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Abstract BIVV003 is a gene-edited autologous cell therapy in clinical development for the potential treatment of sickle cell disease (SCD). Hematopoietic stem cells (HSC) are genetically modified with mRNA encoding zinc finger nucleases (ZFN) that target and disrupt a specific regulatory GATAA motif in the BCL11A erythroid enhancer to reactivate fetal hemoglobin (HbF). We characterized ZFN-edited HSC from healthy donors and donors with SCD. Results of preclinical studies show that ZFN-mediated editing is highly efficient, with enriched biallelic editing and high frequency of on-target indels, producing HSC capable of long-term multilineage engraftment in vivo, and express HbF in erythroid progeny. Interim results from the Phase 1/2 PRECIZN-1 study demonstrated that BIVV003 was well-tolerated in seven participants with SCD, of whom five of the six with more than 3 months of follow-up displayed increased total hemoglobin and HbF, and no severe vaso-occlusive crises. Our data suggest BIVV003 represents a compelling and novel cell therapy for the potential treatment of SCD.

Abstract The COVID-19 pandemic brought forth an urgent need for widespread genomic surveillance for rapid detection and monitoring of emerging SARS-CoV-2 variants. It necessitated design, development, and deployment of a nationwide infrastructure designed for sequestration, consolidation, and characterization of patient samples that disseminates de-identified information to public authorities in tight turnaround times. Here, we describe our development of such an infrastructure, which sequenced 594,832 high coverage SARS-CoV-2 genomes from isolates we collected in the United States (U.S.) from March 13th 2020 to July 3rd 2023. Our sequencing protocol (‘Virseq’) utilizes wet and dry lab procedures to generate mutation-resistant sequencing of the entire SARS-CoV-2 genome, capturing all major lineages. We also characterize 379 clinically relevant SARS-CoV-2 multi-strain co-infections and ensure robust detection of emerging lineages via simulation. The modular infrastructure, sequencing, and analysis capabilities we describe support the U.S. Centers for Disease Control and Prevention national surveillance program and serve as a model for rapid response to emerging pandemics at a national scale.

Background: Laryngoscopy and intubation remain critical in pediatric patients as compared to adult patients due to different anatomy. This study was conducted to compare the Cormack and Lehane grade (CL) of laryngoscopy, percentage of glottic opening (POGO) score, and the efficacy of backward upward rightward pressure (BURP) maneuver to improve laryngoscopy grade with or without the use of intubation pillow. Subjects and Methods: A randomized controlled trial was done from January 2020 to July 2020 on 68 children with 34 children in Group 1 (with intubation pillow) and 34 patients in Group 2 (without intubation pillow). Children between 5 and 12 years of age (the American Society of Anesthesiologists class I and II) planned for surgery under general anesthesia with endotracheal tube were included in this study. Pillow of 4 cm size was used in 5–9 years and 6 cm pillow in 9–12 years of age group. Macintosh laryngoscope was used for intubation. CL grading and POGO score were assessed. BURP maneuver was given in cases of difficult laryngoscopy of CL grade above 2 and its effect on improvement in CL grading was measured. Results: Group 1 children had significantly more CL grade I (83.3% vs. 16.7%); significantly lesser CL Grade IIb (23.1% vs. 76.9%, P = 0.001); and significantly more POGO score (97% vs. 86%, P < 0.05) than Group 2. The use of BURP maneuver significantly improved the laryngoscopy view in both the groups. Conclusions: The use of intubation pillow in children between 5 and 12 years of age facilitated better laryngoscopy view and POGO score.

Background: Head and neck cancer treatment does not yield desired outcomes for all patients. This investigation aimed to explore the feasibility of predicting treatment outcomes from routine pre-treatment magnetic resonance images (MRIs). Radiomics features were “mined” and used to train machine learning (ML) classifiers to predict treatment outcomes. Moreover, iterative deep texture analysis (DTA) was explored to boost model performances. Methods: Radiomics features were determined from T1-weighted post-contrast MRIs of pathologically involved lymph node (LN) segmentations for n = 63 patients. SVM, k-NN, and FLD classifier models were trained, selecting for 1–10 features. The model with the top balanced accuracy was chosen for an iteration of DTA. New feature sets were used to retrain and test the ML. Radiomics features were explored for a total of three layers through two iterations of DTA. Results: Models proved useful in predicting treatment outcomes. The best model was a nine-feature multivariable k-NN model with a sensitivity (%Sn) of 93%, specificity (%Sp) of 74%, 86% accuracy (%Acc), and 86% precision (%Per). The best model for two of the three classifiers (k-NN and FLD) was trained using features from three layers. The performance of the average k-NN and FLD models trained with features was boosted significantly with the inclusion of deeper-layer features. Conclusions: Pre-treatment LN MRIs contain quantifiable texture information that can be used to train ML models to predict cancer treatment outcomes. Furthermore, DTA proved useful to boosting predictive models.

ObjectiveAttention deficit hyperactivity disorder (ADHD), prevalent in 5% of children worldwide, impacts academic performance and often coexists with psychiatric disorders. Psychostimulant medications are primary treatments for ADHD, enhancing dopamine to reduce symptoms. However, dopamine increase may cause appetite loss. This pioneering study in the United Arab Emirates (UAE) explores psychostimulant effects on weight in children diagnosed with ADHD, aiming to uncover unique regional characteristics and contributing factors to weight changes.MethodsThis retrospective cohort study assessed data from electronic medical records from 2017 to 2022, aiming to assess the impact of psychostimulants on weight in children aged 6–18 years. Inclusion criteria covered psychostimulant-treated and untreated patients with ADHD. Statistical analysis, involving longitudinal data methods aimed to demonstrate significant weight differences.ResultsData from 107 pediatric patients diagnosed with ADHD were analyzed, with 86 meeting inclusion criteria. Most patients were male (80.2%). ADHD presentations varied, and methylphenidate immediate release was the most prescribed stimulant medication. Patients experienced initial weight loss followed by overall gain over 12 months. Coexisting conditions, maternal factors, family history, and correlations with autism spectrum disorder were explored.ConclusionThis study provides valuable insights into the effects of psychostimulant medications on the weight of children and adolescents diagnosed with ADHD in the UAE. It suggests avenues for future research, emphasizing extended follow-ups to understand long-term psychostimulant effects, nuanced examinations of age and gender, and exploring interactions with comorbidities. Despite limitations, the research provides insights into ADHD medication effects, guiding personalized treatment approaches for pediatric populations.

