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Successful nerve repair using bioadhesive hydrogels demands minimizing tissue-material interfacial mechanical mismatch to reduce immune responses and scar tissue formation. Furthermore, it is crucial to maintain the bioelectrical stimulation-mediated cell-signaling mechanism to overcome communication barriers within injured nerve tissues. Therefore, engineering bioadhesives for neural tissue regeneration necessitates the integration of electroconductive properties with tissue-like biomechanics. In this study, we propose a stretchable bioadhesive based on a custom-designed chemically modified elastin-like polypeptides (ELPs) and a choline-based bioionic liquid (Bio-IL), providing an electroconductive microenvironment to reconnect damaged nerve tissue. The stretchability akin to native neural tissue was achieved by incorporating hydrophobic ELP pockets, and a robust tissue adhesion was obtained due to multi-mode tissue-material interactions through covalent and noncovalent bonding at the tissue interface. Adhesion tests revealed adhesive strength ~10 times higher than commercially available tissue adhesive, Evicel®. Furthermore, the engineered hydrogel supported in vitro viability and proliferation of human glial cells. We also evaluated the biodegradability and biocompatibility of the engineered bioadhesive in vivo using a rat subcutaneous implantation model, which demonstrated facile tissue infiltration and minimal immune response. The outlined functionalities empower the engineered elastic and electroconductive adhesive hydrogel to effectively enable sutureless surgical sealing of neural injuries and promote tissue regeneration.

The high ductile brittle transition temperature of tungsten (W) poses challenges like cracking and porosity during its laser-powder bed additive manufacturing (L-PBFAM). Computational alloy design evaluated niobium (Nb) and carbon (C) to activate eutectic solidification, addressing the cracking issues during the terminal stage of solidification. Furthermore, these elements prevent O segregation at grain boundaries, thereby enhancing grain boundary cohesion in W. In combination with effective process parameters, the novel W-Nb-C alloy developed for L-PBFAM produces near crack-free specimens with heterogeneous microstructures. The intrinsic mechanical responses associated with the heterogenous microstructure were delineated by nanoindentation analysis.

Background: Breast lymphomas are a rare group of malignancies that are further subdivided into primary and secondary. AIMS: To study the pathological and clinical course of breast lymphomas. Materials and Methods: This is a retrospective analysis of patients treated at our institute over a period of 4.5 years from September 2018 to February 2023. The details of all the patients diagnosed with breast lymphoma were reviewed and analysed for the histomorphological, immunohistochemical, clinical, and treatment details. Appropriate statistical analysis including Kaplan-Meier methods was used. Results: Out of 11 cases of breast lymphoma, five were primary and six were secondary. It was seen predominantly in females (82%) and the age range was 31 to 73 years. Diffuse large B cell lymphoma (DLBCL) was the predominant morphology (73%), along with single rare cases of ALK-negative anaplastic large cell lymphoma, Burkitt lymphoma, and small lymphocytic lymphoma. The treatment details were analyzed for 7 patients. The median follow-up was 28 months. Rituximab along with CHOP regimen or its variants was commonly used as first-line treatment with initial response rates of 71%. The median progression-free survival was 5 months. The median overall survival was 15 months. Conclusion: Lymphomas of the breast are rare but it is crucial to differentiate them from the commoner breast carcinomas as the treatment and prognosis vary vastly.

Background: Asthma is one of the most common chronic diseases globally. It is characterized by airflow obstruction that varies markedly, regardless of treatment. It is a type of airway inflammation due to bronchial hyperresponsiveness to a wide range of allergens, leading to excessive, usually reversible, airway narrowing. Asthma occurs more frequently in obese people, and it may be linked to pro-inflammatory adipokines. Apelin, a bioactive peptide belonging to the adipokines group, is identified as an endogenous ligand of an orphan G-protein coupled receptor named Apelin receptor (AR or APJ). Apelin is demonstrated in mature adipocytes and is highly expressed in pulmonary tissue, including bronchial and alveolar epithelial cells and small pulmonary blood vessels. Aim: The aim of this study was to evaluate the level of serum apelin in adult nonobese patients diagnosed with bronchial asthma cases and compare with healthy volunteers. We also aimed to correlate serum apelin levels with the severity of bronchial asthma. Materials and Methods: Twenty-two nonobese clinically diagnosed cases of bronchial asthma were included as cases and 66 age, sex, and socio-economic status-matched healthy volunteers as controls. Body mass index of cases and control subjects was calculated by weight (kg) per height (m2). Pulmonary function tests of the cases were done using spirometry. Serum apelin levels were estimated using Abbkine human ELISA-based kit. Statistical analysis was done using Pearson’s correlation test and analysis of variance. Results: We recruited 22 nonobese bronchial asthma cases (mean age = 38.2 ± 10.5 years) and 66 matched controls (mean age = 39.5 ± 12.0 years. Majority of the sample were females (n = 19 cases [86.3%], 54 controls [81.8%]). The mean serum apelin levels in cases and controls were 1355.3 ± 781.0 ng/L and 725.9 ± 375.6 ng/L, respectively; this difference was statistically significant (t = 5.053, df = 86, p = 0.001). The mean serum apelin levels in mild and moderate cases of bronchial asthma were 1121.5 ± 535.9 ng/L and 2150.2 ± 1019.1 ng/L, respectively. This difference was also statistically significant (t = 3.06, df = 20, p = 0.01). Conclusion: Serum apelin levels differ significantly between adult patients with bronchial asthma and population controls. Further, serum apelin levels differed with illness severity. These findings have important implications for disease management.

