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Background: Foot infections and ulceration are the most frequent reason for hospitalization in patients with diabetes mellitus (DM), accounting for up to 25% of all patients with DM admission. In the new advanced therapies for treating diabetic foot ulcers (DFUs), platelet-rich plasma (PRP) and negative pressure wound therapy (NPWT) have shown promising results. Aims and Objectives: The objectives of the study are as follows: To study the role of NPWT and PRP in management diabetic foot wounds and to compare granulation tissue formation, total duration of wound healing, and hospital stay between two therapies in diabetic foot wounds. Materials and Methods: This study was conducted with 100 patients of diabetic foot wounds meeting the inclusion criteria with pre-structured proforma and written informed consent for the therapies. After surgical debridement of the wound, the enrolled patients were randomized into two groups to receive either PRP therapy (Group A) (n=50) or NPWT (Group B) (n=50). Results: PRP and NPWT both were effective in healing process of diabetic foot patients. The mean value of Group A in granulation tissue score were 2.10±0.73 and 1.90±0.60 in Group B showed no significant difference. The duration of hospital stay was lower in Group A as compared to Group B that is 19.04±4.85 days in Group A and 35.86±7.35 days in Group B. The total duration of wound healing in Group A was longer that is 7.22±3.34 weeks in Group A and Group B was 4.0±2.27 weeks. Conclusion: PRP (Group A) and NPWT (Group B) are novel tools in management of DFUs. PRP is more simple, more safe, less costly, less equipment requirement, less complication, shorter time for the method, less painful, less hospital stay, and autologous nature in the preparation and had proved the superiority over NPWT.

Background: Various pulmonary cytology techniques have been established, representing morphological diversity and ranging from non-invasive to minimally invasive. However, there is currently no widely accepted classification system for respiratory cytology. In 2016, the Papanicolaou Society of Cytopathology (PSC) proposed a six-tiered category system for the cytological sample obtained from the respiratory tract. The diagnostic efficacy of such a category system can help in the early diagnosis of lung cancer patients, thereby reducing mortality and morbidity. Aim: The aim of this study was to determine the diagnostic accuracy and utility of PSC guidelines for reporting respiratory cytology specimens in comparison to histopathology. Materials and Methods: This study was conducted over 6 months, from September 2023 to February 2024, on a prospective basis. Out of a total of 165 samples analyzed, 147 (89%) were bronchoalveolar lavage samples, 09 (05%) were bronchial brushings, 03 (02%) were bronchial washings, and 06 (04%) were endobronchial ultrasound-guided transbronchial fine-needle aspiration. These cytological specimens were processed and categorized according to the PSC reporting system for respiratory cytology, as non-diagnostic, benign, atypical, neoplastic (benign/undetermined malignant potential), suspicious of malignancy, and malignant. In all cases, the histopathological correlation with corresponding transbronchial or endobronchial lung biopsy was done. Results: Our study found that 4% of the cases were non-diagnostic, 80% were benign, 13% were atypical, 2% were suspicious for malignancy, and 1% were malignant. When compared with the histological diagnosis, the sensitivity was 53.13%, the specificity was 93.23%, and the overall accuracy was 85.45%. Conclusion: The PSC reporting system for respiratory cytology can easily standardize reporting patterns to enhance communication between cytopathologists and clinicians, ultimately improving patient care.

Background Ingrown toenail is a nail disorder that commonly affects great toenails. Surgical modalities are commonly used as first-line treatment. A conservative approach is preferable as surgical procedures lead to pain and affect routine activities. Our aim is to study the efficacy of a new conservative technique using steri-stripTM for the treatment of mild to moderate ingrown toenails as an outpatient procedure. Methods Fifty patients presenting with stage 1–2 ingrown toenails were treated with the ‘steri-strip sling technique’. Steri-stripTM is inserted between the disto-lateral edge of the nail plate and nail fold and secured with an elastic bandage. The dressing is kept till the nail edge grows over the nail fold and stops penetrating the skin. Patients were reviewed every week for a month after the treatment and monthly thereafter. The final result was assessed at 6 months. The success of treatment was assessed clinically and by patient satisfaction levels. Results Fifty patients were treated and followed up. Immediately after the procedure, all patients reported significant relief in pain. After 6 months, 46 out of 50 patients were satisfied with the treatment and showed no recurrence. No complications were reported. Conclusion The ‘Steri-stripTM sling technique’ is a simple and effective outpatient procedure for the management of mild to moderate ingrown toenails.

Abstract Lignocellulosic biomass (LB) represents a sustainable and abundant feedstock with diverse applications, particularly in microbial fermentation processes for the production of biofuels and biochemical. This biomass serves as a promising alternative to conventional fossil fuels. Extensive research on the decomposition of wood has elucidated various microbial enzymatic and non-enzymatic mechanisms that facilitate the utilization of this resource. This review systematically examines the chemical and structural factors contributing to biomass recalcitrance, emphasizing recent advancements in analytical tools for their assessment. Furthermore, we explore innovative predictive models for hydrolysis, which are crucial for optimizing the conversion of LB into biofuels and bio-based chemicals. A comprehensive cost analysis of hydrolysis processes is also presented, offering insights into the economic feasibility of these conversion methods. The findings underscore the potential of lignocellulosic biomass as a key player in sustainable energy and material production.

