Evaluation of the effect of precursor ratios on the electrochemical performances of binder-free NiMn-phosphate electrodes for supercapattery

Binary metal phosphate electrodes have been widely studied for energy storage applications due to the synergistic effects of two different transition elements that able to provide better conductivity and stability. Herein, the battery-type binder-free nickel-manganese phosphate (NiMn-phosphate) electrodes were fabricated with different Ni:Mn precursor ratios via microwave-assisted hydrothermal technique for 5 min at 90 °C. Overall, NiMn3P electrode (Ni:Mn = 1:3) showed an outstanding electrochemical performance, displaying the highest specific (areal) capacity at 3 A/g of 1262.4 C/g (0.44 C/cm2), and the smallest charge transfer resistance of 108.8 Ω. The enhanced performance of NiMn3P electrode can be ascribed to the fully grown amorphous nature and small-sized flake and flower structures of NiMn3P electrode material on the nickel foam (NF) surface. This configuration offered a higher number of active sites and a larger exposed area, facilitating efficient electrochemical reactions with the electrolyte. Consequently, the NiMn3P//AC electrode combination was chosen to further investigate its performance in supercapattery. The NiMn3P//AC supercapattery exhibited remarkable energy density of 105.4 Wh/kg and excellent cyclic stability with 84.7% retention after 3000 cycles. These findings underscored the superior electrochemical performance of the battery-type binder-free NiMn3P electrode, and highlight its potential for enhancing the overall performance of supercapattery.

Perspectives on cultivation and harvesting technologies of microalgae, towards environmental sustainability and life cycle analysis

The development of algae is seen as a potential and ecologically sound approach to address the increasing demands in multiple sectors. However, successful implementation of processes is highly dependent on effective growing and harvesting methods. The present study provides a complete examination of contemporary techniques employed in the production and harvesting of algae, with a particular emphasis on their sustainability. The review begins by examining several culture strategies, encompassing open ponds, closed photobioreactors, and raceway ponds. The analysis of each method is conducted in a systematic manner, with a particular focus on highlighting their advantages, limitations, and potential for expansion. This approach ensures that the conversation is in line with the objectives of sustainability. Moreover, this study explores essential elements of algae harvesting, including the processes of cell separation, dewatering, and biomass extraction. Traditional methods such as centrifugation, filtration, and sedimentation are examined in conjunction with novel, environmentally concerned strategies including flocculation, electro-coagulation, and membrane filtration. It evaluates the impacts on the environment that are caused by the cultivation process, including the usage of water and land, the use of energy, the production of carbon dioxide, and the runoff of nutrients. Furthermore, this study presents a thorough examination of the current body of research pertaining to Life Cycle Analysis (LCA) studies, presenting a perspective that emphasizes sustainability in the context of algae harvesting systems. In conclusion, the analysis ends up with an examination ahead at potential areas for future study in the cultivation and harvesting of algae. This review is an essential guide for scientists, policymakers, and industry experts associated with the advancement and implementation of algae-based technologies.

Slideflow: deep learning for digital histopathology with real-time whole-slide visualization

Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing the feasibility and practicality of experimenting with new architectures. We developed a flexible deep learning library for histopathology called Slideflow, a package which supports a broad array of deep learning methods for digital pathology and includes a fast whole-slide interface for deploying trained models. Slideflow includes unique tools for whole-slide image data processing, efficient stain normalization and augmentation, weakly-supervised whole-slide classification, uncertainty quantification, feature generation, feature space analysis, and explainability. Whole-slide image processing is highly optimized, enabling whole-slide tile extraction at 40x magnification in 2.5 s per slide. The framework-agnostic data processing pipeline enables rapid experimentation with new methods built with either Tensorflow or PyTorch, and the graphical user interface supports real-time visualization of slides, predictions, heatmaps, and feature space characteristics on a variety of hardware devices, including ARM-based devices such as the Raspberry Pi.

Generative Adversarial Networks Accurately Reconstruct Pan-Cancer Histology from Pathologic, Genomic, and Radiographic Latent Features

Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of novel molecular features. These approaches distill cancer histologic images into high-level features which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network - HistoXGAN - capable of reconstructing representative histology using feature vectors produced by common feature extractors. We evaluate HistoXGAN across 29 cancer subtypes and demonstrate that reconstructed images retain information regarding tumor grade, histologic subtype, and gene expression patterns. We leverage HistoXGAN to illustrate the underlying histologic features for deep learning models for actionable mutations, identify model reliance on histologic batch effect in predictions, and demonstrate accurate reconstruction of tumor histology from radiographic imaging for a 'virtual biopsy'.