Haemophilia presents challenges in joint pathology assessment, prompting exploration of point-of-care ultrasonography (POC-US) as a diagnostic tool. Aims: The study aimed to assess the diagnostic performance of point-of-care ultrasonography (POC-US) compared to magnetic resonance imaging (MRI) in detecting synovial hypertrophy, cartilage abnormality, and bony abnormality among people with haemophilia (PWH) treated at Assam Medical College Hospital.

Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma, ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts.

Abstract Background Clinical trials play a crucial role in biomedical research, and it is important to register them in public registries to ensure transparency and prevent research waste. In this study, we wished to determine what steps need to be taken to identify every clinical trial run in India that has been registered in any of the (non-Indian) World Health Organization-recognised primary registries. Of the 16 registries, we studied all except that of the European Union, which will be studied separately. Methods Two methodologies were employed for each registry, except for four that did not facilitate one or the other method. Methodology A involved downloading all the records in a registry and querying them. Methodology B involved conducting a search via the registry website. Results Only four registries provided consistent results with both methodologies. Seven registries had different results from the two methodologies. Of these, in four cases, in Methodology A one field indicated that the study ran in India, while another indicated otherwise. Conclusions The above-mentioned ambiguities should be addressed by the concerned registries. Overall, this study reinforces the need for improved data accuracy and transparency in clinical trial registries and emphasizes the importance of resolving complications faced by users while navigating the registries. Ensuring accurate and comprehensive registration of clinical trials is essential for meta-research and the use of such data by a variety of stakeholders.

Background and Objective: Zirconia endocrowns are a popular restorative option due to their fracture resistance and aesthetic appeal, but the influence of different preparation designs on their performance remains underexplored. This study evaluated the effects of preparation design and the occlusal thickness on the fracture strength and failure modes of zirconia endocrowns in endodontically treated teeth. Materials and Methods: Thirty-two extracted human mandibular first molars were endodontically treated and divided into four groups (n = 8), based on margin design and occlusal thickness: Group A: 2 mm butt joint with 3 mm occlusal thickness, Group B: 1 mm butt joint with 4 mm occlusal thickness, Group C: 2 mm ferrule with 3 mm occlusal thickness, and Group D: 1 mm ferrule with 4 mm occlusal thickness. Zirconia endocrowns were fabricated using CAD/CAM technology and cemented with self-adhesive resin cement. The specimens were subjected to thermocycling and then tested for fracture strength using a universal testing machine. Failure modes were analyzed under a stereomicroscope. Data were statistically analyzed. Results: Group B showed the highest mean fracture strength (6655.66 N), followed by Group D at 6091.00 N. Group C exhibited the lowest fracture strength (4867.41 N), although the differences were not statistically significant (P = 0.121). Type V failure was the most common failure mode across all groups, with 100% occurrence in both 2 mm margin groups (Groups A and C). Conclusion: Margin design and occlusal thickness appeared to influence the fracture strength and failure modes of zirconia endocrowns; the differences were not significant.

Inadequate turfgrass irrigation management poses a significant challenge, resulting in considerable water loss through runoff and the transport of contaminants, ultimately jeopardizing surface and groundwater quality. This study introduces a Machine Learning (ML)-based Decision Support System (DSS) designed to optimize turfgrass irrigation, concurrently minimizing runoff and preserving turfgrass quality. A robust ML classifier, specifically the Radial Basis Function - Support Vector Machine (RBF-SVM) was trained on synthetic data generated through the Monte-Carlo (MC) technique, which was then used to specify a set of irrigation rules implemented in the irrigation controller. The synthetic data were derived from observations collected from irrigation plots at the Texas A&M University Turfgrass Laboratory in Texas, United States, with Soil Wetting Efficiency Index (SWEI) serving as the target variable. When tested against a commercially available irrigation controller, the ML-based controller significantly reduced runoff by an average of 74 % while maintaining high Green Cover (GC) in turfgrass, achieving an accuracy of 87 %. These findings highlight the potential of ML-driven irrigation systems to improve water use efficiency, reduce environmental impact, and maintain turf quality. Such systems could be beneficial for urban landscapes, sports fields, and agriculture, helping users conserve water while achieving sustainable turf management.

This study investigates the synthesis of nanostructured magnesium oxide (MgO) using the auto-combustion method with urea (MgO-U), glycine (MgO-G), and citric acid (MgO-C) as fuels, followed by subsequent calcination. The selection of fuel plays a crucial role in determining the morphology of the nanostructures. X-ray diffraction (XRD) analysis confirms that the samples exhibit a well-crystallized structure with the desired phase and are free of impurities. Field emission scanning electron microscopy (FESEM) shows that MgO-U nanoparticles have diameters ranging from 10 to 20 nm, MgO-C nanoparticles from 60 to 80 nm, and MgO-G nanoparticles from 80 to 100 nm. These MgO nanomaterials are specifically engineered for adsorption applications, aimed at removing toxic organic dyes and fluoride ions from aqueous solutions. Results demonstrate that MgO-G nanoparticles exhibit superior adsorption activity compared to MgO-U and MgO-C nanoparticles, with maximum uptake capacities of 1429 and 14 mg/g, respectively. The adsorption processes adhere to second-order kinetic model. These findings highlight the potential application of the synthesized MgO nanoparticles for mitigating water pollution by adsorbing pollutants from aqueous media.