The segmentation of gastrointestinal (GI) organs, including the stomach, small intestine, and large intestine, is crucial for radio oncologists to plan effective cancer therapy. This study presents an innovative semantic segmentation approach that integrates the Swin Transformer Block with the U-Net model to delineate healthy GI organs accurately using MRI data. The paper presents a novel approach that merges the Swin Transformer and U-Net models to leverage global context learning capabilities and fine-grained spatial resolution. Incorporating this integration greatly enhances the model’s capacity to achieve precise and comprehensive semantic segmentation, specifically in accurately outlining the gastrointestinal tract in MRI data. It utilizes the Swin Transformer, incorporating a shift-based windowing technique to gather contextual information efficiently while ensuring scalability. This novel architecture effectively balances local and global contexts, improving performance across various computer vision tasks, especially in medical imaging for segmenting the gastrointestinal tract. The model was trained and tested on the UW Madison GI Tract dataset, which comprises 38,496 MRI images from actual cancer cases. By leveraging the self-attention mechanisms of the Swin Transformer to capture global context and long-term dependencies, this approach combines the strengths of both models. The proposed architecture achieved a loss of 0.0949, a dice coefficient of 0.9190, and an Intersection over Union (IoU) score of 0.8454, demonstrating its effectiveness in providing high accuracy and robust performance. This technology holds significant potential for integration into clinical processes, enhancing the precision of radiation therapy for GI cancer patients.

The pervasive presence of microplastics (MPs) in the environment has raised significant concerns about their infiltration into the human food chain. In current review, the occurrence and distribution of MPs in various food matrices such as seafood, drinking water, fruits, vegetables, and beverages are discussed along with their potential routes of MPs entry into the human food chain. The toxicity of MPs on human health and different organs are discussed in brief. Current technological advancement and green analytical methods for the detection of MPs in food samples are compared, discussing their advantages and limitations. Green analytical methods, including stereomicroscopy, Fourier Transform Infrared spectroscopy, Raman spectroscopy, and enzymatic digestion, are evaluated for their efficacy and environmental impact. The Analytical Eco-Scale is used to assess the greenness of these methods. Challenges associated with MPs detection in food, such as complex food matrices, pretreatment methods, and variability in MPs concentrations, are addressed.

Carcinoma of the gall bladder (CAGB) has a poor prognosis. Molecular analysis of bile could classify indicators of CAGB. Bile samples (n = 87; training cohort) were screened for proteomics and metabolomics signatures of cancer detection. In bile, CAGB showed distinct proteomic (217 upregulated, 258 downregulated) and metabolomic phenotypes (111 upregulated, 505 downregulated, p 1.5, false discovery rate 0.5, p 90% (p 0.94. Validation of the top four metabolites—toluene, 5,6-DHET, creatine, and phenylacetaldehyde—in separate cohorts (n = 80; bile [T1] and paired plasma [T2]) showed accuracy (99%) and sensitivity/specificity (>98%) for CAGB detection. Tissue validation showed bile 5,6-DHET positively correlated with tissue PCNA (proliferation), and caspase-3 linked to cancer development (r2 >0.5, p

Wireless sensor networks (WSNs) have been highly utilized and defensible technology in diverse application areas for data gathering from remote and hard-to-approach regions. Wireless Sensor Networks are substantially important for the real-world applications such as environmental monitoring, surveillance, and smart infrastructure. Network coverage, connectivity and energy savings are significant factors in the WSN deployment. Wireless sensor networks (WSNs) undergo a great deal of crucial challenges such as minimize energy consumption, maximize coverage, and network lifetime improvement. Sensor nodes are energy constrained and deployed in resource-constrained environments for many real-world applications. Low energy usage is hence crucial to prolong network life. Meanwhile, to guarantee the performance of a WSN, it is crucial to ensure data transmission with less energy consumption and full coverage. These challenges are the central focus of this work, requiring scalable and efficient deployment strategies. In this paper, a complete survey study on optimization technique for deployment of WSN to improve network performance and resource utilization is offered. The paper also suggests a new algorithm named as Fractal Clustering Based Firefly Deployment Algorithm which is particularly designed for the deployment of sensor nodes deployed in WSNs. The proposed hybridize method uses the principles of fractal clustering and firefly optimization algorithm to make light-weight, energy efficient and enhanced optimized deployment strategy. To start with, the algorithm makes use of a fractal clustering technique to partition an area of interest into regions that have similar attributes. This clustering determines the areas that are needed to have higher sensor node density requirements — regions where events requiring a critical response or data traffic are high. The algorithm represents each cluster by a virtual firefly. The firefly algorithm is a biologically-inspired swarm intelligence optimization technique, inspired by the flashing behavior of fireflies which stochastically moves through input parameter space to find favorable deployment configurations. In this paper, the efficiency of the algorithm is verified by simulating the proposed algorithm using MATLAB2020 and comparing it with other deployment strategies. This analysis shows promising results.