Objective This study examines the emotional state and interpersonal relationships of social media influencers, focusing on the psychological effects of their popularity and engagement. Despite increased awareness of mental health issues, influencers remain underrepresented in research. Barriers such as low mental health literacy, stigma, access issues, and the pressure to maintain virtual personas highlight the need for this investigation. This research addresses these gaps by systematically examining the impact of social media on influencers’ mental health. Methods An online survey was conducted using the positive and negative affect schedule (PANAS) and the Relationship Structures Questionnaire (ECR-RS). The target audience was social media influencers from various countries, with a specific focus on the United Arab Emirates (UAE). Participants were recruited from 2 November 2022, to 1 March 2023, through email, SMS, and direct messages on social media platforms. Statistical analyses included t -tests, ANOVA, and Pearson correlation coefficients. Results A total of 161 social media influencers completed the survey. A significant association was found between extended social media usage and heightened negative emotions among influencers spending more than 5 hours daily on these platforms ( p

Anthocyanins (ACNs) are natural pigments commonly found in plants which contribute to the vibrant colors of fruits, vegetables, and flowers. The present review aims to cover the ACNs field in terms of sources, extraction/purification techniques, as well as characterization methods that are crucial for assessing their medicinal potential and sustainable applications. Characterization methods e.g. HPLC, UPLC-QTOF-MS, MS, and NMR are discussed as analytical tools for the identification and quantification of ACNs in various vegetable matrices. Their antioxidant, anti-inflammatory, antidiabetic, anti-cancer and cardiovascular properties are, also, highlighted. Besides, the use of ACNs as natural colorants, preservatives, and functional ingredients is discussed considering their impact on the food industry. Likewise, due to their anti-aging and skin-protective properties, their employment in the cosmetic field is reported making them appealing for skincare formulations. This review provides a comprehensive overview, emphasizing the ACNs versatility in medicine, food industry, and cosmetic field, fostering future research and innovation.

The field of nanomedicine has attracted much interest and is now serving as the impetus for many revolutionary advances in the pharmaceutical industry.

ObjectiveTo describe the difference in values among medical students from a first-year student’ and final year student’ perspective. In addition, it is designed to report associations and trends between personal values and overall emotional states.MethodsThis is an analytical cross-sectional study that involved disseminating an online survey via email to first and final year students at the College of Medicine in Mohammed Bin Rashid University (MBRU) in Dubai, United Arab Emirates in December of 2023. The survey encompassed queries on demographics, the Life Values Inventory (LVI) and the Positive and Negative Affect Schedule (PANAS).ResultsThe survey was completed by 84 students. About half of the participants were final year medical students (45/84; 53.6%) with the majority being females (70/84; 83.3%). Positive emotions were positively correlated to various life values, including belonging, scientific understanding, responsibility, and achievement (p

V937 is an investigational, genetically unmodified Kuykendall strain of coxsackievirus A21, which has been evaluated in the clinic for advanced solid tumor malignancies. V937 specifically infects and lyses tumor cells that overexpress intercellular adhesion molecule-1 (ICAM-1). Intratumoral V937 as a monotherapy and in combination with anti-PD-1 antibody pembrolizumab has shown clinical response in patients with metastatic melanoma, which overexpresses ICAM-1. Here, we investigate in preclinical studies the potential bidirectional cross-talk between hepatocellular carcinomas (HCC) or colorectal carcinomas (CRC) and immune cells when treated with V937 alone or in combination with pembrolizumab. We show that while V937 treatment of tumor cell lines or organoids or peripheral blood mononuclear cells (PBMCs) alone induced a minimal immunological response, V937 treatment of non-contact co-cultures of tumor cell lines or CRC organoids with PBMCs led to robust production of proinflammatory cytokines and immune cell activation. In addition, both recombinant interferon-gamma and pembrolizumab increased ICAM-1 on tumor cell lines or organoids and, in turn, amplified V937-mediated oncolysis and immunogenicity. These findings provide critical mechanistic insights on the cross-talk between V937-mediated oncolysis and immune responses, demonstrating the therapeutic potential of V937 in combination with PD-1 blockade to treat immunologically quiescent cancers.

(1) Background: Some cancer patients do not experience tumour shrinkage but are still at risk of experiencing unwanted treatment side effects. Radiomics refers to mining biomedical images to quantify textural characterization. When radiomics features are labelled with treatment response, retrospectively, they can train predictive machine learning (ML) models. (2) Methods: Radiomics features were determined from lymph node (LN) segmentations from treatment-planning CT scans of head and neck (H&N) cancer patients. Binary treatment outcomes (complete response versus partial or no response) and radiomics features for n = 71 patients were used to train support vector machine (SVM) and k-nearest neighbour (k-NN) classifier models with 1–7 features. A deep texture analysis (DTA) methodology was proposed and evaluated for second- and third-layer radiomics features, and models were evaluated based on common metrics (sensitivity (%Sn), specificity (%Sp), accuracy (%Acc), precision (%Prec), and balanced accuracy (%Bal Acc)). (3) Results: Models created with both classifiers were found to be able to predict treatment response, and the results suggest that the inclusion of deeper layer features enhanced model performance. The best model was a seven-feature multivariable k-NN model trained using features from three layers deep of texture features with %Sn = 74%, %Sp = 68%, %Acc = 72%, %Prec = 81%, %Bal Acc = 71% and with an area under the curve (AUC) the receiver operating characteristic (ROC) of 0.700. (4) Conclusions: H&N Cancer patient treatment-planning CT scans and LN segmentations contain phenotypic information regarding treatment response, and the proposed DTA methodology can improve model performance by enhancing feature sets and is worth consideration in future radiomics studies.

Abstract Locally advanced breast cancer (LABC) is a severe type of cancer with a poor prognosis, despite advancements in therapy. As the disease is often inoperable, current guidelines suggest upfront aggressive neoadjuvant chemotherapy (NAC). Complete pathological response to chemotherapy is linked to improved survival, but conventional clinical assessments like physical exams, mammography, and imaging are limited in detecting early response. Early detection of tissue response can improve complete pathological response and patient survival while reducing exposure to ineffective and potentially harmful treatments. A rapid, cost-effective modality without the need for exogenous contrast agents would be valuable for evaluating neoadjuvant therapy response. Conventional ultrasound provides information about tissue echogenicity, but image comparisons are difficult due to instrument-dependent settings and imaging parameters. Quantitative ultrasound (QUS) overcomes this by using normalized power spectra to calculate quantitative metrics. This study used a novel transfer learning-based approach to predict LABC response to neoadjuvant chemotherapy using QUS imaging at pre-treatment. Using data from 174 patients, QUS parametric images of breast tumors with margins were generated. The ground truth response to therapy for each patient was based on standard clinical and pathological criteria. The Residual Network (ResNet) deep learning architecture was used to extract features from the parametric QUS maps. This was followed by SelectKBest and Synthetic Minority Oversampling (SMOTE) techniques for feature selection and data balancing, respectively. The Support Vector Machine (SVM) algorithm was employed to classify patients into two distinct categories: nonresponders (NR) and responders (RR). Evaluation results on an unseen test set demonstrate that the transfer learning-based approach using spectral slope parametric maps had the best performance in the identification of nonresponders with precision, recall, F1-score, and balanced accuracy of 100, 71, 83, and 86%, respectively. The transfer learning-based approach has many advantages over conventional deep learning methods since it reduces the need for large image datasets for training and shortens the training time. The results of this study demonstrate the potential of transfer learning in predicting LABC response to neoadjuvant chemotherapy before the start of treatment using quantitative ultrasound imaging. Prediction of NAC response before treatment can aid clinicians in customizing ineffectual treatment regimens for individual patients.

