Preventing Adverse Drug Reactions After Hospital Discharge (PADR-AD): Protocol for a randomised-controlled trial in older people

BackgroundAdverse drug reactions (ADRs) and adverse drug events (ADEs) in older people contribute to a significant proportion of hospital admissions and are common following discharge. Effective interventions are therefore required to combat the growing burden of preventable ADRs. The Prediction of Hospitalisation due to Adverse Drug Reactions in Elderly Community Dwelling Patients (PADR-EC) score is a validated risk score developed to assess the risk of ADRs in people aged 65 years and older and has the potential to be utilised as part of an intervention to reduce ADRs.ObjectivesThis trial was designed to investigate the effectiveness of an intervention to reduce ADR incidence in older people and to obtain further information about ADRs and ADEs in the 12–24 months following hospital discharge.MethodsThe study is an open-label randomised-controlled trial to be conducted at the Royal Hobart Hospital, a 500-bed public hospital in Tasmania, Australia. Community-dwelling patients aged 65 years and older with an unplanned overnight admission to a general medical ward will be recruited. Following admission, the PADR-EC ADR score will be calculated by a research pharmacist, with the risk communicated to clinicians and discussed with participants. Following discharge, nominated general practitioners and community pharmacists will receive the risk score and related medication management advice to guide their ongoing care of the patient. Follow-up with participants will occur at 3 and 12 and 18 and 24 months to identify ADRs and ADEs. The primary outcome is moderate-severe ADRs at 12 months post-discharge, and will be analysed using the cumulative incidence proportion, survival analysis and Poisson regression.SummaryIt is hypothesised that the trial will reduce ADRs and ADEs in the intervention population. The study will also provide valuable data on post-discharge ADRs and ADEs up to 24 months post-discharge.     

FDA“治疗等效性评价供企业用指导原则”（草案）介绍

美国食品药品管理局（FDA）于2022年7月发布了"治疗等效性评价供企业用指导原则"（草案）。该指导原则阐明了FDA治疗等效性的标准以及治疗等效性编码系统，目的是准确评价仿制药与参比制剂的治疗等效性并通过治疗等效性代码，在"橙皮书"中迅速检索到治疗等效的仿制药。而中国目前尚无类似的指导原则，详细介绍FDA该指导原则主要内容，期望对中国加强仿制药的治疗等效性评价和加速完善和实施符合国情的治疗等效性编码系统有所帮助。     

The role of SPP1 as a prognostic biomarker and therapeutic target in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignancies and has a low 5-year survival rate. Mounting evidence suggests that oral potentially malignant disorders, such as oral leukoplakia (OLK), may progress to HNSCC. Given that OLK and HNSCC are often insidious and asymptomatic, the identification of markers of OLK malignant transformation and therapeutic targets in HNSCC is critical. Using various online tools and publicly available gene expression datasets, the secreted phosphoprotein 1 gene (SPP1) was identified as a significant differentially expressed gene among OLK, HNSCC, and non-cancerous tissues. SPP1 mRNA levels were elevated in HNSCC tissues and were associated with cancer stage, tumor grade, and human papillomavirus infection status. High SPP1 mRNA levels were correlated with poor overall survival of HNSCC patients. In contrast, SPP1 mutations were not significantly associated with overall survival, although their frequency in HNSCC was very low (0.6%). Furthermore, SPP1 expression levels in HNSCC were positively correlated with the infiltration of CD4⁺ cells, macrophages, neutrophils, and dendritic cells. The study results suggest that SPP1 may represent a diagnostic and prognostic biomarker, as well as a potential therapeutic target in HNSCC.     