Staphylococcal scalded skin syndrome in neonate: Another face of CA-MRSA

ABSTRACT

Staphylococcal scalded skin syndrome (SSSS), also known as Ritter's disease, in its severe form occurs predominantly in infants and children. It is caused by infection with group II (often phage group 71) Staphylococcus aureus. The foci of infection include nasopharynx, less commonly umbilicus, urinary tract, superficial abrasion, conjunctivae, and blood. Staphylococci are non-motile, non-spore-forming, catalase-positive, gram-positive cocci that appear predominantly as grape-like clusters. Although this organism is frequently a part of normal human microbial flora, it can cause significant opportunistic infections under certain conditions such as when extremes of age groups are involved, the presence of indwelling medical devices, and intravenous (iv) drug abuse. Staphylococcus aureus may cause a variety of infectious manifestations ranging from relatively benign skin infections to life-threatening systemic illnesses. SSSS caused by S. aureus strains produces exfoliative toxins which result in the development of blisters, erythema, and desquamation. Here, we present a case of an 11-day-old neonate who was diagnosed with SSSS. The causative agent responsible for this syndrome was identified as methicillin-resistant Staphylococcus aureus (MRSA). The molecular characterization of the gene Panton-Valentine leukocidin (PVL) was done by polymerase chain reaction (PCR) and was detected positive for PVL which is a distinctive virulence factor seen almost in all of the community-acquired MRSA strains. The patient was discharged after parenteral clindamycin therapy with almost complete resolution of symptoms.

Magnetic and Magnetostrictive Properties of Sol-Gel-Synthesized Chromium-Substituted Cobalt Ferrite

Chromium (Cr)-doped cobalt ferrite nanoparticles were synthesized using a sol-gel autocombustion method, with the chemical formula CoCrxFe2xO4. The value of x ranged from 0.00 to 0.5 in 0.1 increments. X-ray diffraction analysis confirmed the development of highly crystalline cubic spinel structures for all samples, with an average crystallite size of approximately 40 to 45 nm determined using the Scherrer equation. Pellets were prepared using a traditional ceramic method. The magnetic and magnetostrictive properties of the samples were tested using strain gauge and VSM (vibrating sample magnetometer) techniques. The results of the magnetic and magnetostrictive tests showed that the chromium-substituted cobalt ferrites exhibited higher strain derivative magnitudes than pure cobalt ferrite. These findings indicated that the introduction of chromium into the cobalt ferrite structure led to changes in the material's magnetic properties. These changes were attributed to anisotropic contributions, resulting from an increased presence of Co2+ ions at B-sites due to the chromium substitutions. In summary, this study concluded that introducing chromium into the cobalt ferrite structure caused alterations in the material's magnetic properties, which were explained by changes in the cationic arrangement within the crystal lattice. This study successfully explained these alterations using magnetization and coercivity data and the probable cationic dispersion.

Efficacy and safety of fixed-dose combination of Bilastine-Montelukast in adult patients with allergic rhinitis: a phase III, randomized, multi-center, double-blind, active controlled clinical study

BACKGROUND

Histamine and cysteinyl leukotrienes (CysLTs) are potent inflammatory mediators in allergic rhinitis (AR). Studies involving other combinations of antihistaminics (Levocetirizine) and highly selective leukotriene receptor antagonist (LTA) (Montelukast) combination have shown additive benefits and are widely prescribed for AR.

OBJECTIVE

Evaluate the efficacy and safety of Bilastine 20 mg and Montelukast 10 mg fixed-dose combination (FDC) therapy in patients with AR.

METHODS

A randomized, double-blind, comparative, parallel, phase III study was conducted to evaluate efficacy and safety of Bilastine 20 mg and Montelukast 10 mg FDC at 16 tertiary care otolaryngology centres in India. Adult patients with AR for one year with IgE antibody positive and 12-h NSS score >36 in 3 days were randomized to receive either Bilastine 20 mg and Montelukast 10 mg or Montelukast 10 mg & Levocetirizine 5 mg tablets for 4 weeks. The change in total symptom score (nasal symptom scores (NSS) & non-nasal symptom scores (NNSS)) from baseline to week 4 was assessed as primary endpoint. Secondary endpoints included changes in TSS, NSS, NNSS, individual symptom scores (ISS), Rhinoconjunctivitis Quality of Life (RQLQ), discomfort due to rhinitis (VAS), and clinical global impression (CGI) scores.

