whose deprotonation and protonation with the pH alternation are accountable for the pH-sensitive properties. The vitamin B12 release study was speedy from the MOF structure in an acidic medium, which was checked by gelatin coating, and a controlled drug release behavior was observed. The system showed excellent cytocompatibility toward the L929 cell line and remarkable antibacterial performance against Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus. Furthermore, the combined effect of Zn2+, the imidazole unit, and CDs produces an outstanding bactericidal effect on the injury sites. Finally, the in vitro wound model suggests that the presence of the vitamin B12-loaded gelatin MS accelerates the proliferation of resident fibroblast L929 cells and causes tissue regeneration in a time-dependent manner. The relative wound area, % of wound closure, and wound healing speed values are remarkable and suggest the requirement for assessing the response of the system before exploiting its prospective in vivo application.

CsPbI3/N-GQDs dual layer phosphor-converted white-LEDs with ultrahigh luminous efficiency and color rendering index.

Phosphor-converted LEDs or pc-LEDs, as a solid-state lighting source, are attractive for next-generation display technologies because of their energy savings, and green environmentally friendly nature. Recently, white LEDs are being produced commercially by coating blue LED (440-470 nm) chips with various yellow-emitting phosphors. However, the LEDs produced by this technique often exhibit high correlated color temperature (CCT) and low color rendering index (CRI) values, due to sufficient red spectral components not being present, and thus aren't suitable for commercial grade white illumination. To circumvent this drawback, our work reports for the first time the use of blue and green-emitting nitrogen-functionalized graphene quantum dots (GQDs) coupled with red-emitting CsPbI3NCs for phosphor-based LED applications. We deployed near-UV to visible excitable red-emitting perovskite CsPbI3nanocrystals which contribute toward the red spectral component, thus greatly improving the CRI of the LEDs. CsPbI3nanocrystals are optically excited by nitrogen-functionalized GQD with blue and green emissions in a remote double-layer phosphor stack technique. This double phosphor layer stacking greatly improves both the CRI and luminous efficiency of radiation (LER), which usually has a trade-off in previously reported phosphor stacks. A CCT of ∼5182 K providing daylight white tonality, with superior CRI (∼90%) and ultrahigh LER (∼250 lumens/watt) are reported, which are significantly higher than the established benchmarks.

Unusual Hypermucoviscous Clinical Isolate of Klebsiella pneumoniae with No Known Determinants of Hypermucoviscosity.

Klebsiella pneumoniae can be broadly classified into classical strains that cause drug-resistant, hospital-associated infections and hypervirulent strains that cause invasive, community-acquired, drug-susceptible infections. Hypermucoviscosity in Klebsiella pneumoniae has been associated with immune evasion and hypervirulence. A string-test-positive, hypermucoviscous strain of Klebsiella pneumoniae, P34, was isolated from the cystic lesion of a patient who reported to a tertiary care hospital in Jodhpur, Rajasthan, India. Given the antibiotic-susceptible and hypermucoviscous nature of the isolate, it was suspected to belong to the hypervirulent lineage of Klebsiella pneumoniae. However, P34 did not overproduce capsular polysaccharides and also remained susceptible to the antimicrobial effects of human serum when tested alongside strains that were non-hypermucoviscous. Sequencing of the genome of P34 revealed the absence of any large virulence plasmids or integrative conjugative elements that usually carry hypermucoviscosity- and hypervirulence-associated genes. P34 also lacked key virulence determinants such as aerobactin, yersiniabactin, and salmochelin biosynthesis clusters. In addition, P34 lacked homologs for genes associated with enhanced capsule synthesis and hypermucoviscosity, such as rmpA, rmpA2, rmpC, and rmpD (regulator of mucoid phenotype). These observations suggest that P34 may harbor novel genetic determinants of hypermucoviscosity independent of the indirectly acting rmpA and the recently described rmpD. IMPORTANCE Hypermucoviscosity is a characteristic of hypervirulent Klebsiella pneumoniae strains, which are capable of causing invasive disease in community settings. This study reports phenotyping and genomic analysis of an unusual clinical isolate of Klebsiella pneumoniae, P34, which exhibits hypermucoviscosity and yet does not harbor rmp (regulator of mucoid phenotype) genes, which are known determinants of hypermucoviscosity (rmpA and rmpD). Similar clinical isolates belonging to the K. pneumoniae complex that are hypermucoviscous but do not harbor the rmp loci have been reported from India and abroad, indicating the prevalence of unknown determinants contributing to hypermucoviscosity. Therefore, strains like P34 will serve as model systems to mechanistically study potentially novel determinants of hypermucoviscosity in the K. pneumoniae complex.