AbstractIn this paper, we obtain the Italian domination number, perfect Italian domination number and double Roman domination number of generalized Sierpiński graph [Formula: see text] where G is a cycle Cn, [Formula: see text] a complete bipartite graph [Formula: see text] or [Formula: see text] and a bistar [Formula: see text]

Cumene is a precursor for producing many organic chemicals and is thinner in paints and lacquers. Its production process involves one of the large-scale manufacturing processes with complex kinetics. Different classical control strategies have been implemented and compared in this process for the cumene reactor. As a system with large degrees of freedom, a novel approach for extracting the state space model from the COMSOL Multiphysics implementation of the system is adopted here. Internal Modern Control (IMC) based PI and PID controllers are derived for the system. The system is reduced to the FOPDT and SOPDT model structure to derive the controller setting using Skogestad half rules. The integral time is modified for excellent set point tracking and faster disturbance rejection. From the analysis, it can be stated that the PI controller suits more for this specific process. The particle swarm optimization (PSO) algorithm, an evolutionary computation technique, is also used to tune the PI settings. The PI controllers with IMC, Zeigler Nichols, and PSO tuning are compared, and it can be concluded that the PSO PI controller settles at 45 s without any oscillations and settles down faster for the disturbance of magnitude 0.5 applied at t = 800 s. However, it is computationally intensive compared to other controller strategies.

Abstract The purpose of this study was to investigate the performances of the tumor response prediction prior to neoadjuvant chemotherapy based on quantitative ultrasound, tumour core-margin, texture derivative analyses, and molecular parameters in a large cohort of patients (n = 208) with locally advanced and earlier-stage breast cancer and combined them to best determine tumour responses with machine learning approach. Two multi-features response prediction algorithms using a k-nearest neighbour and support vector machine were developed with leave-one-out and hold-out cross-validation methods to evaluate the performance of the response prediction models. In a leave-one-out approach, the quantitative ultrasound-texture analysis based model attained good classification performance with 80% of accuracy and AUC of 0.83. Including molecular subtype in the model improved the performance to 83% of accuracy and 0.87 of AUC. Due to limited number of samples in the training process, a model developed with a hold-out approach exhibited a slightly higher bias error in classification performance. The most relevant features selected in predicting the response groups are core-to-margin, texture-derivative, and molecular subtype. These results imply that that baseline tumour-margin, texture derivative analysis methods combined with molecular subtype can potentially be used for the prediction of ultimate treatment response in patients prior to neoadjuvant chemotherapy.

Background/Objectives: Phloroglucinol (PHL), a phenolic compound extracted from the brown alga Rosenvingea intricata, exhibits potent antioxidant and anticancer properties. This study aims to extract, purify, and characterize PHL, and further develop functionalized zinc oxide nanoparticles (ZnO NPs) loaded with PHL to enhance its therapeutic potential. Methods: PHL was extracted using acetone and purified through Sephadex LH-20 column chromatography, yielding a highly enriched fraction (F-3). The purified compound was characterized by FTIR, HPLC, NMR, and LC-MS. ZnO NPs were synthesized, PEGylated, and conjugated with PHL, forming ZnO-PEG-PHL NPs. Their characterization included DLS, zeta potential, XRD, SEM-EDAX, and encapsulation efficiency studies. Antioxidant assays (DPPH, FRAP, ABTS, RPA) were performed and in vitro cytotoxicity on A549 lung cancer cells were determined to evaluate the therapeutic efficacy of PHL. Results: The purified PHL fraction showed a high phenolic content (45.65 PHL mg/g), which was was confirmed by spectral analysis. The ZnO-PEG-PHL NPs increased in size from 32.36 nm to 46.68 nm, with their zeta potential shifting from −37.87 mV to −26.82 mV. The antioxidant activity was superior for the ZnO-PEG-PHL NPs in all assays, while the in vitro cytotoxicity tests showed an IC50 of 40 µg/mL compared to 60 µg/mL for the ZnO NPs and 70 µg/mL for PHL. Apoptotic studies revealed significant cell cycle arrest and apoptosis induction. Conclusions: The synthesized ZnO-PEG-PHL NPs demonstrated enhanced antioxidant and anticancer properties, making them promising candidates for cancer therapy and antioxidant applications.

Abstract Objective Clinical and genetic heterogeneities make diagnosis of limb‐girdle muscular dystrophy (LGMD) and other overlapping disorders of muscle weakness complicated and expensive. We aimed to develop a comprehensive next generation sequence‐based multi‐gene panel (“The Lantern Focused Neuromuscular Panel”) to detect both sequence variants and copy number variants in one assay. Methods Patients with clinical diagnosis of LGMD or other overlapping muscular dystrophies in the United States were tested by PerkinElmer Genomics in 2018–2021 via “The Lantern Project,” a sponsored diagnostic testing program. Sixty‐six genes related to LGMD subtypes‐ and other myopathies were investigated. Main outcomes were diagnostic yield, gene‐variant spectrum, and LGMD subtypes' prevalence. Results Molecular diagnosis was established in 19.6% (1266) of 6473 cases. Major genes contributing to LGMD were identified including CAPN3 (5.4%, 68), DYSF (4.0%, 51), GAA (3.7%, 47), ANO5 (3.6%, 45), and FKRP (2.7%, 34). Genes of other overlapping MD subtypes identified included PABPN1 (10.5%, 133), VCP (2.2%, 28), MYOT (1.2% 15), LDB3 (1.0%, 13), COL6A1 (1.5%, 19), FLNC (1.1%, 14), and DNAJB6 (0.8%, 10). Different sizes of copy number variants including single exon, multi‐exon, and whole genes were identified in 7.5% (95) cases in genes including DMD, EMD, CAPN3, ANO5, SGCG, COL6A2, DOK7, and LAMA2. Interpretation “The Lantern Focused Neuromuscular Panel” enables identification of LGMD subtypes and other myopathies with overlapping clinical features. Prevalence of some MD subtypes was higher than previously reported. Widespread deployment of this comprehensive NGS panel has the potential to ensure early, accurate diagnosis as well as re‐define MD epidemiology.

