Application of Biomaterials in the Development of Hydrogel-Forming Microneedles Integrated with a Cyclodextrin Drug Reservoir for Improved Pharmacokinetic Profiles of Telmisartan

Telmisartan (TEL) is a promising antihypertensive agent among other angiotensin receptor blockers. However, its oral application is limited by its poor water solubility. This study presents the successful utilization of biomaterial-based hydrogel-forming microneedles integrated with a direct compressed tablet reservoir (HFMN-DCT) for the transdermal delivery of telmisartan in the treatment of hypertension. The combination of PVP, PVA, and tartaric acid was used in the HFMN formulation. A range of cross-linking temperatures and times were employed to optimize the characteristics of the HFMN. The HFMN exhibited excellent swelling capacity, mechanical strength, and insertion properties. Additionally, the poorly soluble characteristic of TEL was improved by the inclusion complex formulation with β-cyclodextrin (βCD). Phase solubility analysis showed an Ap-type diagram, indicating a higher-order complex between TEL and βCD, with respect to βCD. A ratio of TEL:βCD of 1:4 mM demonstrates the highest solubility enhancement of TEL. The inclusion complex formation was confirmed by FTIR, XRD, DSC, and molecular docking studies. A significantly higher release of TEL (up to 20-fold) from the inclusion complex was observed in the in vitro release study. Subsequently, a DCT reservoir was developed using various concentrations of sodium starch glycolate. Essentially, both the HFMN and DCT reservoir exhibit hemocompatibility and did not induce any skin irritation. The optimized combination of the HFMN-DCT reservoir showed an ex vivo permeation profile of 83.275 ± 2.405%. Notably, the proposed system showed superior pharmacokinetic profiles in the in vivo investigation using male Wistar rats. Overall, this study highlights the potential of HFMN-DCT reservoir systems as a versatile platform for transdermal drug delivery applications.

Matrix Metalloproteinase-9 Testing of Golden Rice Cookies With Piper Crocatum Active Extract for Preventing Foot Ulcers in Patients With Diabetes: Protocol for a Randomized Controlled Trial

BACKGROUND

Diabetic foot ulcers (DFUs) present a formidable challenge to both patients and health care systems. DFUs significantly reduce the quality of life for patients, prolong hospital stays, and are the cause of approximately 70,000 lower limb amputations across the globe annually. Prevention of DFUs primarily involves the optimization of blood sugar levels and the effective management of complications, particularly peripheral neuropathy. Golden Rice has been proven to lower blood sugar levels due to its beta-carotene content, and Piper crocatum (P. crocatum) has been found to be effective in reducing the risk factors of DFUs through biomolecular regulation because of its polyphenol content.

OBJECTIVE

The principal objective of this study is to identify the efficacy of P. crocatum-enriched cookies, with Golden Rice as their primary ingredient, in preventing DFUs. The evaluation will center on their impact on the expression of matrix metalloproteinase-9 (MMP-9), a pivotal factor in the development of DFUs.

METHODS

This study is an experimental clinical research that follows the randomized controlled trial method and uses a single-blind design. The participants in the study are outpatients from primary health centers in Makassar, Indonesia, who have been diagnosed with diabetes mellitus. The sample for the study will be randomly selected and subsequently categorized into 2 groups: the intervention group and the control group. The intervention group consumes P. crocatum-enriched Golden Rice cookies, while the control group receives cookies without these additives. The participants from both groups will consume their respective cookies (packaged identically) twice a day for 14 days. The cookies will be prepared according to a modified recipe with an emphasis on low glucose content, resulting in 51 calories per cookie, comprising 1% carbohydrates, 6% fat, 4% cholesterol, and 4% fiber, excluding gluten, sugar, and salt. They will be baked at 158°C for 20 minutes. The process involves the addition of 20% Golden Rice and 10% P. crocatum ethanol extract, both prepared via maceration with 96% ethanol. The dependent variable in this study is the expression of gelatinases matrix metalloproteinase, to be assessed at 2 distinct time points-preintervention (pretest) and postintervention (posttest)-with the evaluation conducted through the western blotting method.

RESULTS

The recruitment and testing phase started in January 2024. The study is scheduled to be completed by the end of March 2024. Data analysis will commence in April 2024, and the publication of the results is anticipated in the same year (2024). The study will report on the changes in primary data, encompassing gelatinases matrix metalloproteinase, as well as secondary data, including the ankle-brachial index, neuropathy score, and random blood glucose level.

CONCLUSIONS

The findings of this trial are expected to significantly impact the selection of strategies by health care practitioners to enhance diabetes self-management, particularly in the domain of therapeutic snacking, for patients diagnosed with diabetes mellitus.

TRIAL REGISTRATION

Thai Clinical Trials Registry TCTR20230502001; https://www.thaiclinicaltrials.org/show/TCTR20230502001.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/49940.