RESULTS

The change in mean TSS from baseline to week 4 in Test group (16.6 units) was comparable to reference group (17 units) (p= 0.8876). The difference in change in mean NSS, NNSS and ISS from baseline to day 7, 14, 28 were comparable. RQLQ improved from baseline to Day 28. Significant improvements were observed in discomfort due to AR measured by VAS and CGI scores from baseline to day 14 and 28. The safety and tolerability of patients were comparable between the groups. All adverse events (AEs) were mild to moderate in severity. No patient discontinued due to AEs.

CONCLUSIONS

The FDC of Bilastine 20 mg and Montelukast 10 mg was efficacious and well tolerated in Indian patients with AR.

Research on Cartilage 3D Printing Technology Based on SA-GA-HA

Cartilage damage is difficult to heal and poses a serious problem to human health as it can lead to osteoarthritis. In this work, we explore the application of biological 3D printing to manufacture new cartilage scaffolds to promote cartilage regeneration. The hydrogel made by mixing sodium alginate (SA) and gelatin (GA) has high biocompatibility, but its mechanical properties are poor. The addition of hydroxyapatite (HA) can enhance its mechanical properties. In this paper, the preparation scheme of the SA-GA-HA composite hydrogel cartilage scaffold was explored, the scaffolds prepared with different concentrations were compared, and better formulations were obtained for printing and testing. Mathematical modeling of the printing process of the bracket, simulation analysis of the printing process based on the mathematical model, and adjustment of actual printing parameters based on the results of the simulation were performed. The cartilage scaffold, which was printed using Bioplotter 3D printer, exhibited useful mechanical properties suitable for practical needs. In addition, ATDC-5 cells were seeded on the cartilage scaffolds and the cell survival rate was found to be higher after one week. The findings demonstrated that the fabricated chondrocyte scaffolds had better mechanical properties and biocompatibility, providing a new scaffold strategy for cartilage tissue regeneration.

A universal tool for stability predictions of biotherapeutics, vaccines and in vitro diagnostic products

It is of particular interest for biopharmaceutical companies developing and distributing fragile biomolecules to warrant the stability and activity of their products during long-term storage and shipment. In accordance with quality by design principles, advanced kinetic modeling (AKM) has been successfully used to predict long-term product shelf-life and relies on data from short-term accelerated stability studies that are used to generate Arrhenius-based kinetic models that can, in turn, be exploited for stability forecasts. The AKM methodology was evaluated through a cross-company perspective on stability modeling for key stability indicating attributes of different types of biotherapeutics, vaccines and biomolecules combined in in vitro diagnostic kits. It is demonstrated that stability predictions up to 3 years for products maintained under recommended storage conditions (2-8 °C) or for products that have experienced temperature excursions outside the cold-chain show excellent agreement with experimental real-time data, thus confirming AKM as a universal and reliable tool for stability predictions for a wide range of product types.

Fabrication and characterization of graphene oxide-based polymer nanocomposite coatings, improved stability and hydrophobicity

In this study, acrylic-epoxy-based nanocomposite coatings loaded with different concentrations (0.5-3 wt.%) of graphene oxide (GO) nanoparticles were successfully prepared via the solution intercalation approach. The thermogravimetric analysis (TGA) revealed that the inclusion of GO nanoparticles into the polymer matrix increased the thermal stability of the coatings. The degree of transparency evaluated by the ultraviolet-visible (UV-Vis) spectroscopy showed that the lowest loading rate of GO (0.5 wt.%) had completely blocked the incoming irradiation, thus resulting in zero percent transmittance. Furthermore, the water contact angle (WCA) measurements revealed that the incorporation of GO nanoparticles and PDMS into the polymer matrix had remarkably enhanced the surface hydrophobicity, exhibiting the highest WCA of 87.55º. In addition, the cross-hatch test (CHT) showed that all the hybrid coatings exhibited excellent surface adhesion behaviour, receiving 4B and 5B ratings respectively. Moreover, the field emission scanning electron microscopy (FESEM) micrographs confirmed that the presence of the functional groups on the GO surface facilitated the chemical functionalization process, which led to excellent dispersibility. The GO composition up to 2 wt.% showed excellent dispersion and uniform distribution of the GO nanoparticles within the polymer matrix. Therefore, the unique features of graphene and its derivatives have emerged as a new class of nanofillers/inhibitors for corrosion protection applications.

