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Introduction: Combination therapy is an effective approach to simultaneously target multiple pathogenic factor of acne. A unique combination of oral azithromycin pulse therapy and daily topical benzoyl peroxide has been developed for treatment of acne. Material & Methods: It was an open, controlled, clinical trial, conducted on 37 out patients with acne vulgaris. Patients were clinically assessed at baseline &at week 0,4, 8 and 12. Evaluation included success rate (subjects clear or excellent improvement, good response), lesion count & percentage change in lesion count from baseline, cutaneous tolerability & adverse events. Results: The combination of oral azithromycin pulse therapy & daily topical benzoyl peroxide was very safe & effective with significant differences in percentage of lesion count change observed as early as 1-4 weeks. Adverse events were more frequent with the combination therapy that occurred early in the study &were transient. Conclusion: This study revealed that combination regimen of azithromycin &benzoyl peroxide (4%) is indeed very much efficacious & safe in the management of acne vulgaris. [ABSTRACT FROM AUTHOR]

Type 2 diabetes mellitus (T2DM) is usually depicted by relative insulin deficiency, raised blood glucose levels, and the predominant risk factor, insulin resistance. Hence, the development of insulin sensitizer drugs targeting PPAR-γ receptors has expanded enormous interest as an attractive choice for T2DM treatment. Thiazolidinediones (TZD) enhance insulin sensitivity either by directly functioning on gene transcription of the PPARγ receptor related to glucose homeostasis or by systemic sensitization of insulin and, therefore, improved hyperglycemia in a wide range of patients. However, severe complications and adverse effects of TZDs necessitate the development of an efficacious and reliable insulin sensitizer from alternative resources. On the contrary, Nature is a rich source of anticipated effective and safer medicine; more than fifty percent of drugs on the market are developed from natural products. Hence, searching for a new PPAR-γ agonist from bioactive secondary compounds of medicinal plants along with greater efficacy and safety is a recognized and consistent tactic for developing novel antidiabetic agents. Pulicaria jaubertii is a fragrant perennial aromatic plant with anti-inflammatory, antidiabetic, antimicrobial, antimalarial, and insecticidal properties. The current study was designed to use a computer-aided drug design to explore the best antidiabetic compounds from P. jaubertii. Herein, the molecular docking study of 80 investigated ligands against the PPAR-γ receptor identifies the highest docking score for five ligands ranging from -8.9 kcal/mol to 8.0 kcal/mol, which is also more significant than the standard drug pioglitazone (-7.7 kcal/mol) determined by the PyRx 8.0 virtual screening software. GLN286, CYS285, SER289, TYR473, MET364, ARG288, ILE341, and LEU333 residues are found to be significant contributors to the non-bonded interaction between ligands and receptors. Molecular electrostatic potential (MEP), DFT, molecular orbital (MO), ADMET, and toxicological analyses were performed on the selected five high-scored ligands of P. jaubertii. Results documented that all investigated ligands, especially L4, show considerably excellent profiles in molecular docking, MEP, DFT, MO, ADMET, and toxicological predictions, suggesting our drug-designing approaches may contribute to the development of a novel antidiabetic drug for the treatment of T2DM from natural resources., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background and Objective: Since plant-based natural drugs are widely accepted in modern times and possess numerous pharmacological effects with an extensive therapeutic range, an ethnomedicinal plant native to Bangladesh was selected to investigate for investigation of its various pharmacological effects. Macropanax dispermus has been traditionally used and has demonstrated numerous pharmacological effects in preclinical investigations. Therefore, this research aimed to assess the central nervous system (CNS) depressant and antioxidant activities of the crude methanol extracts of the stem barks (MDMS), leaves (MDML), and their different fractions., Methods: The CNS depressant activity was assessed using the hole cross, rota-rod, and elevated plus maze tests on Swiss Albino mice, while the antioxidant activity was evaluated using the 2,2-diphenyl-1-picrylhydrazyl free radical, hydrogen peroxide (H 2 O 2 ) nonradical scavenging, and ferric reducing power assays., Results: The conducted assays successfully demonstrated that the chloroform fraction of MDML is a significantly ( P < 0.001) effective CNS depressant, muscle relaxant, and anxiolytic agent with excellent antioxidative effects compared to standard and control. The aqueous fraction of MDML also acted as a significantly ( P < 0.001) active CNS depressant and muscle relaxant, and it was a highly active ferric-reducing agent. All effects were dose and concentration-dependent., Conclusion: The presence of various phytochemicals might contribute to these activities. However, further research is suggested to isolate their active compounds and evaluate their mechanisms of action., Competing Interests: The authors declare that they have no conflict of interest., (© 2024 Institute of Bioorganic Chemistry, Polish Academy of Sciences.)