Introduction: Polycystic ovary syndrome (PCOS) is a common endocrine disorder in women of reproductive age. Current management strategies only provide symptomatic relief, and there is a need for better treatments. Gamma-oryzanol, a dietary component found in rice bran oil, has been shown to be effective in increasing insulin sensitivity, reducing weight, and alleviating oxidative stress. In modern Chinese medicine, 香格里拉谷维素胶囊, 纯净谷维素软胶囊, and 诺特博谷维素片 are some of the medicines marketed in China that contain gamma-oryzanol. This study aims to evaluate the effects of gamma-oryzanol on clinical hallmarks of PCOS. Also to compare the efficacy of gamma-oryzanol alone versus in combination with clomifene citrate. Methods: In this study, PCOS was induced in a rat model using estradiol valerate. Gamma-oryzanol was then administered both alone and in combination with clomifene citrate. Results: The effects of gamma-oryzanol on estrous cyclicity, fertility, histopathological alterations, estradiol and testosterone levels, and oxidative parameters were evaluated. The results showed that gamma-oryzanol, both alone and in combination with clomifene citrate, had protective effects against the estradiol valerate-induced PCOS rat model. The combination of gamma-oryzanol and clomifene citrate was particularly effective, showing significantly better results than the standard treatment group (clomifene citrate alone). Discussion: These results suggest that gamma-oryzanol could be used in combination with existing standard treatments to enhance the management of PCOS. However, further molecular and clinical studies are needed to confirm these findings.

Skin cancer is a widespread and perilous disease that necessitates prompt and precise detection for successful treatment. This research introduces a thorough method for identifying skin lesions by utilizing sophisticated deep learning (DL) techniques. The study utilizes three convolutional neural networks (CNNs)—CNN1, CNN2, and CNN3—each assigned to a distinct categorization job. Task 1 involves binary classification to determine whether skin lesions are present or absent. Task 2 involves distinguishing between benign and malignant lesions. Task 3 involves multiclass classification of skin lesion images to identify the precise type of skin lesion from a set of seven categories. The most optimal hyperparameters for the proposed CNN models were determined using the Grid Search Optimization technique. This approach determines optimal values for architectural and fine-tuning hyperparameters, which is essential for learning. Rigorous evaluations of loss, accuracy, and confusion matrix thoroughly assessed the performance of the CNN models. Three datasets from the International Skin Imaging Collaboration (ISIC) Archive were utilized for the classification tasks. The primary objective of this study is to create a robust CNN system that can accurately diagnose skin lesions. Three separate CNN models were developed using the labeled ISIC Archive datasets. These models were designed to accurately detect skin lesions, assess the malignancy of the lesions, and classify the different types of lesions. The results indicate that the proposed CNN models possess robust capabilities in identifying and categorizing skin lesions, aiding healthcare professionals in making prompt and precise diagnostic judgments. This strategy presents an optimistic avenue for enhancing the diagnosis of skin cancer, which could potentially decrease avoidable fatalities and extend the lifespan of people diagnosed with skin cancer. This research enhances the discipline of biomedical image processing for skin lesion identification by utilizing the capabilities of DL algorithms.

ObjectiveThe objective of this study was to develop and evaluate a low-cost 3D-printed simulator to improve the ability of neurosurgical residents to handle and coordinate endoscopes in performing technically demanding procedures such as neuroendoscopic removal of ventricular tumors or endoscopic third ventriculostomy (ETV).MethodsThe simulator was developed, printed in-house, and evaluated in a trial involving neurosurgery residents who performed ETV and intraventricular tumor resection tasks using it. Participants completed a questionnaire that assessed various aspects of the simulator's effectiveness, including anatomical visualization, procedural understanding, competency enhancement, and subjective impressions.ResultsA total of 12 participants were included in the evaluation. The majority (n = 7, 53.85%) were male, with a mean age of 29.8 ± 3.27 years and 4 ± 2 years of neurosurgical experience. All participants agreed or strongly agreed (4.5 ± 0.50) that the 3D printed simulator helped develop systematic intraventricular visualization and understanding of surgical steps (4.42 ± 0.64). The handling of the endoscope was rated as realistic (4.5 ± 0.50), while the haptic qualities of the tumor were rated lower (3.83 ± 0.80; 3.92 ± 0.64). Training increased competence (4.25 ± 0.45) and coordination skills (4.5 ± 0.50), with 75% (n = 9) feeling more confident with neuroendoscopic instruments and 91.7% (n = 11) in future procedures.ConclusionThe developed 3D-printed simulator offers an accessible and practical training resource for neurosurgical residents, addressing the limitations of traditional training methods. The simulator appears to improve procedural skills and the competence of future neurosurgeons, potentially improving patient safety and outcomes in neurosurgical practice.

Objective: The objectives of this study were to explore the cytotoxic potential of different fractions (Hexane, chloroform, ethyl acetate and methanol) of Plumbago zeylanica L. root powder. Methodology: In this study, PzMH, PzMC, PzME, and PzMM fractions from the root powder of P. zeylanica were obtained through the cold maceration technique. The cytotoxic properties of these fractions on MG-63 cells were assessed using the MTT assay. The most potent fraction was further analyzed to understand its cytotoxic mechanism. To explore its apoptosis-inducing potential, microscopic and flow cytometric studies were conducted. Additionally, western blot analysis was used to examine the expression levels of proteins associated with apoptotic pathways. Results: PzMH showed the highest cytotoxicity against MG-63 osteosarcoma cells, with a GI50 value of 53.07 µg/ml. Flow cytometric studies revealed an upsurge in reactive oxygen species and disruption of mitochondrial membrane potential. The highest concentration of PzMH fraction (183.48 µg/ml) led to 62 % cell population arrest at G0/G1 stage. The anti-apoptotic protein Bcl2 was downregulated, with p53, cleaved-caspase 9, cleaved-caspase3, Bad and Bax expression upregulated. HPLC analysis revealed the presence of lawsone, p-coumaric acid, plumbagin, and 4-hydroxy benzaldehyde, indicating that the quinone-rich fraction of Plumbago zeylanica L. attenuates the proliferation of MG-63 osteosarcoma cells through the Bax/Bcl-2 pathway. Conclusion: To the best of our current knowledge, this marks the first documented case where the hexane fraction enriched with quinones extracted from the roots of P. zeylanica demonstrates its ability to induce apoptosis by influencing the Bax/Bcl2 pathway.