Deep learning generates synthetic cancer histology for explainability and education

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers

Large language models such as ChatGPT can produce increasingly realistic text, with unknown information on the accuracy and integrity of using these models in scientific writing. We gathered fifth research abstracts from five high-impact factor medical journals and asked ChatGPT to generate research abstracts based on their titles and journals. Most generated abstracts were detected using an AI output detector, 'GPT-2 Output Detector', with % 'fake' scores (higher meaning more likely to be generated) of median [interquartile range] of 99.98% 'fake' [12.73%, 99.98%] compared with median 0.02% [IQR 0.02%, 0.09%] for the original abstracts. The AUROC of the AI output detector was 0.94. Generated abstracts scored lower than original abstracts when run through a plagiarism detector website and iThenticate (higher scores meaning more matching text found). When given a mixture of original and general abstracts, blinded human reviewers correctly identified 68% of generated abstracts as being generated by ChatGPT, but incorrectly identified 14% of original abstracts as being generated. Reviewers indicated that it was surprisingly difficult to differentiate between the two, though abstracts they suspected were generated were vaguer and more formulaic. ChatGPT writes believable scientific abstracts, though with completely generated data. Depending on publisher-specific guidelines, AI output detectors may serve as an editorial tool to help maintain scientific standards. The boundaries of ethical and acceptable use of large language models to help scientific writing are still being discussed, and different journals and conferences are adopting varying policies.

Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology

The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict genetic alterations from pathology slides, but it is unclear how well these predictions generalize to external datasets. We performed a systematic study on Deep-Learning-based prediction of genetic alterations from histology, using two large datasets of multiple tumor types. We show that an analysis pipeline that integrates self-supervised feature extraction and attention-based multiple instance learning achieves a robust predictability and generalizability.

Applications of Deep Learning in Endocrine Neoplasms

Machine learning methods have been growing in prominence across all areas of medicine. In pathology, recent advances in deep learning (DL) have enabled computational analysis of histological samples, aiding in diagnosis and characterization in multiple disease areas. In cancer, and particularly endocrine cancer, DL approaches have been shown to be useful in tasks ranging from tumor grading to gene expression prediction. This review summarizes the current state of DL research in endocrine cancer histopathology with an emphasis on experimental design, significant findings, and key limitations.

3D Printing and Performance Study of Porous Artificial Bone Based on HA-ZrO2-PVA Composites

An ideal artificial bone implant should have similar mechanical properties and biocompatibility to natural bone, as well as an internal structure that facilitates stomatal penetration. In this work, 3D printing was used to fabricate and investigate artificial bone composites based on HA-ZrO₂-PVA. The composites were proportionally configured using zirconia (ZrO₂), hydroxyapatite (HA) and polyvinyl alcohol (PVA), where the ZrO₂ played a toughening role and PVA solution served as a binder. In order to obtain the optimal 3D printing process parameters for the composites, a theoretical model of the extrusion process of the composites was first established, followed by the optimization of various parameters including the spray head internal diameter, extrusion pressure, extrusion speed, and extrusion line width. The results showed that, at the optimum parameters of a spray head diameter of 0.2 mm, extrusion pressure values ranging from 1-3 bar, a line spacing of 0.8-1.5 mm, and a spray head displacement range of 8-10 mm/s, a better structure of biological bone scaffolds could be obtained. The mechanical tests performed on the scaffolds showed that the elastic modulus of the artificial bone scaffolds reached about 174 MPa, which fulfilled the biomechanical requirements of human bone. According to scanning electron microscope observation of the scaffold sample, the porosity of the scaffold sample was close to 65%, which can well promote the growth of chondrocytes and angiogenesis. In addition, c5.18 chondrocytes were used to verify the biocompatibility of the composite materials, and the cell proliferation was increased by 100% when compared with that of the control group. The results showed that the composite has good biocompatibility.

Recent advances in anaesthesia for intrauterine and foetal surgery

Advances in prenatal diagnostic techniques have enabled early detection of potentially correctable foetal anomalies. Here, we summarise recent developments in anaesthesia for foetal surgery. Types of foetal surgery include minimally invasive, open mid-gestational and ex-utero intrapartum treatment (EXIT) procedures. Foetoscopic surgery avoids hysterotomy, with risk of uterine dehiscence, preserving the possibility of subsequent vaginal delivery. Minimally invasive procedures are performed under local or regional anaesthesia; open or EXIT procedures are usually done under general anaesthesia. Requirements include maintenance of uteroplacental blood flow, and uterine relaxation to prevent placental separation and premature labour. Foetal requirements include monitoring of well-being, providing analgesia and immobility. EXIT procedures require maintenance of placental circulation till the airway is secured, requiring multidisciplinary involvement. Here, the uterine tone must return after baby delivery to prevent major maternal haemorrhage. The anaesthesiologist plays a crucial role in maintaining maternal and foetal homeostasis and optimising surgical conditions.

