Synthesis of Microwave Functionalized, Nanostructured Polylactic Co-Glycolic Acid (nfPLGA) for Incorporation into Hydrophobic Dexamethasone to Enhance Dissolution.

The low solubility and slow dissolution of hydrophobic drugs is a major challenge for the pharmaceutical industry. In this paper, we present the synthesis of surface-functionalized poly(lactic-co-glycolic acid) (PLGA) nanoparticles for incorporation into corticosteroid dexamethasone to improve its in vitro dissolution profile. The PLGA crystals were mixed with a strong acid mixture, and their microwave-assisted reaction led to a high degree of oxidation. The resulting nanostructured, functionalized PLGA (nfPLGA), was quite water-dispersible compared to the original PLGA, which was non-dispersible. SEM-EDS analysis showed 53% surface oxygen concentration in the nfPLGA compared to the original PLGA, which had only 25%. The nfPLGA was incorporated into dexamethasone (DXM) crystals via antisolvent precipitation. Based on SEM, RAMAN, XRD, TGA and DSC measurements, the nfPLGA-incorporated composites retained their original crystal structures and polymorphs. The solubility of DXM after nfPLGA incorporation (DXM-nfPLGA) increased from 6.21 mg/L to as high as 87.1 mg/L and formed a relatively stable suspension with a zeta potential of -44.3 mV. Octanol-water partitioning also showed a similar trend as the logP reduced from 1.96 for pure DXM to 0.24 for DXM-nfPLGA. In vitro dissolution testing showed 14.0 times higher aqueous dissolution of DXM-nfPLGA compared to pure DXM. The time for 50% (T50) and 80% (T80) of gastro medium dissolution decreased significantly for the nfPLGA composites; T50 reduced from 57.0 to 18.0 min and T80 reduced from unachievable to 35.0 min. Overall, the PLGA, which is an FDA-approved, bioabsorbable polymer, can be used to enhance the dissolution of hydrophobic pharmaceuticals and this can lead to higher efficacy and lower required dosage.

Seroprevalence of Anti-SARS-CoV-2 Antibodies in Chattogram Metropolitan Area, Bangladesh.

Seroprevalence studies of COVID-19 are used to assess the degree of undetected transmission in the community and different groups such as health care workers (HCWs) are deemed vulnerable due to their workplace hazards. The present study estimated the seroprevalence and quantified the titer of anti-SARS-CoV-2 antibody (IgG) and its association with different factors. This cross-sectional study observed HCWs, in indoor and outdoor patients (non-COVID-19) and garment workers in the Chattogram metropolitan area (CMA, N = 748) from six hospitals and two garment factories. Qualitative and quantitative ELISA were used to identify and quantify antibodies (IgG) in the serum samples. Descriptive, univariable, and multivariable statistical analysis were performed. Overall seroprevalence and among HCWs, in indoor and outdoor patients, and garment workers were 66.99% (95% CI: 63.40-70.40%), 68.99% (95% CI: 63.8-73.7%), 81.37% (95% CI: 74.7-86.7%), and 50.56% (95% CI: 43.5-57.5%), respectively. Seroprevalence and mean titer was 44.47% (95% CI: 38.6-50.4%) and 53.71 DU/mL in the non-vaccinated population, respectively, while it was higher in the population who received a first dose (61.66%, 95% CI: 54.8-68.0%, 159.08 DU/mL) and both doses (100%, 95% CI: 98.4-100%, 255.46 DU/mL). This study emphasizes the role of vaccine in antibody production; the second dose of vaccine significantly increased the seroprevalence and titer and both were low in natural infection.

Development of nano structured graphene oxide incorporated dexamethasone with enhanced dissolution

We present the development of nanographene oxide (nGO) incorporated dexamethasone (DXM) composites (DXM-nGO) with enhanced aqueous solubility. Antisolvent precipitation was used for successful incorporation of nGO into DXM, a popular COVID-19 drug. The study focuses on morphology and dissolution performance of the formulated DXM-nGO, which were characterized using scanning electron microscopy (SEM), Raman spectroscopy, X-ray diffraction (XRD), Differential scanning calorimetry (DSC) and Thermogravimetric analysis (TGA). In vitro dissolution profile showed that for a DXM-nGO containing 0.5% nGO showed that time for 50% dissolution (T50) dropped from 39 min to 24 min, while time for 80% dissolution (T80) went from not dissolved state to 56 min for the same composite. In general, the nGO incorporation into DXM showed enhanced solubility, and in vitro dissolution data suggest that the nGO may be a candidate for successful bioavailability improvement.

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach.

Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner's premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way.