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The purpose of this study was to evaluate the potential of a newly modified cyclodextrin derivative, water-soluble β-cyclodextrin–epichlorohydrin (β-CD), as an effective drug carrier to enhance the poor solubility and bioavailability of galangin (GAL), a poorly water-soluble model drug. In this regard, inclusion complexes of GAL/β-CDP were prepared. UV-VIS spectrophotometry, Fourier-transform infrared spectroscopy (FTIR), X-ray crystallography (XRD), zeta potential analysis, particle size analysis, field emission scanning electron microscopy (FESEM), and transmission electron microscopy (TEM) were applied to characterize the synthesized GAL/β-CD. Michigan Cancer Foundation-7 (MCF-7; human breast cancer cells) and rat embryo fibroblast (REF; normal cells) were employed to examine the in vitro cytotoxic effects of GAL/β-CD using various parameters. The dye-based tests of MTT and crystal violet clearly exhibited that GAL/β-CD-treated cells had a reduced proliferation rate, an influence that was not found in the normal cell line. The cells’ death was found to follow apoptotic mechanisms, as revealed by the dye-based test of acridine orange/ethidium bromide (AO/EtBr), with the involvement of the mitochondria via caspase-3-mediated events, as manifested by the Rh 123 test. We also included a mouse model to examine possible in vivo toxic effects of GAL/β-CD. It appears that the inclusion complex does not have a significant influence on normal cells, as indicated by serum levels of kidney and liver enzymatic markers, as well as thymic and splenic mass indices. A similar conclusion was reached on the histological level, as manifested by the absence of pathological alterations in the liver, kidney, thymus, spleen, heart, and lung.

ABSTRACT Physiologically‐based pharmacokinetic (PBPK) modeling has become a major tool in drug discovery and development. Here, we describe the bottom‐up PBPK modeling approaches employed at AbbVie using Simcyp Simulator and evaluate the impact of three system parameters, GI physiology, P‐gp Relative Expression Factor (REF), and recombinant CYP enzyme (rCYP) intersystem extrapolation factor (ISEF), independently and in combination, on PBPK prediction performance through retrospective analysis of 8 clinical assets. Overall, the application of New GI physiology resulted in a considerable improvement in the prediction of oral absorption for most compounds compared to the Original GI physiology (Cmax: 76% vs. 43% within 3‐fold) when using the default P‐gp REF (1.5) and adjusted ISEF. Decreasing P‐gp REF to 0.5 resulted in additional improvement in the predictions of Cmax for P‐gp substrates (86% within 3‐fold). The observed plasma exposure‐time profiles and AUCINF are better predicted using the default rCYP ISEF values instead of individually adjusted values (48% vs. 43% within 3‐fold) when using the Original GI and default P‐gp REF (1.5). The combination of optimized parameters (New GI physiology, P‐gp REF of 0.5 and rCYP default ISEF) predicted the plasma exposures (AUCINF and Cmax) within 3‐fold for 81% and 86% of the tested simulations, respectively. In conclusion, the present study proposes an optimized strategy for bottom‐up PBPK model development in Simcyp Simulator. Retrospective comparison with observed clinical PK data is vital for model verification as well as further improvement in prospective predictions for future drug candidates.

Background Patient-reported outcomes (PROs) are commonly collected in clinical trials and should provide impactful evidence on the effect of interventions on patient symptoms and quality of life. However, it is unclear how PRO impact is currently realised in practice. In addition, the different types of impact associated with PRO trial results, their barriers and facilitators, and appropriate impact metrics are not well defined. Therefore, our objectives were: i) to determine the range of potential impacts from PRO clinical trial data, ii) identify potential PRO impact metrics and iii) identify barriers/facilitators to maximising PRO impact; and iv) to examine real-world evidence of PRO trial data impact based on Research Excellence Framework (REF) impact case studies. Methods Two independent investigators searched MEDLINE, EMBASE, CINAHL+, HMIC databases from inception until December 2018. Articles were eligible if they discussed research impact in the context of PRO clinical trial data. In addition, the REF 2014 database was systematically searched. REF impact case studies were included if they incorporated PRO data in a clinical trial. Results Thirty-nine publications of eleven thousand four hundred eighty screened met the inclusion criteria. Nine types of PRO trial impact were identified; the most frequent of which centred around PRO data informing clinical decision-making. The included publications identified several barriers and facilitators around PRO trial design, conduct, analysis and report that can hinder or promote the impact of PRO trial data. Sixty-nine out of two hundred nine screened REF 2014 case studies were included. 12 (17%) REF case studies led to demonstrable impact including changes to international guidelines; national guidelines; influencing cost-effectiveness analysis; and influencing drug approvals. Conclusions PRO trial data may potentially lead to a range of benefits for patients and society, which can be measured through appropriate impact metrics. However, in practice there is relatively limited evidence demonstrating directly attributable and indirect real world PRO-related research impact. In part, this is due to the wider challenges of measuring the impact of research and PRO-specific issues around design, conduct, analysis and reporting. Adherence to guidelines and multi-stakeholder collaboration is essential to maximise the use of PRO trial data, facilitate impact and minimise research waste. Trial registration Systematic Review registration PROSPERO CRD42017067799.