Creep Behavior of A356 Aluminum Alloy Reinforced with Multi-Walled Carbon Nanotubes by Stir Casting

Lightweight aluminum alloy components are often used to manufacture a variety of engineering components in many industries. In recent years, researchers have studied the effect of improving the mechanical properties of metal alloys by incorporating nano-carbon into its structure. In this study, the effect of the addition of 0.2, 0.5, and 1 wt% of multi-walled carbon nanotubes (MWCNTs) on the stress-strain behavior and creep phenomenon of an A356 aluminum alloy were studied. The effect of nickel coating on 0.2 wt% MWCNTs was also investigated. Samples were prepared using the stir-casting method. The results revealed that the grain size became finer when MWCNT nano-particulates were introduced. Although the MWCNTs were distributed homogeneously in the A356 matrix, as confirmed by FESEM analysis, there were some agglomerations observed in a specific area with dimensions smaller than 100 nm. Nevertheless, the addition of MWCNTs was found to be beneficial in enhancing the hardness of alloys containing 0.2 wt%, 0.2 wt% nickel-coated, 0.5 wt%, and 1 wt% MWCNTs by 9%, 24%, 32%, and 15%, respectively, as compared with the unreinforced A345 matrix. It was also found that the 0.5 wt% MWCNT-A356 matrix exhibited an improvement in the creep lifetime by more than two orders of magnitude.

Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a clinically-oriented approach to uncertainty quantification for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without uncertainty, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that uncertainty thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts.

Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review

Genomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time. The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.

Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review

PURPOSE

There is a need for an improved understanding of clinical and biologic risk factors in pediatric cancer to improve patient outcomes. Machine learning (ML) represents the application of computational inference from advanced statistical methods that can be applied to increasing amount of data available for study in pediatric oncology. The goal of this systematic review was to systematically characterize the state of ML in pediatric oncology and highlight advances and opportunities in the field.

METHODS

We conducted a systematic review of the Embase, Scopus, and MEDLINE databases for applications of ML in pediatric oncology. Query results from all three databases were aggregated and duplicate studies were removed.

RESULTS

A total of 42 unique articles that examined the applications of ML in pediatric oncology met inclusion criteria for review. We identified 20 studies of CNS tumors, 13 of solid tumors, and nine of leukemia. ML tasks included classification, prediction of treatment response, and dose optimization with a variety of methods being used including neural network, k-nearest neighbor, random forest, naive Bayes, and support vector machines. Strengths of the identified studies included matching or outperforming physician comparators via automated analysis and predicting therapeutic response. Common limitations included significant heterogeneity in reporting standards, clinical applicability, small sample sizes, and missing external validation cohorts.

CONCLUSION

We identified areas where ML can enhance clinical care in ways that may not otherwise be achievable. Although ML promises enormous potential in improving diagnostics, decision making, and monitoring for children with cancer, the field remains in early stages and future work will be aided by standards and guidelines to ensure rigorous methodologic design and maximizing clinical utility.

Assessment of natural DNA methylation variation and its association with economically important traits in dolichos bean (Lablab purpureus L. Var. Lignosus) using AMP-PCR assay

As a prelude to exploit DNA methylation-induced variation, we hypothesized the existence of substantial natural DNA methylation variation and its association with economically important traits in dolichos bean, and tested it using amplified methylation polymorphism-polymerase chain reaction (AMP-PCR) assay. DNA methylation patterns such as internal, external, full and non-methylation were amplified in a set of 64 genotypes using 26 customized randomly amplified polymorphic DNA (RAPD) primers containing 5'CCGG3' sequence. The 64 genotypes included 60 germplasm accessions (GA), two advanced breeding lines (ABLs) and two released varieties. The ABLs and released varieties are referred to as improved germplasm accessions (IGA) in this study. The association of DNA methylation patterns with economically important traits such as days to 50% flowering, raceme length, fresh pods plant^-1, fresh pod yield plant^-1 and 100-fresh seed weight was explored. At least 50 genotypes were polymorphic for DNA methylation patterns at 10 loci generated by seven of the 26 RAPD primers. The GA and IGA differed significantly for total, full and external methylation and the frequency of methylation was higher in GA compared to that in IGA. The genotypes with external methylation produced longer racemes than those with full, internal and non-methylation in that order at polymorphic RAPD-11-242 locus. High pod yielding genotypes had significantly lower frequency of full methylation than low yielding ones. On the contrary, the genotypes that produced heavier fresh seeds harboured higher frequencies of total and externally methylated loci than those that produced lighter fresh seeds.