Telangiectatic osteosarcoma (TO) is one of the rare subtypes of osteosarcoma. As radiographic findings of TO are often confused with aneurysmal bone cysts, early tissue biopsy should be encouraged for diagnostic purposes. We report an original case of TO in a 28-year-old female presenting with severe pain and slight inability to bear weight on the right lower limb followed by a pathological fracture at the proximal right femur. It highlights the diagnostic and histopathological features of TO and discusses differential diagnostic considerations, treatment, and prognostic implications. The case was treated with hip disarticulation surgery along with neoadjuvant chemotherapy.

The introduction of Computational Chemistry via "Molecular Modelling and Drug Design" course at undergraduate level has opened wide possibilities for students to expand their knowledge of quantum chemistry, organic reaction mechanisms, protein-ligand interactions, drug design, and probable "in vivo" behavior of drug candidates. Learning the use of various freely available molecular modeling software and web tools can further help in building a strong foundation for this course. The present work deals with the molecular docking study on inhibition of beta-trypsin with para-substituted benzamidine derivatives. The students were introduced to freely available software like ChemSketch, ArgusLab, UCSF Chimera, and SwissADME. The study involves docking of 12 ligands into the binding site of beta-trypsin, aiding the students in visualizing various factors responsible for protein-ligand interactions. The HOMO-LUMO plots, electrostatic potential (ESP) maps, and Mulliken charge analysis facilitated reactivity prediction of the ligands. To complement the docking studies, drug-likeness, physicochemical properties, and pharmacokinetics of the docked ligands were also analyzed.

Abstract Background Bioresorbable patient-specific additive-manufactured bone grafts, meshes, and plates are emerging as a promising alternative that can overcome the challenges associated with conventional off-the-shelf implants. The fabrication of patient-specific implants (PSIs) directly at the point-of-care (POC), such as hospitals, clinics, and surgical centers, allows for more flexible, faster, and more efficient processes, reducing the need for outsourcing to external manufacturers. We want to emphasize the potential advantages of producing bioresorbable polymer implants for cranio-maxillofacial surgery at the POC by highlighting its surgical applications, benefits, and limitations. Methods This study describes the workflow of designing and fabricating degradable polymeric PSIs using three-dimensional (3D) printing technology. The cortical bone was segmented from the patient’s computed tomography data using Materialise Mimics software, and the PSIs were designed created using Geomagic Freeform and nTopology software. The implants were finally printed via Arburg Plastic Freeforming (APF) of medical-grade poly (L-lactide-co-D, L-lactide) with 30% β-tricalcium phosphate and evaluated for fit. Results 3D printed implants using APF technology showed surfaces with highly uniform and well-connected droplets with minimal gap formation between the printed paths. For the plates and meshes, a wall thickness down to 0.8 mm could be achieved. In this study, we successfully printed plates for osteosynthesis, implants for orbital floor fractures, meshes for alveolar bone regeneration, and bone scaffolds with interconnected channels. Conclusions This study shows the feasibility of using 3D printing to create degradable polymeric PSIs seamlessly integrated into virtual surgical planning workflows. Implementing POC 3D printing of biodegradable PSI can potentially improve therapeutic outcomes, but regulatory compliance must be addressed.

Head and neck cancer (HNC) is a diverse group of malignancies arising in the mucosal linings of the oral cavity, pharynx, and larynx, influenced by factors such as tobacco use, alcohol consumption, and human papillomavirus (HPV) infection. This study conducts a comprehensive meta-analysis of the mutational landscape of HNC across Asian cohorts, encompassing India, Korea, Japan, China, Singapore, and Saudi Arabia. The analysis highlights distinct genetic profiles influenced by environmental exposures, lifestyle habits, and genetic predispositions. Notably, the RAF family proteins, enriched in both Indian and Chinese cohorts, present potential therapeutic targets for RAF inhibitors like Vemurafenib. Additionaly, specific mutations like MET in Singaporean patients can be effectively addressed with drugs like Crizotinib, leading to rapid responses in HNSCC. Smokers exhibited high frequencies of CASP8 and FAT1 mutations. Novel driver genes, including RYR2 and ANK2, emerged with significant mutational frequencies in smokers. The RAS signaling pathway was identified as a prominent driver in HNC, contrasting with the globally prevalent PIK3CA/MTOR pathway. This study also underscores the high prevalence of HRAS mutations in Indian and Saudi cohorts. The study emphasizes the necessity for region-specific data to understand the unique molecular differences and develop effective therapies. The identification of NBEA and ANK2 as potential novel driver genes in HNC highlights new avenues for research and targeted therapeutic interventions tailored to the genetic profiles of Asian HNC patients.

