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Sangseo Kim,1 Sadikalmahdi Abdella,1 Fatima Abid,1 Franklin Afinjuomo,1 Souha H Youssef,1 Amy Holmes,1 Yunmei Song,1 Sachin Vaidya,2 Sanjay Garg1 1Centre for Pharmaceutical Innovation, Clinical and Health Sciences, University of South Australia, Adelaide, SA, 5000, Australia; 2Central Adelaide Local Health Network, The Queen Elizabeth Hospital, Woodville, SA, 5011, AustraliaCorrespondence: Sanjay Garg, Tel +61 8 8302 1575, Email Sanjay.garg@unisa.edu.auBackground: Imiquimod (IMQ) is an immunomodulating drug that is approved for the treatment of superficial basal cell carcinoma, actinic keratosis, external genital warts and perianal warts. However, IMQ cream (Aldara®) has several drawbacks including poor skin permeation, local toxicity, and compromised patient compliance as a topical pharmacological option.Methods: Our research aimed to develop and optimize nanostructured lipid carriers (NLCs) containing IMQ for the first time using a hybrid design of experiments approach. The optimized formulation was then incorporated into a matrix-type topical patch as an alternative dosage form for topical application and evaluated for IMQ deposition across different skin layers in comparison to the performance of the commercial product. Additionally, our work also attempted to highlight the possibility of implementing environment-friendly practices in our IMQ-NLCs formulation development by reviewing our analytical methods and experimental designs and reducing energy and solvent consumption where possible.Results: In this study, stearyl alcohol, oleic acid, Tween® 80 (polysorbate 80), and Gelucire® 50/13 (Stearoyl polyoxyl-32 glycerides) were selected for formulation development. The formulation was optimized using a 2k factorial design and a central composite design. The optimized formulation achieved the average particle size, polydispersity index, and zeta potential of 75.6 nm, 0.235, and – 30.9 mV, respectively. Subsequently, a matrix-type patch containing IMQ-NLCs was developed and achieved a statistically significant improvement in IMQ deposition in the deeper skin layers. The IMQ deposition from the patch into the dermis layer and receptor chamber was 3.3 ± 0.9 μg/cm2 and 12.3 ± 2.2 μg/cm2, while the commercial cream only deposited 1.0 ± 0.8 μg/cm2 and 1.5 ± 0.5 μg/cm2 of IMQ, respectively.Conclusion: In summary, IMQ-NLC-loaded patches represent great potential as a topical treatment option for skin cancer with improved patient compliance.Keywords: imiquimod, nanostructured lipid carrier, design of experiments, topical patches, skin cancer, greenness evaluation

Abstract Stevia rebaudiana (stevia) is a plant in the Asteraceae that contains several biologically active compounds including the antidiabetic diterpene glycosides (e.g. stevioside, rebaudioside and dulcoside) that can serve as zero-calorie sugar alternatives. In this study, an elicitation strategy was applied using 5% polyethylene glycol (PEG), sodium chloride (NaCl; 50 and 100 mM) and gibberellic acid (2.0 and 4.0 mg/L GA3) to investigate their effect on shoot morphogenesis, and the production of phenolics, flavonoids, total soluble sugars, proline and stevioside, as well as antioxidant activity, in shoot cultures of S. rebaudiana. Herewith, the media supplemented with 2 mg/L and 4 mg/L GA3 exhibited the highest shooting response (87% and 80%). The augmentation of lower concentrations of GA3 (2 mg/L) in combination with 6-benzylaminopurine (BAP) resulted in the maximum mean shoot length (11.1 cm). The addition of 100 mM NaCl salts to the media led to the highest observed total phenolics content (TPC; 4.11 mg/g-DW compared to the control 0.52 mg/g-DW), total flavonoids content (TFC; 1.26 mg/g-DW) and polyphenolics concentration (5.39 mg/g-DW) in shoots cultured. However, the maximum antioxidant activity (81.8%) was observed in shoots raised in media treated with 50 mM NaCl. The application of 2 mg/L of GA3 resulted in the highest accumulation of proline (0.99 μg/mL) as compared to controls (0.37 μg/mL). Maximum stevioside content (71 µL/mL) was observed in cultures supplemented with 100 mM NaCl and 5% PEG, followed by the 4 mg/L GA3 treatment (70 µL/mL) as compared to control (60 µL/mL). Positive correlation was observed between GA3 and stevioside content. Notably, these two compounds are derived from a shared biochemical pathway. These results suggest that elicitation is an effective option to enhance the accumulation of steviosides and other metabolites and provides the groundwork for future industrial scale production using bioreactors.