The gap between water supply and demand is increasing in several urban clusters of the world. This study uses the water evaluation and planning model to assess the water supply and demand dynamics in one of the large metropolitan regions of the Chennai hydrological basin. The primary water supply sources, including reservoirs, groundwater, inter-basin transfer, and desalination plants, were integrated into the model to simulate the current and future water demand and supply scenario. Three rainfall scenarios (excess, normal, and deficit) were utilized to assess their impacts on water supply. The study highlights the increase in unmet demand for normal and deficit rainfall scenarios. In response, various mitigation options were explored, including increasing groundwater recharge, reservoir capacity enhancement, water treatment plant expansion, additional storage, and utilization of water stored in rock quarries. The findings provide valuable insights for policymakers and stakeholders to develop sustainable water management strategies in the Chennai Basin. HIGHLIGHTS Water supply-demand gap keeps increasing in Chennai Basin, highlighting the need for proactive planning and efficient management.; Expanding desalination plants, increasing water treatment capacity, and increasing reservoir capacity through desilting and utilizing quarries can enhance water availability.; Groundwater recharge measures and alternative measures are crucial to bridge the supply-demand gap in the Chennai Basin.;

In this research article we report the potentials of chitin-based silver nanoparticles (chitin AgNPs) derived from Indian mimic goatfish (Mulloidichthys ayliffe) scales as an effective food preservation agent. The study comprehensively presents the multifaceted attributes of chitin AgNPs, including their synthesis, characterization, and antimicrobial properties. Chitin yield from M. ayliffe scales and three-spot swimming crab (P. sanguinolentus) exoskeleton was determined, with the insoluble content quantified. FTIR analysis unveiled distinct absorption peaks for chitin, and scanning electron microscopy revealed the ultrastructure of chitin from both the sources. Using UV–visible spectroscopy, the biosynthesis of AgNPs was accomplished and characterized, with the color shift of the solution serving as proof of a successful synthesis. UV–vis spectra provided insights into nanoparticle size and shape. SEM micrographs exhibited spherical particle morphology, while FTIR spectra indicated amino group interactions contributing to AgNP stabilization. The antimicrobial potential of chitin AgNPs was assessed against the food pathogen, Vibrio spp. Chitin films displayed significant antimicrobial activity, particularly AgNP-synthesized chitin from M. ayliffe scales, demonstrated the highest Vibrio spp. inhibition activity. Furthermore, chitin AgNPs were incorporated into the common chili, Capsicum annuum and the tomato, Solanum lycopersicum to extend their shelf life at room temperature. This study reveals the efficacy of chitin AgNPs from M. ayliffe scales as potent agents for food preservation, offering insights into their physical, mechanical, and antimicrobial attributes. The application of chitin AgNPs to perishable food items highlights their potential in enhancing shelf life and quality, opening innovative avenues for sustainable food preservation.

The development of hand gesture recognition systems has gained more attention in recent days, due to its support of modern human-computer interfaces. Moreover, sign language recognition is mainly developed for enabling communication between deaf and dumb people. In conventional works, various image processing techniques like segmentation, optimization, and classification are deployed for hand gesture recognition. Still, it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption, increased false positives, error rate, and misclassification outputs. Hence, this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques. During image segmentation, skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion. Then, the Heuristic Manta-ray Foraging Optimization (HMFO) technique is employed for optimally selecting the features by computing the best fitness value. Moreover, the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate. Finally, an Adaptive Extreme Learning Machine (AELM) based classification technique is employed for predicting the recognition output. During results validation, various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.

Human Action Recognition (HAR) attempts to recognize the human action from images and videos. The major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments. A novel action descriptor is proposed in this study, based on two independent spatial and spectral filters. The proposed descriptor uses a Difference of Gaussian (DoG) filter to extract scale-invariant features and a Difference of Wavelet (DoW) filter to extract spectral information. To create a composite feature vector for a particular test action picture, the Discriminant of Guassian (DoG) and Difference of Wavelet (DoW) features are combined. Linear Discriminant Analysis (LDA), a widely used dimensionality reduction technique, is also used to eliminate duplicate data. Finally, a closest neighbor method is used to classify the dataset. Weizmann and UCF 11 datasets were used to run extensive simulations of the suggested strategy, and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform well. The average accuracy of DoG + DoW is observed as 83.6635% while the average accuracy of Discrinanat of Guassian (DoG) and Difference of Wavelet (DoW) is observed as 80.2312% and 77.4215%, respectively. The average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG + DoW is observed as 62.5231% while the average accuracy of Difference of Guassian (DoG) and Difference of Wavelet (DoW) is observed as 60.3214% and 58.1247%, respectively. From the above accuracy observations, the accuracy of Weizmann is high compared to the accuracy of UCF 11, hence verifying the effectiveness in the improvisation of recognition accuracy.

Georgios D Panos,1,2 Arun Lakshmanan,1 Panagiotis Dadoukis,3 Matteo Ripa,4 Lorenzo Motta,4 Winfried M Amoaku1,2 1Department of Ophthalmology, Queen’s Medical Centre, Nottingham University Hospitals, Nottingham, UK; 2Division of Ophthalmology and Visual Sciences, Faculty of Medicine & Health Sciences, School of Medicine, University of Nottingham, Nottingham, UK; 3Eye Treatment Centre, Whipps Cross University Hospital, Barts Health NHS Trust, London, UK; 4Department of Ophthalmology, William Harvey Hospital, East Kent Hospitals University NHS Foundation Trust, Kent, UKCorrespondence: Georgios D Panos, Department of Ophthalmology, Queen’s Medical Centre, Derby Road, Lenton, Nottingham, NG7 2UH, UK, Tel +44 115 924 9924, Email gdpanos@gmail.com Lorenzo Motta, Department of Ophthalmology, William Harvey Hospital, Kennington Road, Willesborough, Ashford, Kent, TN24 0LZ, UK, Tel +44 1233 633331, Email lorenzo.motta@nhs.netAbstract: Degenerative eye conditions such as age-related macular degeneration (AMD), diabetic retinopathy, and retinal vein occlusion are major contributors to significant vision loss in developed nations. The primary therapeutic approach for managing complications linked to these diseases involves the intravitreal delivery of anti-vascular endothelial growth factor (VEGF) treatments. Faricimab is a novel, humanised, bispecific antibody that simultaneously binds all VEGF-A isoforms and Angiopoietin-2, which has been approved by regulatory agencies, such as the US Food and Drug Administration (FDA), the UK Medicines and Healthcare products Regulatory Agency (MHRA) and the European Medicines Agency (EMA), for the treatment of neovascular AMD and diabetic macular oedema (DMO). Intravitreal faricimab holds the promise of reducing the treatment burden for patients with these conditions by achieving comparable or superior therapeutic outcomes with fewer clinic visits. The scope of faricimab’s application includes addressing complex macular conditions such as DMO. This review intends to elucidate the distinctive pharmacological characteristics of faricimab and provide an overview of the key clinical trials and real-world studies that assess its effectiveness and safety in treating degenerative macular diseases.Keywords: degenerative macular disorders, faricimab, anti-VEGF, efficacy, safety, intravitreal treatment, age-related macular degeneration, diabetic macular oedema, retinal vein occlusion, diabetic retinopathy