Predicting Response to Chemotherapy in Patients With Newly Diagnosed High-Risk Neuroblastoma: A Report From the International Neuroblastoma Risk Group

PURPOSE

Metaiodobenzylguanidine (MIBG) scans are a radionucleotide imaging modality that undergo Curie scoring to semiquantitatively assess neuroblastoma burden, which can be used as a marker of therapy response. We hypothesized that a convolutional neural network (CNN) could be developed that uses diagnostic MIBG scans to predict response to induction chemotherapy.

METHODS

We analyzed MIBG scans housed in the International Neuroblastoma Risk Group Data Commons from patients enrolled in the Children's Oncology Group high-risk neuroblastoma study ANBL12P1. The primary outcome was response to upfront chemotherapy, defined as a Curie score ≤ 2 after four cycles of induction chemotherapy. We derived and validated a CNN using two-dimensional whole-body MIBG scans from diagnosis and evaluated model performance using area under the receiver operating characteristic curve (AUC). We also developed a clinical classification model to predict response on the basis of age, stage, and MYCN amplification.

RESULTS

Among 103 patients with high-risk neuroblastoma included in the final cohort, 67 (65%) were responders. Performance in predicting response to upfront chemotherapy was equivalent using the CNN and the clinical model. Class-activation heatmaps verified that the CNN used areas of disease within the MIBG scans to make predictions. Furthermore, integrating predictions using a geometric mean approach improved detection of responders to upfront chemotherapy (geometric mean AUC 0.73 v CNN AUC 0.63, P < .05; v clinical model AUC 0.65, P < .05).

CONCLUSION

We demonstrate feasibility in using machine learning of diagnostic MIBG scans to predict response to induction chemotherapy for patients with high-risk neuroblastoma. We highlight improvements when clinical risk factors are also integrated, laying the foundation for using a multimodal approach to guiding treatment decisions for patients with high-risk neuroblastoma.

Discovery of genomic regions associated with resistance to late wilt disease caused by Harpophora maydis (Samra, Sabet and Hing) in maize (Zea mays L.)

Late wilt disease (LWD) caused by Harpophora maydis (Samra, Sabet and Hing) is emerging as major production constraint in maize across the world. As a prelude to develop maize hybrid resistance to LWD, genetic basis of resistance was investigated. Two F_2:3 mapping populations (derived from CV156670 × 414-33 (P-1) and CV156670 × CV143587 (P-2)) were challenged with LWD at two locations (Kallinayakanahalli and Muppadighatta) during 2017 post-rainy season. A wider range of LWD scores was observed at both locations in both the populations. LWD response was influenced by significant genotype × location interaction. Six and 56 F_2:3 progeny families showed resistance level better than resistant parent. A total of 150 and 199 polymorphic single nucleotide polymorphism markers were used to genotype P-1 and P-2, respectively. Inclusive composite interval mapping was performed to detect significant Quantitative Trait Loci (QTL), QTL × QTL, QTL × location interaction effects. Three major and four minor QTL controlling LWD resistance were detected on chromosome-1. The position and effect of the QTL varied with the location. Significant di-QTL interactions involving QTL (with significant and/or non-significant effects) located within and between all the ten chromosomes were detected. Five of the seven detected QTL showed significant QTL × location interaction. Though two major QTL (q-lw-1.5 and q-lw-1.6) with lower Q×L interaction effects could be considered as stable, their phenotypic variance is not large enough to deploy them in Marker Assisted Selection (MAS). However, these QTL are of paramount importance in accumulating positive alleles for LWD resistance breeding.

Long-term outcomes in children with Hirschsprung's disease and transition zone bowel pull-through: impact of surgical techniques and role for conservative approach

PURPOSE

Presence of transition zone (TZ) in the pulled colon can impact the outcome of surgery in children with Hirschsprung's disease. There is a wide variation in terminology used to define TZ and its management. We present our series of managing 11 such children with considerations for conservative management.

METHODS

Eleven of 114 children operated for Hirschsprung's disease had features of TZ on the 4-quadrant doughnut assessment of proximal anastomosing margin. They were followed up for development of obstructive symptoms, failure of pull-through procedure or bowel-related complications. Intervention done were observation with laxatives, dilatation, Botox injection and redo pull-through.

RESULTS

Of the 11 children, 6 underwent Duhamel's procedure and 5, transanal endorectal pull-through (TERP). Features identified on HPE were presence of hypertrophic nerve bundles involving 2 or 3 quadrants in the circumferential doughnut biopsy of proximal anastomosing margin. Observed symptoms included constipation, enterocolitis, increased bowel frequency and soiling. Intervention done were use of laxatives with bowel management program in six and Botox injections in four. Only one child with TZ in 3 quadrants required redo surgery. Mean follow-up was 5.2 years with resolution of symptoms in most.