The ongoing trend of warming climate has made Glacial Lake Outburst Floods (GLOFs) a major cryospheric hazard worldwide, especially in the Himalayas. GLOFs in the Himalayan region are mostly caused by moraine-dammed proglacial lakes and ice-dammed lakes. These sporadic disasters have resulted in significant loss of life and property. This study offers a comprehensive analysis of the GLOF hazard potential of a potentially dangerous proglacial lake (PDGL) in the Ladakh region. This research explores the GLOF threat from the lake using multi-criteria analysis and advanced 2D hydrodynamic modeling approaches. The mass balance response of the mother glacier, its flow dynamics, and glacier-lake interactions were examined for the past 22 years. The findings show that over this period, the PDGL has had a notable expansion of 78.7%, accompanied by a significant recession of 13.2% in its feeding glacier. The glacier has witnessed an average thickness loss of ⁓7 m at the rate of 0.32 m a−1 during this period. The average, lowest, and maximum depth of the glacier were found to be 30.95, 14.30, and 50.57 m, respectively and the average velocity of the glacier was estimated as 3.38 m a−1. Because of the lake’s rapid expansion and steep surrounding slopes, it was classified as a high-hazard lake. The risk to the downstream community was assessed through 2D hydrodynamic modeling using the HEC-RAS tool. The maximum discharge under the worst-case scenario for the piping and overtopping failures was estimated as 3890.99 m3s−1 and 5111.39 m3s−1, respectively. The area potentially under the threat of inundation was calculated to be 4.74 and 5.38 km2 for the moderate and worst-case scenarios respectively. The expected maximum flood velocities range from 18.26 to 23.78 meters, respectively for the moderate and worst-case scenarios. At several locations in the downstream area, routed hydrographs representing the GLOF propagation were generated. The findings show that the flood wave in the worst-case scenario would arrive at the first settlement in 50 min, with a peak velocity of 12.36 m s−1. The potentially inundated area includes critical infrastructure such as bridges, residential houses, and roads. To mitigate the potential risk associated with this lake, a more detailed and on-site study is highly recommended.

Climate change poses significant challenges to the Himalayas, a region characterised by its fragile ecosystems and vulnerable communities dependent on environmental resources. Accurate climate data are crucial for understanding regional climatic variations and assessing climate change impacts, particularly in areas with limited observational networks. This study represents a pioneering effort in evaluating climatic fluctuations in the Jhelum basin, located in the North Western Himalayas, by utilising a diverse range of gridded meteorological datasets (APHRODITE, CHIRPS, CRU, and IMDAA) alongside observed climate data from the Indian Meteorological Department. The primary goal is to identify the most effective gridded climate data product for regions with limited data and to explore the potential of combining gridded data sets with observed data to understand climatic variability. Findings indicate a consistent upward trend in temperature across all datasets, with varying rates of increase. CRU records a rise of 1 °C in Tmax and 1.6 °C in Tmin, while APHRODITE shows a Tmean increase of approximately 1 °C. IMDAA reports increases in Tmax and Tmin. Observed mean annual Tmax and Tmin show net increases of 1 °C and 0.6 °C, respectively. Regarding precipitation, all datasets except IMDAA exhibit an increasing trend, contrary to observed data, which decreases from 1266 mm to 1068 mm over 40 years. CHIRPS, CRU, and APHRODITE display increasing trends, while IMDAA aligns closely with observed data but tends to overestimate precipitation by about 30%. Our research identifies IMDAA as the most suitable gridded climate data for the Jhelum basin in the North-western Himalayas. Despite some discrepancies in precipitation trends, IMDAA closely aligns with observed data, providing valuable insights for scholars and policymakers navigating climate data uncertainties in complex environments. Our findings contribute to informed decision-making and effective climate change mitigation strategies in the region.