A device that transforms chemical energy into electrical energy is an electrochemical cell. The reaction type inside the cell determines whether it is exothermic or endothermic. This paper discusses the mathematical modelling of exothermic explosions in a slab. This model is based on a nonlinear equation containing a nonlinear term related to Arrhenius, bimolecular, and sensitised laws of reaction kinetics. The absolute temperature can be derived by solving the nonlinear equation using the Akbari–Ganji technique. The mathematical model also numerically solved and simulated in the MATLAB® v2016b software. The new simple theoretical result is validated with previously identified analytical and numerical findings. The influence of the parameters of Frank-Kamenetskii number, activation energy and the numerical exponent on temperature is discussed. The Frank-Kamenetskii number is observed to drop as the temperature is found to decrease, while the activation energy parameter is shown to increase. The numerical exponent has little or no effect on the temperature. An extension of this model to cylinder and sphere geometry is also provided.

Abstract Computational drug repositioning methods have emerged as an attractive and effective solution to find new candidates for existing therapies, reducing the time and cost of drug development. Repositioning methods based on biomedical knowledge graphs typically offer useful supporting biological evidence. This evidence is based on reasoning chains or subgraphs that connect a drug to a disease prediction. However, there are no databases of drug mechanisms that can be used to train and evaluate such methods. Here, we introduce the Drug Mechanism Database (DrugMechDB), a manually curated database that describes drug mechanisms as paths through a knowledge graph. DrugMechDB integrates a diverse range of authoritative free-text resources to describe 4,583 drug indications with 32,249 relationships, representing 14 major biological scales. DrugMechDB can be employed as a benchmark dataset for assessing computational drug repositioning models or as a valuable resource for training such models.

An electrochemical photobioreactor with a packed bed containing transparent gel granules and immobilized photosynthetic bacterial cells is shown with a one-dimensional two-phase flow and transport model. We consider the biological/chemical events in the electrochemical photobioreactor, the intrinsically connected two-phase flow and mass transport, and other factors. This model is based on a system of nonlinear equations. This paper applies Akbari-Ganji’s and Taylor series methods to find analytical solutions to nonlinear differential equations that arise in an immobilized-cell electrochemical photobioreactor. Approximate analytical expressions of the concentration of glucose and hydrogen are obtained in liquid and gas phases for different parameter values. Numerical simulations are presented to validate the theoretical investigations.

BackgroundPreclinical studies have demonstrated that tumour cell death can be enhanced 10- to 40-fold when radiotherapy is combined with focussed ultrasound-stimulated microbubble (FUS-MB) treatment. The acoustic exposure of microbubbles (intravascular gas microspheres) within the target volume causes bubble cavitation, which induces perturbation of tumour vasculature and activates endothelial cell apoptotic pathways responsible for the ablative effect of stereotactic body radiotherapy. Subsequent irradiation of a microbubble-sensitised tumour causes rapid increased tumour death. The study here presents the mature safety and efficacy outcomes of magnetic resonance (MR)-guided FUS-MB (MRgFUS-MB) treatment, a radioenhancement therapy for breast cancer.Methods and findingsThis prospective, single-center, single-arm Phase 1 clinical trial included patients with stages I-IV breast cancer with in situ tumours for whom breast or chest wall radiotherapy was deemed adequate by a multidisciplinary team (clinicaltrials.gov identifier: NCT04431674). Patients were excluded if they had contraindications for contrast-enhanced MR or microbubble administration. Patients underwent 2 to 3 MRgFUS-MB treatments throughout radiotherapy. An MR-coupled focussed ultrasound device operating at 800 kHz and 570 kPa peak negative pressure was used to sonicate intravenously administrated microbubbles within the MR-guided target volume. The primary outcome was acute toxicity per Common Terminology Criteria for Adverse Events (CTCAE) v5.0. Secondary outcomes were tumour response at 3 months and local control (LC). A total of 21 female patients presenting with 23 primary breast tumours were enrolled and allocated to intervention between August/2020 and November/2022. Three patients subsequently withdrew consent and, therefore, 18 patients with 20 tumours were included in the safety and LC analyses. Two patients died due to progressive metastatic disease before 3 months following treatment completion and were excluded from the tumour response analysis. The prescribed radiation doses were 20 Gy/5 fractions (40%, n = 8/20), 30 to 35 Gy/5 fractions (35%, n = 7/20), 30 to 40 Gy/10 fractions (15%, n = 3/20), and 66 Gy/33 fractions (10%, n = 2/20). The median follow-up was 9 months (range, 0.3 to 29). Radiation dermatitis was the most common acute toxicity (Grade 1 in 16/20, Grade 2 in 1/20, and Grade 3 in 2/20). One patient developed grade 1 allergic reaction possibly related to microbubbles administration. At 3 months, 18 tumours were evaluated for response: 9 exhibited complete response (50%, n = 9/18), 6 partial response (33%, n = 6/18), 2 stable disease (11%, n = 2/18), and 1 progressive disease (6%, n = 1/18). Further follow-up of responses indicated that the 6-, 12-, and 24-month LC rates were 94% (95% confidence interval [CI] [84%, 100%]), 88% (95% CI [75%, 100%]), and 76% (95% CI [54%, 100%]), respectively. The study's limitations include variable tumour sizes and dose fractionation regimens and the anticipated small sample size typical for a Phase 1 clinical trial.ConclusionsMRgFUS-MB is an innovative radioenhancement therapy associated with a safe profile, potentially promising responses, and durable LC. These results warrant validation in Phase 2 clinical trials.Trial registrationclinicaltrials.gov, identifier NCT04431674.