Cervical cancer is a cancer that remains a significant global health challenge all over the world. Due to improper screening in the early stages, and healthcare disparities, a large number of women are suffering from this disease, and the mortality rate increases day by day. Hence, in these studies, we presented a precise approach utilizing six different machine learning models (decision tree, logistic regression, naïve bayes, random forest, k nearest neighbors, support vector machine), which can predict the early stage of cervical cancer by analysing 36 risk factor attributes of 858 individuals. In addition, two data balancing techniques-Synthetic Minority Oversampling Technique and Adaptive Synthetic Sampling-were used to mitigate the data imbalance issues. Furthermore, Chi-square and Least Absolute Shrinkage and Selection Operator are two distinct feature selection processes that have been applied to evaluate the feature rank, which are mostly correlated to identify the particular disease, and also integrate an explainable artificial intelligence technique, namely Shapley Additive Explanations, for clarifying the model outcome. The applied machine learning model outcome is evaluated by performance evaluation matrices, namely accuracy, sensitivity, specificity, precision, f1-score, false-positive rate and false-negative rate, and area under the Receiver operating characteristic curve score. The decision tree outperformed in Chi-square feature selection with outstanding accuracy with 97.60%, 98.73% sensitivity, 80% specificity, and 98.73% precision, respectively. During the data imbalance, DT performed 97% accuracy, 99.35% sensitivity, 69.23% specificity, and 97.45% precision. This research is focused on developing diagnostic frameworks with automated tools to improve the detection and management of cervical cancer, as well as on helping healthcare professionals deliver more efficient and personalized care to their patients., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (© 2024 The Authors.)

Fruits are mature ovaries of flowering plants that are integral to human diets, providing essential nutrients such as vitamins, minerals, fiber and antioxidants that are crucial for health and disease prevention. Accurate classification and segmentation of fruits are crucial in the agricultural sector for enhancing the efficiency of sorting and quality control processes, which significantly benefit automated systems by reducing labor costs and improving product consistency. This paper introduces the "FruitSeg30&#95;Segmentation Dataset & Mask Annotations", a novel dataset designed to advance the capability of deep learning models in fruit segmentation and classification. Comprising 1969 high-quality images across 30 distinct fruit classes, this dataset provides diverse visuals essential for a robust model. Utilizing a U-Net architecture, the model trained on this dataset achieved training accuracy of 94.72 %, validation accuracy of 92.57 %, precision of 94 %, recall of 91 %, f1-score of 92.5 %, IoU score of 86 %, and maximum dice score of 0.9472, demonstrating superior performance in segmentation tasks. The FruitSeg30 dataset fills a critical gap and sets new standards in dataset quality and diversity, enhancing agricultural technology and food industry applications., (© 2024 The Authors.)