Photocatalytic is considered a deadlock-breaking technology for renewable energy and lowering environmental pollutants. Photocatalytic efficiency efforts are improved by activating the photocatalyst to introduce an external magnetic field. This review highlights recent breakthroughs by comparing original coffee waste, activation of coffee waste with the addition of catalysts, and manipulation of spin electrons by applying external magnets. Characterization was done with FTIR to look at chemical bonds, UV-Vis with Tauc's Relation approach to measure bandgap, and material morphology using SEM EDX. The increase in photocatalyst activation aligns with the decrease in bandgap value. The rate of decline in bandgap is in line with the rate of acceleration of hydrogen production. Adding an external magnetic increases hydrogen production up to 1.5 times greater than the original photocatalytic.

Indonesia has a big potential of development by boosting up the SMEs. The SMEs seems neglected by its history on Indonesia as the main focus remained on public sector. The SMEs particular in large scale manufacturing units beyond the government, bank, and liberalization. As the SMEs sector period withdraw their development because of lack skill and function. In this research, SMEs sector in Indonesia as an object using the bibliography methodological to analyze the paradigm and liberate realizing the need to specify the key terms for purposes resilience on post global pandemic. Through innovative business opportunities and creating value by individuals, it is an effort to create value. In addition, this value creation can be done by providing encouragement to manage the risks of a project, improving communicative and management skills in helping the movement of people, fast materials and financial resources for project success.

A conductive wire can explode by rapidly heating it to vaporization temperature by flowing a current through it. This process is utilized to generate high-temperature high-density plasmas. The temperature and pressure distributions at the time of the explosion are not easily measured. Moreover, the amount of metal vapor from the wire that remains within the arcing area is unknown. This work presents the whole-process model of a single-wire electrical explosion from solid-state to plasma formation. For this purpose, the voltage drop and resistance of the exploding copper wire in solid-state are simulated through a zero-dimensional thermo-electrical model. Then, compressible Euler equations are implemented with nodal discontinuous Lagrange shape functions in a one-dimensional model to compute the flow of the generated copper vapor (due to the wire explosion) in surrounding nitrogen gas. The aim is to calculate the distributions of pressure, density, velocity, temperature, and mass flow along the cylindrical shock waves to estimate the arc's copper/nitrogen mixture ratio in free burning and nozzle constricted arcs. This mixture ratio is used to calculate the precise percentage of the metal vapor in the arcing area and to calculate Townsend growth coefficients utilizing to estimate the streamer breakdown of the mixture. The simulation results show good agreement with the experimental results in terms of the temporal evolution of the plasma channel boundary, the shock front speed estimation as well as the arc voltage magnitude numerically calculated deploying the extracted mixture percentage from this study, manifesting the validity of the model. It shows that despite the low-pressure studies, the exploding wire method is not suitable for circuit breakers employing supercritical fluids as the insulation. [ABSTRACT FROM AUTHOR]