A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI)

The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation's economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2's pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset.

HPLC-UV method validation for quantification of β-carotene in the development of sustained release supplement formulation containing solid dispersion-floating gel in situ

Despite the health benefits of β-carotene, its activity has been hampered by poor aqueous solubility and low oral bioavailability. Therefore, it is crucial to develop a new approach to overcome these problems. In this study, we developed a dry powder supplement comprising a combination approach of solid dispersion and floating gel in situ of β-carotene to enhance the solubility and achieve sustained release behavior. Here, we validated an HPLC method to quantify β-carotene as per the guidelines from ICH. The analytical method was validated in methanol and Fasted-State Simulated Gastric Fluid (FaSSGF) to determine β-carotene in recovery and in vitro release studies, respectively. A simple HPLC method using Xselect CSH™ C18 column (Waters, 3.0 × 150 mm) with the particle size of 3.5 µm was validated with 100% acetonitrile as the mobile phase. The calibration curves were found to be linear with LLOQ values < 3 ng/mL. Importantly, the method was accurate and precise without a carry over effect and successfully applied to determine the β-carotene concentration in the content analysis of the compound and in vitro drug release from floating gel in situ laden with solid dispersion formulations. The sensitivity of the method obtained here offers a wide potential use in various applications in drug delivery systems.

Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage

COVID-19 has imposed many challenges and barriers on traditional healthcare systems due to the high risk of being infected by the coronavirus. Modern electronic devices like smartphones with information technology can play an essential role in handling the current pandemic by contributing to different telemedical services. This study has focused on determining the presence of this virus by employing smartphone technology, as it is available to a large number of people. A publicly available COVID-19 dataset consisting of 33 features has been utilized to develop the aimed model, which can be collected from an in-house facility. The chosen dataset has 2.82% positive and 97.18% negative samples, demonstrating a high imbalance of class populations. The Adaptive Synthetic (ADASYN) has been applied to overcome the class imbalance problem with imbalanced data. Ten optimal features are chosen from the given 33 features, employing two different feature selection algorithms, such as K Best and recursive feature elimination methods. Mainly, three classification schemes, Random Forest (RF), eXtreme Gradient Boosting (XGB), and Support Vector Machine (SVM), have been applied for the ablation studies, where the accuracy from the XGB, RF, and SVM classifiers achieved 97.91%, 97.81%, and 73.37%, respectively. As the XGB algorithm confers the best results, it has been implemented in designing the Android operating system base and web applications. By analyzing 10 users’ questionnaires, the developed expert system can predict the presence of COVID-19 in the human body of the primary suspect. The preprocessed data and codes are available on the GitHub repository.

Mobile Application for the Blind and Their Family

Many people want to use gadgets, but they cannot due to some reasons. One of the reasons is the person who is visually impaired and some children are challenged by birth. To overcome this problem, we have designed a mobile application to make communication easier between the blind and their family or their friends. In this project, we want to utilize some of the technology that already exists to develop a mobile application that can be used by the blind to communicate easily to their family.

Pharmaceutical Prospects of Bee Products: Special Focus on Anticancer, Antibacterial, Antiviral, and Antiparasitic Properties

Bee products have long been used in traditional healing practices to treat many types of disorders, including cancer and microbial-related diseases. Indeed, several chemical compounds found in bee products have been demonstrated to display anticancer, antibacterial, antiviral, and antiparasitic properties. With the improvement of research tools and in view of recent advances related to bee products, this review aims to provide broad yet detailed insight into the pharmaceutical prospects of bee products such as honey, propolis, bee pollen, royal jelly, bee bread, beeswax, and bee venom, in the domain of cancer and infectious disease management. Available literature confirms the efficacy of these bee products in the alleviation of cancer progression, inhibition of bacterial and viral proliferation, and mitigation of parasitic-related symptoms. With such potentials, bioactive components isolated from the bee products can be used as an alternative approach in the long-run effort to improve humans’ health at a personal and community level.

Lymphocutaneous Sporotrichosis Treated with Potassium Iodide with Development of Subclinical Hypothyroidism: Wolff-Chaikoff Effect?

Sporotrichosis is a subcutaneous mycotic infection caused by Sporothrix schenckii that is acquired by traumatic implantation. The diagnosis is established by demonstration of fungal elements on histopathology and culture. Potassium iodide, azole antifungals, and terbinafine are the treatment options available. In this article, we report a 60-year-old female with lymphocutaneous sporotrichosis that responded well to potassium iodide. However, subclinical hypothyroidism (Wolff–Chaikoff effect) was encountered as a side effect of therapy which was managed with thyroxine replacement. Knowledge about the Wolff-Chaikoff effect (WCE) is important for the dermatologist and reinforces the need for screening and monitoring of thyroid stimulating hormone (TSH) in patients where long duration therapy is being planned.