The primary objective of this study was to isolate bacteria from diabetic foot ulcers and subsequently assess their antibiotic resistance capabilities. Seventy-five patients diagnosed with diabetic foot ulcers were investigated. A number of these patients (97.33%) had type 2 diabetes, with a significant proportion of them having been diagnosed for 1-5 years (29.33%). Notably, a substantial number of these individuals were on insulin usage (78.66%). Among the patients under examination, 49.33% reported having no use of tobacco products, alcohol, or betel leaf. The ulcers analyzed in this study were classified into grades 1-5 according to the Wagner scale. Wagner grade 2 diabetic foot ulcers had the highest number of culture-positive patients, at 33.33%. Pus samples collected from patients were cultured on selective media, and bacterial identity was confirmed by biochemical tests and polymerase chain reaction. A total of 141 isolates were isolated. Among the isolates, 82.97% gram-negative bacteria and 17.02% gram-positive bacteria were detected. Klebsiella pneumoniae was the most common isolate. Proteus spp., Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus were also detected. Approximately 61.33% of the ulcers exhibited were polybacterial. In this study, it was observed that all bacterial isolates, except for Proteus spp., were primarily detected in patients classified under Wagner's grade 2. Moreover, antibiotic susceptibility was also tested on these 141 isolates. Among them, Escherichia coli showed the highest multidrug resistance, 81.81%. Most of the gram-negative bacteria were resistant to ampicillin. All of the gram-negative isolates exhibited high levels of susceptibility to piperacillin-tazobactam, and these levels were Klebsiella pneumoniae (97.56%), Pseudomonas aeruginosa (95.24%), Escherichia coli (81.82%), and Proteus spp. (80%). On the other hand, gram-positive Staphylococcus aureus mostly showed sensitivity towards vancomycin and norfloxacin (79.17%)., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Baral et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Zinc oxide nanoparticles (ZnO NPs) have been successfully prepared using Cocos nucifera leaf extract and their antimicrobial, antioxidant and photocatalytic activity investigated. The structural, compositional and morphological properties of the NPs were recorded and studied systematically to confirm the synthesis. The aqueous suspension of NPs showed an ultraviolet-visible (UV-Vis) absorption maxima of 370 nm, indicating primarily its formation. X-ray diffraction analysis identified the NPs with a hexagonal wurtzite structure and an average particle size of 16.6 nm. Fourier transform infrared analysis identified some biomolecules and functional groups in the leaf extract as responsible for the encapsulation and stabilization of ZnO NPs. Energy-dispersive X-ray analysis showed the desired elemental compositions in the material. A flower-shaped morphology of ZnO NPs was observed by scanning electron microscopy, with a grain size of around 15 nm. The optical properties of the NPs were studied by UV-Vis spectroscopy, and the band gap was calculated as 3.37 eV. The prepared ZnO NPs have demonstrated antimicrobial activity against T. harzianum and S. aureus , with a zone of inhibition of 14 and 10 mm, respectively. The photocatalytic behaviour of ZnO NPs showed absorbance degradation at around 640 nm and it discoloured methylene blue dye after 1 h, with a degradation maximum of 84.29%. Thus, the prepared ZnO NPs could potentially be used in antibiotic development and pharmaceutical industries, and as photocatalysts., (© 2022 The Authors.)

For the welfare of self-development and the country's economic evolution, people invest their youth and money in different cultivation and sustainable production business sectors. The crops or fruits get all the attention for this purpose, but currently, the commercial cultivation of flowers is becoming a numerous beneficial investment. As a consequence, the rose( Genus Rosa ) is one of the most beautiful and commercially demanding flowers among different flowers. However, insecticide resistance is considered one of the lion's share issues facing agricultural production of roses by decreasing plants' growth and the quality as well as the quantity of healthy-looking flowers. Apart from this, due to different natural and environmental issues, rose's quality and production level are losing their fame. Additionally, the cultivators of this sector are not educated enough to identify the initial affection of different diseases of leaves with beard eyes. Besides, the lack of communication skills to consult with an agriculturist timely turns the situation worst more than the estimation of the production. With this concern, early detection of diseases that affected different parts of roses, such as leaves, is crucial. Recently, image processing techniques and machine learning classifiers have been primarily applied to recognize multiple diseases. This article presents an extensive dataset of rose leaves images, both diseases affected and diseases free are classified into three classes (Blackspot, Downy Mildew, and Fresh Leaf). The dataset is composed of the collected images which were captured during the seasonal time of diseases affection with the consultation of a domain expert and the dataset is accessible at https://data.mendeley.com/datasets/7z67nyc57w/2., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:, (© 2022 The Author(s).)

Cauliflower, a winter seasoned vegetable that originated in the Mediterranean region and arrived in Europe at the end of the 15th century, takes the lead in production among all vegetables. It's high in fiber and can keep us hydrated, and have medicinal properties like the chemical glucosinolates, which may help prevent cancer. If proper care is not given to the plants, several significant diseases can affect the plants, reducing production, quantity, and quality. Plant disease monitoring by hand is extremely tough because it demands a great deal of effort and time. Early detection of the diseases allows the agriculture sector to grow cauliflower more efficiently. In this scenario, an insightful and scientific dataset can be a lifesaver for researchers looking to analyze and observe different diseases in cauliflower development patterns. So, in this work, we present a well-organized and technically valuable dataset "VegNet' to effectively recognize conditions in cauliflower plants and fruits. Healthy and disease-affected cauliflower head and leaves by black rot,downy mildew, and bacterial spot rot are included in our suggested dataset. The images were taken manually from December 20th to January 15th, when the flowers were fully blown, and most of the diseases were observed clearly. It is a well-organized dataset to develop and validate machine learning-based automated cauliflower disease detection algorithms. The dataset is hosted by the Institute - National Institute of Textile Engineering and Research (NITER),the Department of Computer Science and Engineering and is available at the link following: https://data.mendeley.com/datasets/t5sssfgn2v/3., Competing Interests: The authors state that they have no competing financial or personal interests that could have influenced the work reported in this study., (© 2022 The Author(s). Published by Elsevier Inc.)