BackgroundIn patients with locally advanced breast cancer (LABC) receiving neoadjuvant chemotherapy (NAC), quantitative ultrasound (QUS) radiomics can predict final responses early within 4 of 16-18 weeks of treatment. The current study was planned to study the feasibility of a QUS-radiomics model-guided adaptive chemotherapy.MethodsThe phase 2 open-label randomized controlled trial included patients with LABC planned for NAC. Patients were randomly allocated in 1:1 ratio to a standard arm or experimental arm stratified by hormonal receptor status. All patients were planned for standard anthracycline and taxane-based NAC as decided by their medical oncologist. Patients underwent QUS imaging using a clinical ultrasound device before the initiation of NAC and after the 1st and 4th weeks of treatment. A support vector machine-based radiomics model developed from an earlier cohort of patients was used to predict treatment response at the 4th week of NAC. In the standard arm, patients continued to receive planned chemotherapy with the treating oncologists blinded to results. In the experimental arm, the QUS-based prediction was conveyed to the responsible oncologist, and any changes to the planned chemotherapy for predicted non-responders were made by the responsible oncologist. All patients underwent surgery following NAC, and the final response was evaluated based on histopathological examination.ResultsBetween June 2018 and July 2021, 60 patients were accrued in the study arm, with 28 patients in each arm available for final analysis. In patients without a change in chemotherapy regimen (53 of 56 patients total), the QUS-radiomics model at week 4 of NAC that was used demonstrated an accuracy of 97%, respectively, in predicting the final treatment response. Seven patients were predicted to be non-responders (observational arm (n=2), experimental arm (n=5)). Three of 5 non-responders in the experimental arm had chemotherapy regimens adapted with an early initiation of taxane therapy or chemotherapy intensification, or early surgery and ended up as responders on final evaluation.ConclusionThe study demonstrates the feasibility of QUS-radiomics adapted guided NAC for patients with breast cancer. The ability of a QUS-based model in the early prediction of treatment response was prospectively validated in the current study.Clinical trial registrationclinicaltrials.gov, ID NCT04050228.

The incorporation of carbon materials in batteries serves to enhance its performance by improving conductivity, achieving uniform active material distribution, increasing capacity, mitigating sulfation, extending cycle life, and considering potential environmental benefits. Even though several possible mechanisms were reported, how exactly carbon works is not fully understood. In the present study a new form of carbon black was prepared from Ageratina adenophora (CBAa) and investigated for its impact on the electrical conductivity of the negative active material in 2 V lead acid cell. The performance was compared with other commercially available carbons like Graphite PG-10, Carbon N550, Carbon N330 and Carbon Vulcan. The carbon was characterised by XRD, SEM and grain size analysis. The initial capacity of the cell was consistently higher and remained stable at 4.6 W∙h; in the life cycle analysis, the cells showed 290 cycles. The post-life cycle test analysis showed that only a white layer on multiple plates indicating the onset of sulfation and there is no corrosion. The performance of the CBAa prepared in the present work was found to be better when compared with the commercially available carbons.

Chitosan is the second most abundant bio-polymer available in the world, second only to cellulose. It is found in crustaceous shells, e.g., those of crabs, shrimps, prawns, and fungi, as well as insect exoskeletons. The use of nanoformulations for the management of pests and diseases is receiving increased interest with the advancement of nanotechnology. Here, chitosan nanospheres were obtained from chitosan using the ionic gelation technique. The nanoformulations obtained were characterized using a particle size analyzer, Fourier transform infrared spectroscopy, and a transmission electron microscope. The efficacy of chitosan nanospheres in suppressing the root-knot nematode Meloidogyne incognita was studied. The particle size of nanospheres formulated for this study was 380.2 nm, with a polydispersity index (PI) of 0.4 and Zeta potential of 45.7 or 50.9 mV at pH 5.2. The chitosan nanospheres were spherical and the particles did not agglomerate. FTIR spectra of the chitosan nanospheres peaked at 3334 cm−1, thereby indicating the stretching of the OH and NH group. In In-vitro studies, chitosan nanospheres showed significant nematicidal activity against M. incognita. Under pot culture conditions, chitosan nanospheres (1%- active compound chitosan) at 2ml/plant decreased the nematode population in roots or soil. Compared to the control, the number of galls was reduced by 83.68%, the number of egg masses by 83.85%, the number of adult females by 66.56%, and the number of second-stage juveniles by 73.20%. In a field experiment, application of chitosan nanospheres (1%) was followed by a 18.75% increase in fruit yield compared to the non-treated control.

Objectives: In medical research, the study’s design and statistical methods are pivotal, as they guide interpretation and conclusion. Selecting appropriate statistical models hinges on the distribution of the outcome measure. Count data, frequently used in medical research, often exhibit over-dispersion or zero inflation. Occasionally, count data are considered ordinal (with a maximum outcome value of 5), and this calls for the application of ordinal regression models. Various models exist for analyzing over-dispersed data such as negative binomial, generalized Poisson (GP), and ordinal regression model. This study aims to examine whether the GP model is a superior alternative to the ordinal logistic regression (OLR) model, specifically in the context of zero-inflated Poisson models using both simulated and real-time data. Methods: Simulated data were generated with varied estimates of regression coefficients, sample sizes, and various proportions of zeros. The GP and OLR models were compared using fit statistics. Additionally, comparisons were made using real-time datasets. Results: The simulated results consistently revealed lower bias and mean squared error values in the GP model compared to the OLR model. The same trend was observed in real-time datasets, with the GP model consistently demonstrating lower standard errors. Except when the sample size was 1000 and the proportions of zeros were 30% and 40%, the Bayesian information criterion consistently favored the GP model over the OLR model. Conclusions: This study establishes that the proposed GP model offers a more advantageous alternative to the OLR model. Moreover, the GP model facilitates easier modeling and interpretation when compared to the OLR model.

Purpose The aim of this study was to recycle and reuse the enormously available unutilized lignocellulosic solid organic waste resource, cashew leaf litter (CLL) admixed with various animal dungs, cowdung, sheepdung and horsedung by employing predominantly available indigenous epigeic earthwormâPerionyx excavatus (Perrier, 1872) and produce quality vermifertilizer. Methods Four different combinations of each [(100 % dung alone, 3:1 (75 % dung ? 25 % CLL), 2:2 (50 % dung ? 50 % CLL) and 1:3 (25 % dung ? 75 % CLL)] vermibeds were allowed for vermicomposting process under laboratory conditions. After 60 days, the worm worked vermicompost and worm unworked normal compost were harvested and characterized. The earthworm activityâgrowth, reproductive performance (cocoon production and hatchling number) and recovery of vermicompost was also studied. Results The obtained results clearly showed that vermicompost from CLL admixed with cowdung at 2:2 ratio had lower pH, organic carbon, CâN ratio, CâP ratio, lignin, cellulose, hemicellulose and phenol content, and higher nitrogen, phosphorus, potassium dehydrogenase and humic acid content than the raw substrates and worm unworked normal compost. In addition, pronounced and better earthworm activity was found in the above combination. Conclusion Through vermitechnology way of producing agronomic valid vermicompost using natural waste resources like CLL and animal dungs can be used as bioorganic fertilizer. These vermiresources have vast and diversified potential for maintaining sustainable soil health, fertility, productivity, waste degradation, soil reclamation, land restoration practices and environment health.