ObjectiveTo report a digital workflow for use and long-term outcome of cranioplasty with a 3D-printed patient-specific Polyetheretherketone (PEEK) implant in a 12-y-old German Shepherd dog after surgical removal of an extensive occipital bone multilobular osteochondrosarcoma (MLO).Study designRetrospective case report.AnimalA 12-year-old neutered female German Shepherd dog was presented with facial deformity, blindness, tetraparesis, and ataxia. Magnetic resonance imaging (MRI) and computed tomography (CT) identified a large skull-based mass extending extra-and intracranially with severe compression of the cerebellum and occipital lobes of the cerebrum.MethodsOne-stage decompressive craniectomy using virtual surgical planned 3D-printed craniotomy cutting guides and the Misonix BoneScalpel® and reconstruction with a patient-specific 3D-printed PEEK cranial implant.Results3D-printed craniectomy cutting guides allowed an adequate fit of the cranial implant to the original skull. Misonix BoneScalpel® allowed performing a safe and extensive craniectomy. Postoperative CT (8 weeks after surgery) confirmed the PEEK cranial implant to be in place and without implant rejection. Clinically, the neurological examination identified only a right-hind limb delay in proprioception 8 weeks postoperatively, which remained unchanged at 18 months after surgery. Adjunctive treatment included metronomic chemotherapy. Eighteen months after surgery the dog passed away for reasons unrelated to the MLO, no implant-related complications were reported.Conclusion3D-printed craniectomy cutting guides, patient-specific PEEK cranial implant, and metronomic chemotherapy can lead to a successful long-term outcome in dogs with extensive skull MLO.Clinical significancePEEK is an alternative biomaterial that can be used successfully for skull reconstruction.

Background: Chemometric analysis is also a powerful tool that can be used in the preliminary stages of analytical methods optimization, but is also efficient in data processing. These new approaches may be the key to the analysis of proteins, carbohydrate, sphignolipids, polyphenols in different food components. Although there are challenges in identifying and annotating those compounds due to the limited availability of analytical standards and structural diversity. Scope and approach: Chromatograms are a useful tool for learning more about the chemical make-up of the food being tested. This information can occasionally be implicit or presented in a subtle way. Therefore, to extract it, chemometric tools and data mining techniques are needed. A chromatogram's fingerprint allows for the possibility of performing both identity and quality testing on food. This viewpoint aims to offer a current assessment of chromatographic fingerprinting technique in the area of food authentication. Additionally, this methodology's limitations, absence from official analytical methods, and future directions are discussed. Novel techniques have been employed in the past few decades, ranging from high-pressure thin liquid chromatography (HPTLC) to mass spectrometry (MS) and other spectroscopic methods. In the last decade research developments, and the application of analytical methods in qualitative and quantitative studies of food components and their adulterants is reviewed in the present work. High-pressure Thin liquid chromatography (HPTLC) is hyphenated with high resolution mass spectrometry is particularly addressed due to its applicability in the targeted/untargeted metabolomic analysis of all food components. Key findings and conclusions: In the current study, the HPTLC-MS technique was used to examine food components and food adulteration. The HPTLC-MS hyphenated techniques were found to be an extremely helpful method for the detection of food adulterants as well as the various food components, such as protein, carbohydrates, lipids, essential oils, and flavonoids, which were found to be effective as medicinally.

The extensive utilization of bismuth and its derivatives in many industries, such as chemical, semiconductor, pharmaceutical, and cosmetics, leads to their accumulation in wastewater, posing a risk to both human health and the environment. Carbon nanorods (CNR) are fluorescent nanoparticles with an ability to detect various analytes as sensing probes. This study focuses on the production, structure, and chemical composition characterization of silkworm-derived CNR (swCNR) and their ability to detect bismuth ions (Bi3+) and inhibit radicals. The optimum wavelength for exciting the fluorescence of swCNR was 370 nm, and the resulting emission peak was observed at 436 nm. The prepared swCNR showed static fluorescence quenching mechanism-based sensing of Bi3+ ions with a limit of detection of 175 nM and two linear ranges from 0.5 to 5 μM (R2 = 0.9997) and 10–50 μM (R2 = 0.9995). The swCNR demonstrated high selectivity in detecting Bi3+ ions in the spiked river water samples, thus establishing the swCNR's role as a nano fluorescence probe designed for the selective detection of Bi3+ ions among other metal ions. Favorable results for the antiradical ability of swCNR were obtained against hydroxyl, 2,2 diphenyl-1 picrylhydrazyl, and 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) radicals with scavenging percentages of 15, 32, and 90, respectively. The possible applications of swCNR in the environmental and antioxidant sectors are proposed in this study.