Automatic segmentation of thoracic CT images using three deep learning models

PurposeDeep learning (DL) techniques are widely used in medical imaging and in particular for segmentation. Indeed, manual segmentation of organs at risk (OARs) is time-consuming and suffers from inter- and intra-observer segmentation variability. Image segmentation using DL has given very promising results. In this work, we present and compare the results of segmentation of OARs and a clinical target volume (CTV) in thoracic CT images using three DL models.Materials and methodsWe used CT images of 52 patients with breast cancer from a public dataset. Automatic segmentation of the lungs, the heart and a CTV was performed using three models based on the U-Net architecture. Three metrics were used to quantify and compare the segmentation results obtained with these models: the Dice similarity coefficient (DSC), the Jaccard coefficient (J) and the Hausdorff distance (HD).ResultsThe obtained values of DSC, J and HD were presented for each segmented organ and for the three models. Examples of automatic segmentation were presented and compared to the corresponding ground truth delineations. Our values were also compared to recent results obtained by other authors.ConclusionThe performance of three DL models was evaluated for the delineation of the lungs, the heart and a CTV. This study showed clearly that these 2D models based on the U-Net architecture can be used to delineate organs in CT images with a good performance compared to other models. Generally, the three models present similar performances. Using a dataset with more CT images, the three models should give better results.     

Co-precipitates of lumefantrine-Eudragit E PO for dissolution improvement

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Synthesis and characterization of photosensitive gelatin-based hydrogels for photodynamic therapy in HeLa-CCL2 cell line

BackgroundHydrogel systems are increasingly gaining visibility involving biomedicine, tissue engineering, environmental treatments, and drug delivery systems. These systems have a three-dimensional network composition and high-water absorption capacity, are biocompatible, allowing them to become an option as photosensitizer carriers (PS) for applications in Photodynamic Therapy (PDT) protocols.MethodsA nanohydrogel system (NAHI), encapsulated with chloroaluminium phthalocyanine (ClAlPc) was synthesized for drug delivery.. NAHI was synthesized using gelatin as based polymer by the chemical cross-linking technique. The drug was encapsulated by immersing the hydrogel in a 1.0 mg.mL⁻¹ ClAlPc solution. The external morphology of NAHI was examined by scanning electron microscopy (SEM). The degree of swelling of the synthesized system was evaluated to determine the water absorption potential. The produced nanohydrogel system was characterized by photochemical, photophysical and photobiologial studies.ResultsThe images from the SEM analysis showed the presence of three-dimensional networks in the formulation. The swelling test demonstrated that the nanohydrogel freeze-drying process increases its water holding capacity. All spectroscopic results showed excellent photophysical parameters of the drug studied when served in the NAHI system. The incorporation efficiency was 70%. The results of trypan blue exclusion test have shown significant reduction (p < 0.05) in the cell viability for all groups treated with PDT, in all concentrations tested. In HeLa cells, PDT mediated by 0,5 mg.mL⁻¹ ClAlPc encapsulated in NAHI showed a decrease in survival close to 95%. In the internalization cell study was possible to observe the internalization of phthalocyanine after one hour of incubation, at 37 °C, with the the accumulation of PS in the cytoplasm and inside the nucleus at both concentrations tested.ConclusionsGiven the peculiar performance of the selected system, the resulting nanohydrogel is a versatile platform and display potential applications as controlled delivery systems of photosensitizer for photodynamic therapy application.     

Comprehensive interrogation of gene lists from genome-scale cancer screens with oncoEnrichR

Genome-scale screening experiments in cancer produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogation of gene lists frequently represents a significant and time-consuming undertaking, in which experimental biologists typically combine results from a variety of bioinformatics resources in an attempt to portray and understand cancer relevance. As a means to simplify and strengthen the support for this endeavor, we have developed oncoEnrichR, a flexible bioinformatics tool that allows cancer researchers to comprehensively interrogate a given gene list along multiple facets of cancer relevance. oncoEnrichR differs from general gene set analysis frameworks through the integration of an extensive set of prior knowledge specifically relevant for cancer, including ranked gene-tumor type associations, literature-supported proto-oncogene and tumor suppressor gene annotations, target druggability data, regulatory interactions, synthetic lethality predictions, as well as prognostic associations, gene aberrations and co-expression patterns across tumor types. The software produces a structured and user-friendly analysis report as its main output, where versions of all underlying data resources are explicitly logged, the latter being a critical component for reproducible science. We demonstrate the usefulness of oncoEnrichR through interrogation of two candidate lists from proteomic and CRISPR screens. oncoEnrichR is freely available as a web-based service hosted by the Galaxy platform ( https://oncotools.elixir.no ), and can also be accessed as a stand-alone R package ( https://github.com/sigven/oncoEnrichR ).