Abstract High intensity focused ultrasound (HIFU) systems have been approved for therapeutic ultrasound delivery to cause tissue ablation or induced hyperthermia. Microbubble agents have also been used in combination with sonication exposures. These require temperature feedback and monitoring to prevent unstable cavitation and prevent excess tissue heating. Previous work has utilized lower power and pressure to oscillate microbubbles and transfer energy to endothelial cells in the absence of thermally induced damage that can radiosensitize tumors. This work investigated whether reduced acoustic power and pressure on a commercial available MR-integrated HIFU system could result in enhanced radiation-induced tumor response after exposure to ultrasound-stimulated microbubbles (USMB) therapy. A commercially available MR-integrated HIFU system was used with a hyperthermia system calibration provided by the manufacturer. The ultrasound transducer was calibrated to reach a peak negative pressure of − 750 kPa. Thirty male New Zealand white rabbits bearing human derived PC3 tumors were grouped to receive no treatment, 14 min of USMB, 8 Gy of radiation in a separate irradiation cabinet, or combined treatments. In vivo temperature changes were collected using MR thermometry at the tumor center and far-field muscle region. Tissues specimens were collected 24 h post radiation therapy. Tumor cell death was measured and compared to untreated controls through hematoxylin and eosin staining and immunohistochemical analysis. The desired peak negative pressure of − 750 kPa used for previous USMB occurred at approximately an input power of 5 W. Temperature changes were limited to under 4 °C in ten of twelve rabbits monitored. The median temperature in the far-field muscle region of the leg was 2.50 °C for groups receiving USMB alone or in combination with radiation. Finally, statistically significant tumor cell death was demonstrated using immunohistochemical analysis in the combined therapy group compared to untreated controls. A commercial MR-guided therapy HIFU system was able to effectively treat PC3 tumors in a rabbit model using USMB therapy in combination with radiation exposures. Future work could find the use of reduced power and pressure levels in a commercial MR-guided therapy system to mechanically stimulate microbubbles and damage endothelial cells without requiring high thermal doses to elicit an antitumor response.

The Tamil Nadu state of India possesses a vast coastline of 1076 ​km and is highly vulnerable to many climate change-induced natural hazards. It is essential to evaluate the elements at risk to the coastal hazards and map their spatial variations for the densely populated Tamil Nadu coast. Most of the disaster risk studies had concentrated much on delineating hazard-prone regions based on the overall analysis of multiple parameters. For the first time, this study estimated the risk for built-up areas at the micro-administrative level for the northern coastal plains of Tamil Nadu. The built-up areas in the coastal stretch (within 40 ​m from MSL) were delineated and categorised based on their density and structure. Also, we conducted a focus group survey from the deprived communities in the study area. Our result shows that out of 1848 sq. km. built-up area in the study region, 127 sq. km. fall under the very high category of the impacts of six significant natural hazards: cyclones, storm surges, Tsunami, floods, earthquakes, and shoreline erosion. Our results show that the built-up areas in Chennai, Pondicherry, Karaikal, and towns/villages of Cuddalore and Nagapattinam are at very high to high multi-hazard risks. Since the present study focuses on the built-up damage at the micro-level, the results can be upscaled and used for local and state-level planning strategies.

Eye care programs, in developing countries, are often planned using the prevalence of blindness and visual impairment, often estimated from Rapid Assessment of Avoidable Blindness (RAAB) surveys. A limitation of this planning approach is that it ignores the annual overall eye care requirements for a given population. Moreover, targets set are arbitrary, often influenced by capacity rather than need. To address this lacunae, we implemented a novel study design to estimate the annual need for comprehensive eye care in a 1.2 million populations. We conducted a population-based longitudinal study in Theni district, Tamil Nadu, India. All permanent residents of all ages were included. We conducted the study in three phases, (i) household-level enumeration and enrollment, (ii) basic eye examination (BEE) at household one-year post-enrollment, and (iii) assessment of eye care utilization and full eye examination (FEE) at central locations. All people aged 40 years and above were invited to the FEE. Those aged

Background: In India, heart failure (HF) is one of the most prevalent chronic diseases. Caregivers are essential and play an important role in bridging the gap in providing care to HF patients. Caregivers' demanding and difficult roles can result in a significant burden, which may lead to stress and symptoms of depression. Aim and Objectives: The objective of the study is to assess the effectiveness of structured counseling on caregiver burden, stress, and depression among caregivers of HF patients. Materials and Methods: Sixty caregivers of patients with HF were selected using consecutive sampling and divided into control and experimental groups. A one-on-one, 30-min structured counseling session was delivered telephonically to caregivers of HF patients. A pretest was taken before the intervention and posttest 1 month later, through telephone, on caregiver burden, stress, and depression using the caregiver burden interview, Perceived Stress Scale, and Patient Health Questionnaire-9. Results: The experimental group showed a significant reduction in caregiver burden (P = 0.001), stress (P = 0.001), and depression (P = 0.001) scores at 1 month. Female caregivers showed a significantly higher caregiver burden (P = 0.046) and depression (P = 0.019), and nuclear families showed a significantly higher caregiver burden (P = 0.016) and depression (P = 0.027). There was a significant positive correlation between stress and the presence of comorbidities (P = 0.021) and depression and the presence of comorbidities (P = 0.042). Conclusions: Structured counseling was effective in reducing caregiver burden and decreasing stress and depression in caregivers of HF patients.

Background : Individuals with Down syndrome (DS) have a 40%–60% chance of being born with congenital heart disease (CHD). This indicates that CHD in individuals with DS is not solely caused by trisomy 21, and there may be other genetic factors contributing to the development of CHD in these children. A study has identified variants in the specific genes that contribute to the pathogenesis of CHD in children with DS, isolated DS, and the CHD group. Computational studies on these identified variants, which, together with trisomy 21, determine the risk for CHD in DS cases, were limited. Here, we aimed to identify the impact of the identified variants that contribute to the pathogenesis of CHD in children with DS through in silico prediction, molecular modeling, and dynamics studies. Methodology and Results : The target single-nucleotide polymorphisms included in the study were examined for pathogenicity, residue conservation, and protein structural changes. The structural predictions were done using I-TASSER, Robetta, SWISS-MODEL, and Phyre2 tools. Further, the predicted models were validated through the PROCHECK server and molecular dynamics simulation using GROMACS software. The conservation analysis conducted on the identified variant highlights its significance in relation to the genetic disorders. Furthermore, a dynamics simulation study revealed the impact of the variant on protein structural stability (≤3 Å), providing valuable insights into its pathogenicity. We have also observed that the structure of the centrosomal protein of 290 kDa gene is relatively unstable, which may be attributed to its exclusive inclusion of helices within its secondary structural components. Conclusions : This computational study explores, for the first time, the association between genes and CHD-DS, evaluating the identified specific frameshift variants. The observed pathogenic mutations in CHD-DS patients require further experimental validation and may contribute to the development of prospective drug design research. The insights gained from the structural and functional implications of these variants could potentially serve as a cornerstone in the development of effective treatments for this debilitating condition.