Guava (Psidium guajava) is a delicious fruit native to Mexico, Central or South America, and the Caribbean region. It's high in vitamin C, Calcium, Pectins and is a good source of fiber. Due to concerns with natural and environmental resources, technical issues, and other impediments, the production level decreases day-to-day. However, we'll concentrate on the most critical challenges, such as infections that affect guava plants, fruits, and disease outbreak prevention through early identification. Besides, the early recognition of guava disease using the expert system will lead to higher yields that will eventually help guava farmers reduce their economic losses. In the recent era, image processing and computer vision have been broadly applied to recognize multiple diseases that are not identified with the naked eyes. This article presents a dataset of guava images containing both leaves and fruit images (diseases affected and disease-free) are classified into six classes: for guava fruits-Phytophthora, Scab, Styler end Rot, and Disease-free fruit, and for guava leaves-Red Rust, and diseases-free leave. All images are basically captured from the guava garden located at Bangladesh Agricultural University in July when the guava fruits are almost ripened, and the infections are found in guava plants. This dataset is mainly for those researchers who work with computer vision, machine learning, and deep learning to develop a system that recognizes the guava disease to assist guava farmers in their cultivation., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s). Published by Elsevier Inc.)

Sunflowers are agricultural seed crops that can be used for essential edible oils and ornamental purposes. This cash crop is primarily cultivated in North and South America. Sunflower crops are prone to various diseases, insects, and nematodes, resulting in a wide range of production losses. Digital image processing and computer vision approaches have been widely utilized to categorize and detect plant diseases including leaves, fruits, and flowers over the last few decades. Early diagnosis of infections in sunflowers helps to prevent them from spreading throughout the farm and reducing financial losses to the farmers. This article offers a resourceful dataset of sunflower leaves and flowers that will help the researchers in developing effective algorithms for the detection of diseases. The dataset contains healthy and affected sunflower leaves and flowers with downy mildew, gray mold, and leaf scars. The images were captured manually between 25 th to 29 th November 2021 from the demonstration farm of Bangladesh Agricultural Research Institute (BARI) at Gazipur in cooperation with its one domain expert when the sunflower plants were about to bloom and the maximum diseases can be found. The dataset is hosted by the Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Bangladesh and freely available at https://data.mendeley.com/datasets/b83hmrzth8/1., Competing Interests: The authors declare that they have no conflict of interests from any competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

A case control study was conducted in Dhaka city to measure the energy intake and expenditure among 220 obese children of 4-10 years age group and 220 randomly selected age and sex matched controls. A 24 hour dietary recall was used to assess the energy intake. Data on energy expenditure was obtained by 24 hour physical activity recall. The mean energy intake of obese children (2056 +/- 751 kcal/d) was found to be significantly higher (P<0.001) compared to the non obese (1508 +/- 529 kcal/d). No sex difference was found in both obese and non obese groups. Mean energy expenditure of the obese children (1868 +/- 313 kcal/d vs 1495 +/- 200 kcal/d) was significantly higher than the non-obese (P<0.001). Of the obese, boys expended more energy than the girls (P=0.01). Furthermore, physical activity level (1.40 +/- 0.09 vs 1.35 +/- 0.14) was significantly higher among the non obese than their obese counterpart (P<0.001). However, energy balance was significantly higher among the obese (P<0.001). Dose response of energy balance shows the estimated relative risk of obesity increases with higher level of energy balance (P<0.001). The findings of this study revealed that energy balance as a result of higher energy intake and lower energy expenditure was one of the important risk factors for the development of obesity among the children of Dhaka city. Thus, appropriate interventions like behavioural change regarding food habits and physical activities are recommended.