Gene expression data classification using topology and machine learning models.

BACKGROUND: Interpretation of high-throughput gene expression data continues to require mathematical tools in data analysis that recognizes the shape of the data in high dimensions. Topological data analysis (TDA) has recently been successful in extracting robust features in several applications dealing with high dimensional constructs. In this work, we utilize some recent developments in TDA to curate gene expression data. Our work differs from the predecessors in two aspects: (1) Traditional TDA pipelines use topological signatures called barcodes to enhance feature vectors which are used for classification. In contrast, this work involves curating relevant features to obtain somewhat better representatives with the help of TDA. This representatives of the entire data facilitates better comprehension of the phenotype labels. (2) Most of the earlier works employ barcodes obtained using topological summaries as fingerprints for the data. Even though they are stable signatures, there exists no direct mapping between the data and said barcodes. RESULTS: The topology relevant curated data that we obtain provides an improvement in shallow learning as well as deep learning based supervised classifications. We further show that the representative cycles we compute have an unsupervised inclination towards phenotype labels. This work thus shows that topological signatures are able to comprehend gene expression levels and classify cohorts accordingly. CONCLUSIONS: In this work, we engender representative persistent cycles to discern the gene expression data. These cycles allow us to directly procure genes entailed in similar processes.

Determining clinically relevant features in cytometry data using persistent homology.

Cytometry experiments yield high-dimensional point cloud data that is difficult to interpret manually. Boolean gating techniques coupled with comparisons of relative abundances of cellular subsets is the current standard for cytometry data analysis. However, this approach is unable to capture more subtle topological features hidden in data, especially if those features are further masked by data transforms or significant batch effects or donor-to-donor variations in clinical data. We present that persistent homology, a mathematical structure that summarizes the topological features, can distinguish different sources of data, such as from groups of healthy donors or patients, effectively. Analysis of publicly available cytometry data describing non-naïve CD8+ T cells in COVID-19 patients and healthy controls shows that systematic structural differences exist between single cell protein expressions in COVID-19 patients and healthy controls. We identify proteins of interest by a decision-tree based classifier, sample points randomly and compute persistence diagrams from these sampled points. The resulting persistence diagrams identify regions in cytometry datasets of varying density and identify protruded structures such as 'elbows'. We compute Wasserstein distances between these persistence diagrams for random pairs of healthy controls and COVID-19 patients and find that systematic structural differences exist between COVID-19 patients and healthy controls in the expression data for T-bet, Eomes, and Ki-67. Further analysis shows that expression of T-bet and Eomes are significantly downregulated in COVID-19 patient non-naïve CD8+ T cells compared to healthy controls. This counter-intuitive finding may indicate that canonical effector CD8+ T cells are less prevalent in COVID-19 patients than healthy controls. This method is applicable to any cytometry dataset for discovering novel insights through topological data analysis which may be difficult to ascertain otherwise with a standard gating strategy or existing bioinformatic tools.

Determining clinically relevant features in cytometry data using persistent homology.