AimCancer treatments with radiation present a challenging physical toll for patients, which can be justified by the potential reduction in cancerous tissue with treatment. However, there remain patients for whom treatments do not yield desired outcomes. Radiomics involves using biomedical images to determine imaging features which, when used in tandem with retrospective treatment outcomes, can train machine learning (ML) classifiers to create predictive models. In this study we investigated whether pre-treatment imaging features from index lymph node (LN) quantitative ultrasound (QUS) scans parametric maps of head & neck (H&N) cancer patients can provide predictive information about treatment outcomes.Methods72 H&N cancer patients with bulky metastatic LN involvement were recruited for study. Involved bulky neck nodes were scanned with ultrasound prior to the start of treatment for each patient. QUS parametric maps and related radiomics texture-based features were determined and used to train two ML classifiers (support vector machines (SVM) and k-nearest neighbour (k-NN)) for predictive modeling using retrospectively labelled binary treatment outcomes, as determined clinically 3-months after completion of treatment. Additionally, novel higher-order texture-of-texture (TOT) features were incorporated and evaluated in regards to improved predictive model performance.ResultsIt was found that a 7-feature multivariable model of QUS texture features using a support vector machine (SVM) classifier demonstrated 81% sensitivity, 76% specificity, 79% accuracy, 86% precision and an area under the curve (AUC) of 0.82 in separating responding from non-responding patients. All performance metrics improved after implementation of TOT features to 85% sensitivity, 80% specificity, 83% accuracy, 89% precision and AUC of 0.85. Similar trends were found with k-NN classifier.ConclusionBinary H&N cancer treatment outcomes can be predicted with QUS texture features acquired from index LNs. Prediction efficacy improved by implementing TOT features following methodology outlined in this work.

Abstract. Introduction/Objective:. Acute pain episodes are a major cause of health care utilization (HCU) in sickle cell disease (SCD), and adolescence is associated with increased pain frequency. We sought to determine whether there were differences in acute pain trajectories by sex and frequency of pain episodes among adolescents with SCD who presented to the emergency department (ED). Methods:. Retrospective review of electronic health records from a large, multicampus, pediatric SCD program. Results:. Of the 113 adolescents included, the mean age was 16.6 (SD 0.9), 41.6% (n = 47) were female, 77.9% (n = 88) had HbSS or a similarly severe genotype, and 43.4% (n = 49) had ≥3 episodes of HCU for pain, which we defined as having history of high HCU for pain. Those with a history of high HCU for pain had higher mean pain intensity scores at presentation, were more likely to receive either intravenous or intranasal opioids, and were more likely to be hospitalized. In a model considering the 3-way interaction between sex, history of high HCU for pain, and follow-up time from the initial pain intensity score, adjusted for opioid per kilogram body weight, and prescription of hydroxyurea, adolescent female patients with high HCU for pain had the slowest decline in pain intensity during treatment for acute pain in the ED. Conclusion:. Sex and history of high HCU for pain are associated with acute pain trajectories in adolescents with SCD presenting to the ED. These novel findings should be confirmed in future prospective studies.

Objectives: Prior study has demonstrated the implementation of quantitative ultrasound (QUS) for determining the therapy response in breast tumour patients. Several QUS parameters quantified from the tumour region showed a significant correlation with the patient's clinical and pathological response. In this study, we aim to identify if there exists such a link between QUS parameters and changes in tumour morphology due to combined ultrasound-stimulated microbubbles (USMB) and hyperthermia (HT) using the breast xenograft model (MDA-MB-231). Method: Tumours grown in the hind leg of severe combined immuno-deficient mice were treated with permutations of USMB and HT. Ultrasound radiofrequency data were collected using a 25 MHz array transducer, from breast tumour-bearing mice prior and post-24-hour treatment. Result: Our result demonstrated an increase in the QUS parameters the mid-band fit and spectral 0-MHz intercept with an increase in HT duration combined with USMB which was found to be reflective of tissue structural changes and cell death detected using haematoxylin and eosin and terminal deoxynucleotidyl transferase dUTP nick end labelling stain. A significant decrease in QUS spectral parameters was observed at an HT duration of 60 minutes, which is possibly due to loss of nuclei by the majority of cells as confirmed using histology analysis. Morphological alterations within the tumour might have contributed to the decrease in backscatter parameters. Conclusion: The work here uses the QUS technique to assess the efficacy of cancer therapy and demonstrates that the changes in ultrasound backscatters mirrored changes in tissue morphology.

Background: The World Health Organisation highlights that 60% of the morbidity and mortality from non communicable diseases are linked to lifestyle behavioural patterns .Primary risk factors include high fat and low fibre diet, lack of exercise, prolonged screen time, insufficient sleep, and the adoption of new habit and addictions. Since medical students are particularly prone to these risk factors, it is crucial to evaluate their lifestyle behaviours, recognising their role as future healthcare providers Objectives: 1. To assess anthropometric risk factors among the study population. 2. To assess the behavioural risk factors among the study population. Methodology: A cross sectional study was undertaken involving 150 undergraduate medical students in a private medical student in a private medical college. Data was collected through self administered questionnaire containing demographic details of the students, life style factors like diet, physical activity, screen time, habits and addictions. Data collected was entered into excel and analysed using SPSS 25. Results: Mean age of the study population was 22.3 (±1.5) years. Majority of the study participants were females (72.2%). Out of150 participants, 94.4% adhere to a non-vegetarian diet in which 15% reported daily consumption of soft drinks and 29% consume junk food daily. The participants who were overweight was found to be 33.4% in which 38.8% of the subjects are physically inactive and 22.2% reported inadequate sleep. Around 60 % of the participants use mobile phone most of the time. Conclusion: Behavioural risk factors such as unhealthy diet, inadequate sleep pattern, physical inactivity were prevalent among the undergraduate medical students.

The theoretical model for a packed porous catalytic particle of the slab, cylindrical, and spherical geometries shape in fixed-bed electrochemical reactors is discussed. These particles have internal mass concentration and temperature gradients in endothermic or exothermic reactions. The model is based on a nonlinear reaction–diffusion equation containing a nonlinear term with an exponential relationship between intrinsic reaction rate and temperature. The porous catalyst particle’s concentration is obtained by solving the nonlinear equation using Akbari-Ganji’s method. A simple and closed-form analytical expression of the effectiveness factor for slab, cylindrical, and spherical geometries was also reported for all values of Thiele modulus, activation energy, and heat reaction. The accordance with results of a reliable numerical method shows the good accuracy that their approximate solution yields.