Abstract Introduction Despite older adults being more vulnerable to sepsis, most preclinical research on sepsis has been conducted using young animals. This results in decreased scientific validity since age is an independent predictor of poor outcome. In this study, we explored the impact of aging on the host response to sepsis using the fecal-induced peritonitis (FIP) model developed by the National Preclinical Sepsis Platform (NPSP). Methods C57BL/6 mice (3 or 12 months old) were injected intraperitoneally with rat fecal slurry (0.75 mg/g) or a control vehicle. To investigate the early stage of sepsis, mice were culled at 4 h, 8 h, or 12 h to investigate disease severity, immunothrombosis biomarkers, and organ injury. Mice received buprenorphine at 4 h post-FIP. A separate cohort of FIP mice were studied for 72 h (with buprenorphine given at 4 h, 12 h, and then every 12 h post-FIP and antibiotics/fluids starting at 12 h post-FIP). Organs were harvested, plasma levels of Interleukin (IL)-6, IL-10, monocyte chemoattract protein (MCP-1)/CCL2, thrombin-antithrombin (TAT) complexes, cell-free DNA (CFDNA), and ADAMTS13 activity were quantified, and bacterial loads were measured. Results In the 12 h time course study, aged FIP mice demonstrated increased inflammation and injury to the lungs compared to young FIP mice. In the 72 h study, aged FIP mice exhibited a higher mortality rate (89%) compared to young FIP mice (42%) (p

Reconfigurable Intelligent Surfaces (RIS) are revolutionizing wireless communications by enhancing signal quality and extending network coverage, particularly in remotely located Internet-of-Things (IoT) applications. However, integrating multiple RIS units into Simultaneous Wireless Information and Power Transfer (SWIPT) systems presents optimization challenges for energy harvesting and data transfer. This paper aims to optimize the performance of RIS-assisted SWIPT-IoT systems, focusing on maximizing three distinct objectives of data rate, energy harvesting (EH) and energy efficiency (EE), under Time-Switching (TS) and Power-Splitting (PS) protocols. The Karush-Kuhn-Tucker (KKT) conditions, alternating optimization-based algorithm and fractional programming based Dinkelbach Transform is proposed to jointly optimize the TS/PS ratio and transmit power. Numerical simulations are conducted to evaluate system performance highlight rate-energy trade-off across different scenarios, considering factors such as RIS angular placement, surfaces count, element count, separation distance, EH and data rate demands. Additionally, the imperfect Channel State Information (CSI) has been analyzed for the proposed algorithm. These findings offer practical insights into deploying multiple RIS units for enhanced SWIPT-IoT performance in real-world settings.

Unmanned aerial vehicles (UAVs) are getting significant attention from both researchers and the industry due to their wide range of applications. Remote sensing is one such application, in which UAVs are deployed to sense remote areas and transmit the data to a ground station for processing. However, due to the mobility and limited transmission range of UAVs, data transfer requires multiple hops. Nevertheless, the higher the number of hops, the larger the network latency. Thus, there is a need to reduce the number of hops and improve the connectivity. This can be achieved by creating small-world networks (SWNs) that perform better than traditional networks in terms of network evaluation metrics. The SWNs are created by adding shortcuts to the traditional network. In the literature, many theoretical works have been proposed for the creation of SWNs. However, these works add shortcuts randomly into the existing conventional network and fail to account for the costs incurred with the added shortcuts. As a result, these works are ineffective in improving the overall performance of the network. Thus, this work presents a novel reinforcement learning technique that uses a Q-learning algorithm to optimize throughput in delay-critical and energy-aware small-world UAV-assisted wireless networks (SW-UAV-WNs). The proposed algorithm populates the Q-matrix with all possible shortcuts and updates the Q-values based on the reward/penalty. It then adds shortcuts based on descending Q-values until the SW-UAV-WN is established. Through numerical results, we demonstrate that the proposed framework surpasses the conventional SWC approach, canonical particle swarm data delivery method, Low Energy Adaptive Clustering Hierarchy (LEACH), modified LEACH, and conventional shortest path routing method in terms of network latency, lifetime, packet delivery ratio, and throughput. Furthermore, we discuss the effect of different UAV velocities and different heights of the layers in which the UAVs hover on the performance of the proposed approach.

Research backgroundThere is considerable diversity in newly developed pummelo × sweet orange citrus hybrids. Most hybrids showed lower peel thickness and high juice yield but there is a lack of information on fruit quality parameters and molecular characterization. Therefore, the aim of the current study is to determine the content of antioxidants and properties of the fresh juice of 24 new pummelo × sweet orange citrus hybrids (Citrus maxima [Burm. f.] Osbeck × Citrus sinensis [L.] Osbeck) and the parental genotypes along with molecular characteristics determined using acidity specific markers. Experimental approach. The correlation and estimate of inheritance of the fruit juice properties: ascorbic acid, total phenol, total flavonoid, total antioxidant, total soluble solid and sugar contents, pH, titratable acidity, along with sensory evaluation was performed. Molecular characterization of these hybrids was carried out using de novo generated acidity specific simple sequence repeat (SSR) markers. Results and conclusions. The main constituents of the fruit juice of pummelo × sweet orange hybrids were observed in the range of w(ascorbic acid)=40.00–58.13 mg/100 g, total phenols expressed as gallic acid equivalents w(GAE)=40.67–107.33 mg/100 g, total antioxidants expressed as Trolox equivalents b(Trolox)=2.03–5.49 µmol/g, total flavonoids expressed as quercetin equivalents w(QE)=23.67–59.33 mg/100 g, along with other properties: total soluble solids=7.33–11.33 %, w(total sugar)=2.10–5.76 %, w(reducing sugar)=1.69–2.78 %, w(non-reducing sugar)=0.39–3.17 % and titratable acidity 1.00–2.11 %. The above parameters differed significantly in the fruit juice of the evaluated pummelo × sweet orange hybrids. Considering these parameters, the hybrids SCSH 17-9, SCSH 13-13, SCSH 11-15 and SCSH 3-15 had superior antioxidant properties in terms of these parameters. A higher heritability (≥80 %) was also observed for all juice properties. Molecular characterization of pummelo × sweet orange hybrids showed that >50 % of the hybrids were grouped with medium acidity parents. Both molecular and biochemical parameter-based clustering showed that interspecific hybrids exhibit transgressive segregation with increased antioxidants that help alleviate the health problems. Novelty and scientific contribution. These newly developed pummelo × sweet orange citrus hybrids are a valuable source of high-quality antioxidants for a healthy diet. The identification of trait markers that enable selection at the seedling stage is of great benefit to citrus breeders, as the characteristic features of a mature tree are not yet visible at the juvenile stage.