CONCLUSION

This study highlights the role of conservative management with good outcomes in children with TZ bowel pull-through having hypertrophic nerve fibers and normal ganglion pattern. Children who underwent Duhamel's procedure had little impact with the presence of TZ at anastomotic margin and majority of those undergoing TERP benefitted from Botox injection. Conservative management can be attempted successfully to prevent redo surgical interventions as they can lead to poorer outcomes. Only those children not responding to conservative measures need to be planned for revision surgery.

Predicting response to chemotherapy in neuroblastoma using deep learning: A report from the International Neuroblastoma Risk Group

10039

Background: Metaiodobenzylguanidine (MIBG) scans are a radionucleotide imaging modality used to evaluate neuroblastoma stage at diagnosis and also determine disease response following therapy. Curie scoring is used to semi-quantitatively assess disease burden from an MIBG scan on a scale from none (0) to widespread throughout the body (30). While a Curie score ≤2 after six cycles of induction chemotherapy has been shown to be prognostic of outcome, there is no established correlation between diagnostic Curie score and outcome. Deep learning models, such as convolutional neural networks (CNN), have been shown to learn generalizable patterns within images for successful classification of metastases and detection of multiple adult cancers. We hypothesized a CNN could be developed to predict response to induction chemotherapy, a proxy for outcome, using diagnostic MIBG scans. Methods: DICOM MIBG scans and associated clinical data from a Children’s Oncology Group (COG) pilot study for children diagnosed with high-risk neuroblastoma (ANBL12P1; NCT1798004) were deidentified and linked to clinical data by the Pediatric Cancer Data Commons and obtained from the International Neuroblastoma Risk Group Data Commons. Patients were defined as having a poor response to induction chemotherapy if their Curie score after four cycles of induction chemotherapy was ≥2. An independent external validation cohort was comprised of 29 images from 26 high-risk patients treated at the University of Chicago with clinically-annotated diagnostic and post-cycle six induction DICOM MIBG scans. The CNN was trained using 2D whole body MIBG scans obtained at diagnosis. We developed the CNN using a transfer learning approach using the Xception architecture as the base layer. Hyperparameter optimization was performed using an 80%-20% train-validation strategy. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Results: Among 146 patients with high-risk neuroblastoma enrolled on ANBL12P1, 104 had available diagnostic and end-induction MIBG scans. There were no differences in clinical or biological characteristics between included and excluded patients. The base model CNN was able to predict which patients had a poor response to induction chemotherapy with an AUROC of 0.72 in the validation set from the ANBL12P1 cohort. Additionally, the CNN was able to predict patient response to therapy with an AUROC of 0.64 in an independent external dataset from University of Chicago. Conclusions: Our study suggests it is feasible to apply machine learning of diagnostic MIBG scans to predict response to chemotherapy for high-risk neuroblastoma patients. Given these promising results, further work to improve AUROC and performance within larger datasets is ongoing.

Optical diagnosis of oral lichen planus: A clinical study on the use of autofluorescence spectroscopy combined with multivariate analysis

Oral lichen planus (OLP) is a chronic mucocutaneous inflammatory condition of stratified squamous epithelia. OLP is a potentially malignant condition in oral mucosa. Patients with OLP have an increased risk of developing squamous cell carcinoma. Therefore an early and accurate diagnosis is necessary to avoid further damage to the oral mucosa. Biopsy followed by histopathological examination is the gold standard for the diagnosis of oral mucosal lesions including OLP. But this invasive procedure is traumatic and time consuming with limited statistical confidence level. Autofluorescence spectroscopy (AFS) has recently emerged as a potential tool to evaluate the biochemical changes associated with oral cavity disorders. In this study, we used AFS to differentiate the oral cavity tissue of 20 OLP patients from that of 16 normal volunteers. Spectra from oral mucosa were acquired at 280, 320 and 410 nm excitation wavelengths which correspond to the excitation energy of major endogenous fluorophores. Normalized spectral data at 320 nm excitation showed significant increase in the intensity of collagen peak for OLP. Optical redox ratio and total hemoglobin concentration estimated from the spectral data revealed significant increase and decrease respectively in OLP and normal patients. Principal component analysis followed by linear discriminant analysis (PCA-LDA) provided sensitivity and specificity of 71 and 80%, 80 and 90%, and 72 and 75% respectively for 280, 320 and 410 nm excited spectral datasets. Meanwhile, partial least square discriminant analysis (PLS-DA) provided sensitivity and specificity of 69 and 77%, 78 and 91% and 73 and 78.13% respectively for 280, 320 and 410 nm excited spectral datasets. From the results, it is concluded that AFS is an efficient tool for the non invasive diagnosis of OLP, with 320 nm light identified as the best wavelength for excitation.