NOABSTRACTAssess whether ChatGPT 4.0 is accurate enough to perform research evaluations on journal articles to automate this time-consuming task.Test the extent to which ChatGPT-4 can assess the quality of journal articles using a case study of the published scoring guidelines of the UK Research Excellence Framework (REF) 2021 to create a research evaluation ChatGPT. This was applied to 51 of my own articles and compared against my own quality judgements.ChatGPT-4 can produce plausible document summaries and quality evaluation rationales that match the REF criteria. Its overall scores have weak correlations with my self-evaluation scores of the same documents (averaging r=0.281 over 15 iterations, with 8 being statistically significantly different from 0). In contrast, the average scores from the 15 iterations produced a statistically significant positive correlation of 0.509. Thus, averaging scores from multiple ChatGPT-4 rounds seems more effective than individual scores. The positive correlation may be due to ChatGPT being able to extract the author’s significance, rigour, and originality claims from inside each paper. If my weakest articles are removed, then the correlation with average scores (r=0.200) falls below statistical significance, suggesting that ChatGPT struggles to make fine-grained evaluations.The data is self-evaluations of a convenience sample of articles from one academic in one field.Overall, ChatGPT does not yet seem to be accurate enough to be trusted for any formal or informal research quality evaluation tasks. Research evaluators, including journal editors, should therefore take steps to control its use.This is the first published attempt at post-publication expert review accuracy testing for ChatGPT.

Natural rubber is a kind of indispensable biopolymers with great use and strategic importance in human society. However, its production relies almost exclusively on rubber-producing plants Hevea brasiliensis, which have high requirements for growth conditions, and the mechanism of natural rubber biosynthesis remains largely unknown. In the past two decades, details of the rubber chain polymerization and proteins involved in natural rubber biosynthesis have been investigated intensively. Meanwhile, omics and other advanced biotechnologies bring new insight into rubber production and development of new rubber-producing plants. This review summarizes the achievements of the past two decades in understanding the biosynthesis of natural rubber, especially the massive information obtained from the omics analyses. Possibilities of natural rubber biosynthesis in vitro or in genetically engineered microorganisms are also discussed.

The UK Research Excellence Framework (REF) is an assessment of the quality of research carried out in UK Higher Education Institutions (HEIs), performed in 7-year cycles. The outcome impacts the rankings and funding of UK HEIs, which afford the exercise high priority. Much of what REF measures is known to be biased against academics with protected characteristics: for example, women and ethnic minority researchers are less likely to win grants or be published in prestigious journals. Despite changes to REF since 2014, the risk remains that the process might amplify well-recognised existing disparities. The BMA Women in Academic Medicine and Medical Academic Staff Committee carried out a survey of UK clinical academics’ experiences of REF2021. The data indicated the persistence of activities previously characterised as ‘extremely harmful’ in Research England-commissioned work, affecting up to 10% of clinical academics. While acknowledging the limitations of the data, women appeared to be disproportionately affected.