PURPOSEColorectal cancer (CRC) in young adults is a rising concern in developing countries such as India. This study investigates clinicopathologic profiles, treatment patterns, and outcomes of CRC in young adults, focusing on adolescent and young adult (AYA) CRC in a low- and middle-income country (LMIC).METHODSA retrospective registry study from January 2018 to December 2020 involved 126 young adults (age 40 years and younger) with CRC. Patient demographics, clinical features, tumor characteristics, treatment modalities, and survival outcomes were analyzed after obtaining institutional ethics committees' approval.RESULTSAmong 126 AYA patients, 62.70% had colon cancer and 37.30% had rectal cancer. Most patients (67%) were age 30-39 years, with no significant gender predisposition. Females had higher metastatic burden. Abdominal pain with obstruction features was common. Adenocarcinoma (65%) with signet ring differentiation (26%) suggested aggressive behavior. Limited access to molecular testing hindered mutation identification. Capecitabine-based chemotherapy was favored because of logistical constraints. Adjuvant therapy showed comparable recurrence-free survival in young adults and older patients. For localized colon cancer, the 2-year median progression-free survival was 74%, and for localized rectal cancer, it was 18 months. Palliative therapy resulted in a median overall survival of 33 months (95% CI, 18 to 47). Limited access to targeted agents affected treatment options, with only 27.5% of patients with metastatic disease receiving them. Chemotherapy was generally well tolerated, with hematologic side effect being most common.CONCLUSIONThis collaborative study in an LMIC offers crucial insights into CRC in AYA patients in India. Differences in disease characteristics, treatment patterns, and limited access to targeted agents highlight the need for further research and resource allocation to improve outcomes in this population.

With the progress in two distinct areas of nanotechnology and aptamer identification technologies, the two fields have merged to what is known as aptamer nanotechnology. Aptamers have varying properties in the biomedical field include their small size, non-toxicity, ease of manufacturing, negligible immunogenicity, ability to identify a wide range of targets, and high immobilizing capacity. Nevertheless, aptamers can utilize the distinct characteristics offered by nanomaterials like optical, magnetic, thermal, electronic properties to become more versatile and function as a novel device in diagnostics and therapeutics. This engineered aptamer conjugated nanomaterials, in turn provides a potentially new and unique properties apart from the pre-existing characteristics of aptamer and nanomaterials, where they act to offer wide array of applications in the biomedical field ranging from drug targeting, delivery of drugs, biosensing, bioimaging. This review gives comprehensive insight of the different aptamer conjugated nanomaterials and their utilization in biomedical field. Firstly, it introduces on the aptamer selection methods and roles of nanomaterials offered. Further, different conjugation strategies are explored in addition, the class of aptamer conjugated nanodevices being discussed. Typical biomedical examples and studies specifically, related to drug delivery, biosensing, bioimaging have been presented.

Owing to the extensive research on imidazolium based ionic liquids; the present study aims to design a potentiometric sensor for alkyl methylimidazolium ion. For this, poly(vinyl chloride) PVC membrane based on the neutral ion-pair complexes of cetylpyridinium chloride and sodium dodecyl sulfate (CPy+DS−) has been developed. The synthesized ion selective electrode (ISEs) have been found to show linear response with near-Nernstian slope for CnMeImCl (C10MeImCl, C12MeImCl and C14MeImCl). The proposed ISEs show quick response (5–6 s) and provide wide pH range for working. The influence of various ions on the performance of ion-selective electrode is investigated in terms of potentiometric selectivity coefficients, which were determined by separate solution method. The performance of synthesized ISE in examining the critical micellar concentration (cmc) of CnMeImCl in aqueous, aqua-organic media and in the presence of amino acids (glycine, L-glutamic acid and L-phenylalanine) has been found to be satisfactory and confirmed through conductivity and fluorescence measurements. This emphasizes its worth for in-situ analysis of alkyl methylimidazolium ions, during an organic reaction, and hence possible reaction mechanism evaluation. Different thermodynamic parameters such as Standard Gibbs energies of micellization (ΔGm∘), degree of counterion dissociation (α), amphiphile tail transfer Gibbs free energy (ΔGm,trans∘) have been evaluated. Further, the analytical application of synthesized electrode as end point indicator in the potentiometric titration of C14MeImCl with SDS has also been explored, which compared well with improved two-phase titration method. The current study is novel as it involves the use of a cheaper material (CPyCl) as ionophore and that the same electrode can be used for different chain alkyl methylimidazolium ions simply by changing the internal reference solutions.