Image analysis and data processing for COVID-19

COVID-19 is a deadly disease caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2). It was first discovered by variations in the respirational and immune systems of a patient who died of a severe acute respiratory syndrome. The first country heavily affected by coronavirus was China. The first case was detected in Wuhan city, China. This virus spreads rapidly from person to person. Based on laboratory tests for coronavirus disease in humans, it is suspected that bats are the natural source of spread of large varieties of virus. The two major viruses, SARS-CoV and Middle East respiratory syndrome coronavirus, originated from the bat; it caused an unexpected disease outbreak in the 21st century throughout the world. Researchers and doctors have investigated COVID in cadavers. The virus was detected in lung, trachea/bronchus, stomach, small intestine, distal convoluted renal tubule, sweat gland, pancreas, adrenal gland, parathyroid, pituitary, cerebrum, and liver. However, it was not noted in bone marrow, heart, aorta, cerebellum, thyroid, testis, esophagus, spleen, lymph node, ovary, muscle, or uterus. This chapter briefly discusses image analysis and data processing used to accelerate COVID-19 detection and support the efforts of researchers and physician to help infected people and break the chain of disease from person to person.

Cobalt Oxide Nanograins and Silver Nanoparticles Decorated Fibrous Polyaniline Nanocomposite as Battery-Type Electrode for High Performance Supercapattery

In this study, silver (Ag) and cobalt oxide (Co₃O₄) decorated polyaniline (PANI) fibers were prepared by the combination of in-situ aniline oxidative polymerization and the hydrothermal methodology. The morphology of the prepared Ag/Co₃O₄@PANI ternary nanocomposite was studied by scanning electron microscopy and transmission electron microscopy, while the structural studies were carried out by X-ray diffraction and X-ray photoelectron spectroscopy. The morphological characterization revealed fibrous shaped PANI, coated with Ag and Co₃O₄ nanograins, while the structural studies revealed high purity, good crystallinity, and slight interactions among the constituents of the Ag/Co₃O₄@PANI ternary nanocomposite. The electrochemical performance studies revealed the enhanced performance of the Ag/Co₃O₄@PANI nanocomposite due to the synergistic/additional effect of Ag, Co₃O₄ and PANI compared to pure PANI and Co₃O₄@PANI. The addition of the Ag and Co₃O₄ provided an extended site for faradaic reactions leading to the high specific capacity. The Ag/Co₃O₄@PANI ternary nanocomposite exhibited an excellent specific capacity of 262.62 C g⁻¹ at a scan rate of 3 mV s⁻¹. The maximum energy and power density were found to be 14.01 Wh kg⁻¹ and 165.00 W kg⁻¹, respectively. The cyclic stability of supercapattery (Ag/Co₃O₄@PANI//activated carbon) consisting of a battery type electrode demonstrated a gradual increase in specific capacity with a continuous charge–discharge cycle until ~1000 cycles, then remained stable until 2500 cycles and later started decreasing, thereby showing the cyclic stability of 121.03% of its initial value after 3500 cycles.

Fundamental Concepts of Hydrogels: Synthesis, Properties, and Their Applications

In the present review, we focused on the fundamental concepts of hydrogels-classification, the polymers involved, synthesis methods, types of hydrogels, properties, and applications of the hydrogel. Hydrogels can be synthesized from natural polymers, synthetic polymers, polymerizable synthetic monomers, and a combination of natural and synthetic polymers. Synthesis of hydrogels involves physical, chemical, and hybrid bonding. The bonding is formed via different routes, such as solution casting, solution mixing, bulk polymerization, free radical mechanism, radiation method, and interpenetrating network formation. The synthesized hydrogels have significant properties, such as mechanical strength, biocompatibility, biodegradability, swellability, and stimuli sensitivity. These properties are substantial for electrochemical and biomedical applications. Furthermore, this review emphasizes flexible and self-healable hydrogels as electrolytes for energy storage and energy conversion applications. Insufficient adhesiveness (less interfacial interaction) between electrodes and electrolytes and mechanical strength pose serious challenges, such as delamination of the supercapacitors, batteries, and solar cells. Owing to smart and aqueous hydrogels, robust mechanical strength, adhesiveness, stretchability, strain sensitivity, and self-healability are the critical factors that can identify the reliability and robustness of the energy storage and conversion devices. These devices are highly efficient and convenient for smart, light-weight, foldable electronics and modern pollution-free transportation in the current decade.