Pengenalan entitas biomedis merupakan salah satu tahapan penting dalam ekstraksi informasi pada domain kesehatan. Untuk melakukannya, penelitian terkini banyak menggunakan model ekstraksi biomedis berbasis deep learning yang juga dikenal sebagai Biomedical NER (BioNER). Banyak penelitian menggunakan data sosial media sebagai basis data latih BioNER untuk memenuhi kebutuhan data yang besar. Di sisi lain, banyaknya topik bahasan pada sosial media membuat sumber data ini kurang representatif digunakan dalam pelatihan BioNER seiring dengan melimpahnya bias serta kurangnya data terkait biomedis. Oleh karena itu, penelitian ini mengusulkan suatu model BioNER yang telah dilatih pada situs konsultasi kesehatan online (KKO) agar memiliki representasi data medis lebih baik dibandingkan dengan penelitian lain yang sejenis. Kontribusi utama penelitian ini adalah terbentuknya model BioNER yang dapat digunakan dalam metode ekstraksi informasi biomedis dalam Bahasa Indonesia. Model ini dibangun menggunakan arsitektur state-of-the-art Transformers sehingga mendapatkan hasil evaluasi F1 score sebesar 0.7691, mengungguli model LSTM sebesar 0.03 poin. Hasil simulasi terhadap data riil juga menunjukkan bahwa model BioNER mampu mengenali entitas biomedis secara umum meskipun dilatih pada data yang terbatas. Selain itu, dengan digunakannya model berbasis XLM-R, maka model juga memiliki kemampuan pengenalan multibahasa sehingga potensi implementasinya tidak terbatas pada entitas Bahasa Indonesia saja. Untuk mendukung penelitian lanjutan, model pengenalan entitas biomedis ini juga dapat diakses secara publik untuk di https://huggingface.co/abid/indonesia-bioner. Abstract Biomedical entity recognition is one of the important stage in the information extraction, particularly in the health domain. Recent research uses a deep learning-based biomedical extraction model known as Biomedical NER (BioNER). Due to extensive data requirement, many studies still use social media data as a BioNER training data. On the other hand, social media data is less representative because it contains a lot of bias and lack of medical representation terms as the impact of many topics discussed. Therefore, this study proposes a BioNER model that has trained on an online health consultation platform to gain a better representation of biomedical data. This model also built using the state-of-the-art Transformers architecture. Hence, its evaluation results show that this model is able to achieve an F1 score of 0.7691, outperforming the LSTM model by 0.03. Simulation results on the real data also indicate that the BioNER model is able to recognize biomedical entities in general cases despite only trained on limited data. In addition, by using an XLM-R-based model, the recognition model also has multilingual recognition capabilities. Therefore, there is a potential implementation to apply the our BioNER model beyond Indonesian biomedical entities. Our biomedical entity recognition model is also accessible at https://huggingface.co/abid/indonesia-bioner.

The field surveys of Lal Suhanra National Park (LSNP) Bahawalpur were conducted to study its vertebrate diversity between October and December 2015. The study was commenced to determine the status of fauna of national park, diversity indices and environmental intimidations to their protection. The data was collected by point count method. The fauna observed there included 17 species of mammals including Asiatic wolf that is endangered. Five species of amphibians and 27 species of reptiles were found there. Among reptiles, Chitra indica is endangered. The results of study showed that maximum account of 7443 birds of 74 species belong to 35 families and representative of 16 orders were witnessed during the study. The seasonal distribution pattern of birds shown that year around resident were 76%, 7% were summer breeders, 4% were passage migrants while 5% were uncommon and 8% were common winter migrants. Hunting, deforestation, wood logging, disturbance by human activities and habitat loss are the major threats for wild animals at LSNP. Alteration in natural lake and their flows for recreational purposes is also a major threat. Proper attention should be given to existing habitat of study area for conservation of unique fauna of this biosphere reserve. [ABSTRACT FROM AUTHOR]

Background: Question-answer (QA) is a popular method to seek health-related information and biomedical data. Such questions can refer to more than one medical entity (multi-label) so determining the correct tags is not easy. The question classification (QC) mechanism in a QA system can narrow down the answers we are seeking. Objective: This study develops a multi-label classification using the heterogeneous ensembles method to improve accuracy in biomedical data with long text dimensions. Methods: We used the ensemble method with heterogeneous deep learning and machine learning for multi-label extended text classification. There are 15 various single models consisting of three deep learning (CNN, LSTM, and BERT) and four machine learning algorithms (SVM, kNN, Decision Tree, and Naïve Bayes) with various text representations (TF-IDF, Word2Vec, and FastText). We used the bagging approach with a hard voting mechanism for the decision-making. Results: The result shows that deep learning is more powerful than machine learning as a single multi-label biomedical data classification method. Moreover, we found that top-three was the best number of base learners by combining the ensembles method. Heterogeneous-based ensembles with three learners resulted in an F1-score of 82.3%, which is better than the best single model by CNN with an F1-score of 80%. Conclusion: A multi-label classification of biomedical QA using ensemble models is better than single models. The result shows that heterogeneous ensembles are more potent than homogeneous ensembles on biomedical QA data with long text dimensions. Keywords: Biomedical Question Classification, Ensemble Method, Heterogeneous Ensembles, Multi-Label Classification, Question Answering