Alarming concern over the persistence and toxicity of per- and polyfluoroalkyl substances (PFAS) in the environment has created an imperative need for designing and redesigning strategies for their detection and remediation. Conventional PFAS removal technologies that uses physical, chemical, or biological methods. Increase in the diversity and quantity of PFAS entering the environment has necessitated the need for developing more advanced and integrated strategies for their removal. Despite of the advances reported in this domain, there exist a huge research gap that need to be mentored to tackle the problems associated with mitigation of combined toxicity of wide variety of PFAS in the environment. The possibility of PFAS to combine with other emerging contaminants poses an additional threat to the existing treatment methods thereby stressing the need for a continuous monitoring and updating the treatment processes. This review work aims at understanding the structure, entry, and fate of different types of PFAS in to the environment. Further an in-depth discussion regarding the different levels of toxicity associated with PFAS is elaborated in the review. The process description of recent PFAS remediation techniques along with their significance, limitations and possibility of integration is discussed in detail. Further a detailed outlook on the advantages and limitations of PFAS removal methods and an insight into the recently developed PFAS removal methods is outlined in this review.

Arsenic (As) is a highly toxic metal that interferes with plant growth and disrupts various biochemical and molecular processes in plants. In this study, the harmful effects of As on rice were mitigated using combined inoculation of a root endophyte Serendipita indica and an actinobacterium Zhihengliuella sp. ISTPL4. A randomized experiment was conducted, in which rice plants were grown under controlled conditions and As-stressed conditions. The control and treatment groups consisted of untreated and non-stressed plants (C1), treated and non-stressed plants (C2), stressed and untreated plants (T1), and stressed and treated plants (T2). Various phenotypic characteristics such as shoot length (SL), root length (RL), shoot fresh weight (SFW), root fresh weight (RFW), shoot dry weight (SDW), and root dry weight (RDW) and biochemical parameters such as chlorophyll content, protein content, and antioxidant enzymatic activities were evaluated. The activity of various antioxidant enzymes was increased in T2 followed by T1 plants. Furthermore, high concentrations of phytohormones such as ethylene (ET), gibberellic acid (GA), and cytokinin (CK) were found at 4.11 μmol mg−1, 2.53 μmol mg−1, and 3.62 μmol mg−1 of FW of plant, respectively. The results of AAS indicated an increased As accumulation in roots of T2 plants (131.5 mg kg−1) than in roots of T1 plants (120 mg kg−1). It showed that there was an increased As accumulation and sequestration in roots of microbial-treated plants (T2) than in uninoculated plants (T1). Our data suggest that this microbial combination can be used to reduce the toxic effects of As in plants by increasing the activity of antioxidant enzymes such as SOD, CAT, PAL, PPO and POD. Furthermore, rice plants can withstand As stress owing to the active synthesis of phytohormones in the presence of microbial combinations.

Objective: Cleft lip and palate is the most common craniofacial birth anomaly and requires surgery in the first year of life. However, craniofacial surgery training opportunities are limited. The aim of this study was to present and evaluate an open-source cleft lip and palate hybrid (casting and three-dimensional (3D) printing) simulation model which can be replicated at low cost to facilitate the teaching and training of cleft surgery anatomy and techniques. Design: The soft tissue component of the cleft surgery training model was casted using a 3D printed 5-component mold and silicone. The bony structure was designed to simulate the facial anatomy and to hold the silicone soft tissue. Setting: Two groups, one group of trainees and one group of expert surgeons, at University Hospital Basel in Switzerland and Pontifical Catholic University of Chile in Santiago, Chile, tested the cleft lip and palate simulation model. Participants completed a Likert-based face and content validity questionnaire to assess the realism of the model and its usefulness in surgical training. Results: More than 70 % of the participants agreed that the model accurately simulated human tissues found in patients with unilateral cleft lip and palate. Over 60 % of the participants also agreed that the model realistically replicated surgical procedures. In addition, 80–90 % of the participants found the model to be a useful and appropriate tool for teaching the anatomy and surgical techniques involved in performing unilateral cleft lip and palate repair. Conclusion: This open-source protocol provides a cost-effective solution for surgeons to introduce the cleft morphology and surgical techniques to trainees on a regular basis. It addresses the current financial barrier that limits access to commercially available models during the early stages of surgeon training prior to specialization in the field.

Abstract The electroencephalogram (EEG) based motor imagery (MI) signal classification, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming, and medical fields. However, the problem is ill-posed as these signals are non-stationary and noisy. Recently, a lot of efforts have been made to improve MI signal classification using a combination of signal decomposition and machine learning techniques but they fail to perform adequately on large multi-class datasets. Previously, researchers have implemented long short-term memory (LSTM), which is capable of learning the time-series information, on the MI-EEG dataset for motion recognition. However, it can not model very long-term dependencies present in the motion recognition data. With the advent of transformer networks in natural language processing (NLP), the long-term dependency issue has been widely addressed. Motivated by the success of transformer algorithms, in this article, we propose a transformer-based deep learning neural network architecture that performs motion recognition on the raw BCI competition III IVa and IV 2a datasets. The validation results show that the proposed method achieves superior performance than the existing state-of-the-art methods. The proposed method produces classification accuracy of 99.7% and 84% on the binary class and the multi-class datasets, respectively. Further, the performance of the proposed transformer-based model is also compared with LSTM.