Cytometry experiments yield high-dimensional point cloud data that is difficult to interpret manually. Boolean gating techniques coupled with comparisons of relative abundances of cellular subsets is the current standard for cytometry data analysis. However, this approach is unable to capture more subtle topological features hidden in data, especially if those features are further masked by data transforms or significant batch effects or donor-to-donor variations in clinical data. We present that persistent homology, a mathematical structure that summarizes the topological features, can distinguish different sources of data, such as from groups of healthy donors or patients, effectively. Analysis of publicly available cytometry data describing non-naïve CD8+ T cells in COVID-19 patients and healthy controls shows that systematic structural differences exist between single cell protein expressions in COVID-19 patients and healthy controls. Our method identifies proteins of interest by a decision-tree based classifier and passes them to a kernel-density estimator (KDE) for sampling points from the density distribution. We then compute persistence diagrams from these sampled points. The resulting persistence diagrams identify regions in cytometry datasets of varying density and identify protruded structures such as 'elbows'. We compute Wasserstein distances between these persistence diagrams for random pairs of healthy controls and COVID-19 patients and find that systematic structural differences exist between COVID-19 patients and healthy controls in the expression data for T-bet, Eomes, and Ki-67. Further analysis shows that expression of T-bet and Eomes are significantly downregulated in COVID-19 patient non-naïve CD8+ T cells compared to healthy controls. This counter-intuitive finding may indicate that canonical effector CD8+ T cells are less prevalent in COVID-19 patients than healthy controls. This method is applicable to any cytometry dataset for discovering novel insights through topological data analysis which may be difficult to ascertain otherwise with a standard gating strategy or in the presence of large batch effects. AUTHOR SUMMARY: Identifying differences between cytometry data seen as a point cloud can be complicated by random variations in data collection and data sources. We apply persistent homology used in topological data analysis to describe the shape and structure of the data representing immune cells in healthy donors and COVID-19 patients. By looking at how the shape and structure differ between healthy donors and COVID-19 patients, we are able to definitively conclude how these groups differ despite random variations in the data. Furthermore, these results are novel in their ability to capture shape and structure of cytometry data, something not described by other analyses.

A Multifunctional Smart Textile Derived from Merino Wool/Nylon Polymer Nanocomposites as Next Generation Microwave Absorber and Soft Touch Sensor.

In recent times e-textiles have emerged as wonder safeguards due to the great potential background in space, military, healthcare, or portable electronics. As a result, widespread research and development have been done to make significant advancement in this field, but it still remains a key challenge to use one single product with multifunctional attributes with the past performance of key characteristics. In this work, phase-separated PEDOT:PSS ornamented with reduced graphene oxide (rGO) nanosheets, deposited on the newly fabricated ultralightweight, superhydrophobic, and mechanically enriched merino wool/nylon (W-N) composite textile followed by the dipping and drying strategy. The open edges-layered structure of rGO helping uniform deposition of PEDOTs clusters, which allows the formation of a stacked layer of PEDOTs/rGO-PEDOTs/PEDOTs for robust three-dimensional electrical transforming channel network within the W-N textile surface. These dip-coated multifunctional textiles show high electrical conductivities up to 90.5 S cm-1 conjugated with a flexible electromagnetic interference shielding efficiency of 73.8 dB (in X-band) and in-plane thermal conductivity of 0.81 W/mK with a minimum thickness of 0.84 mm. This thin coating maintained the hydrophobicity (water contact angle of ∼150°) leading to an excellent EM protective cloth combined with real-life antenna performance under high mechanical or chemical tolerance. Interestingly, this multiuse textile can also act as an exceptional TASER Proof Textile (TPT) due to a short out of the electrical shock coming from the TASER by its unique conducting network architecture. Remarkably, this coated textile can get a response by the soft touch to lighten up the household bulb and could establish wireless communication via an HC-05 Bluetooth module as a textile-based touch switch. This developed fabric could perform as a new potentially scalable single product in intelligent smart garments, portable next-generation electronics, and the growing threat of EM pollution.

Three-dimensional laser micrometry characterization of surface wear in total hip arthroplasty.

Even after decades of clinical use, our ability to quantify wear across total hip replacement implant surfaces is largely limited to single value measurements. The influence of patient factors on wear remains enigmatic. This pilot study for the development of three-dimensional laser micrometry (3DLM) introduces an easy, accurate means of 'mapping' and quantifying material removal. A three-dimensional laser micrometer was constructed using a laser micrometer having an accuracy of 0.5 microm. A 3D surface map is triangulated from a point cloud consisting of approximately 140,000 individual points. Comparison to a reference sphere determines radial wear over the entire surface. 3DLM was able to map and quantify fine scale surface features. Even for zirconia on relatively soft ultra-high molecular weight polyethylene, this technique maps the contributions of localized wear at the macroscopic level. The 0.5 microm (or greater) accuracy of these lasers allows us to image surfaces with a high degree of confidence. This analysis lends itself well to automation, and we anticipate that this advance will prove valuable in establishing that each head and cup combination emerging from a given clinical environment has unique wear patterns as observed in this trial data set.