Parkinson’s disease (PD) is a neurodegenerative condition generated by the dysfunction of brain cells and their 60–80% inability to produce dopamine, an organic chemical responsible for controlling a person’s movement. This condition causes PD symptoms to appear. Diagnosis involves many physical and psychological tests and specialist examinations of the patient’s nervous system, which causes several issues. The methodology method of early diagnosis of PD is based on analysing voice disorders. This method extracts a set of features from a recording of the person’s voice. Then machine-learning (ML) methods are used to analyse and diagnose the recorded voice to distinguish Parkinson’s cases from healthy ones. This paper proposes novel techniques to optimize the techniques for early diagnosis of PD by evaluating selected features and hyperparameter tuning of ML algorithms for diagnosing PD based on voice disorders. The dataset was balanced by the synthetic minority oversampling technique (SMOTE) and features were arranged according to their contribution to the target characteristic by the recursive feature elimination (RFE) algorithm. We applied two algorithms, t-distributed stochastic neighbour embedding (t-SNE) and principal component analysis (PCA), to reduce the dimensions of the dataset. Both t-SNE and PCA finally fed the resulting features into the classifiers support-vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multilayer perception (MLP). Experimental results proved that the proposed techniques were superior to existing studies in which RF with the t-SNE algorithm yielded an accuracy of 97%, precision of 96.50%, recall of 94%, and F1-score of 95%. In addition, MLP with the PCA algorithm yielded an accuracy of 98%, precision of 97.66%, recall of 96%, and F1-score of 96.66%.

Glioblastoma multiforme (GBM) is considered to be one of the most serious version of primary malignant tumors. Temozolomide (TMZ), an anti-cancer drug, is the most common chemotherapeutic agent used for patients suffering from GBM. However, due to its inherent instability, short biological half-life, and dose-limiting characteristics, alternatives to TMZ have been sought. In this study, the TMZ-loaded PLGA nanoparticles were prepared by employing the emulsion solvent evaporation technique. The prepared TMZ-PLGA-NPs were characterized using FT-IR, zeta potential analyses, XRD pattern, particle size estimation, TEM, and FE-SEM observations. The virotherapy, being safe, selective, and effective in combating cancer, was employed, and TMZ-PLGA-NPs and oncolytic Newcastle Disease Virus (NDV) were co-administered for the purpose. An AMHA1-attenuated strain of NDV was propagated in chicken embryos, and the virus was titrated in Vero-slammed cells to determine the infective dose. The in vitro cytotoxic effects of the TMZ, NDV, and the TMZ-PLGA-NPs against the human glioblastoma cancer cell line, AMGM5, and the normal cell line of rat embryo fibroblasts (REFs) were evaluated. The synergistic effects of the nano-formulation and viral strain combined therapy was observed on the cell lines in MTT viability assays, together with the Chou–Talalay tests. The outcomes of the in vitro investigation revealed that the drug combinations of NDV and TMZ, as well as NDV and TMZ-PLGA-NPs exerted the synergistic enhancements of the antitumor activity on the AMGM5 cell lines. The effectiveness of both the mono, and combined treatments on the capability of AMGM5 cells to form colonies were also examined with crystal violet dyeing tests. The morphological features, and apoptotic reactions of the treated cells were investigated by utilizing the phase-contrast inverted microscopic examinations, and acridine orange/propidium iodide double-staining tests. Based on the current findings, the potential for the use of TMZ and NDV as part of a combination treatment of GBM is significant, and may work for patients suffering from GBM.

Atherosclerosis (AS) is the pathologic basis of various cardiovascular and cerebrovascular events, with a high degree of heterogeneity among different arterial beds. However, mechanistic differences between arterial beds remain unexplored. The aim of this study was to explore key genes and potential mechanistic differences between AS in different arterial beds through bioinformatics analysis. Carotid atherosclerosis (CAS), femoral atherosclerosis (FAS), infrapopliteal atherosclerosis (IPAS), abdominal aortic atherosclerosis (AAS), and AS-specific differentially expressed genes (DEGs) were screened from the GSE100927 and GSE57691 datasets. Immune infiltration analysis was used to identify AS immune cell infiltration differences. Unsupervised cluster analysis of AS samples from different regions based on macrophage polarization gene expression profiles. Weighted gene co-expression network analysis (WGCNA) was performed to identify the most relevant module genes with AS. Hub genes were then screened by LASSO regression, SVM-REF, and single-gene differential analysis, and a nomogram was constructed to predict the risk of AS development. The results showed that differential expression analysis identified 5, 4, 121, and 62 CAS, FAS, IPAS, AAS-specific DEGs, and 42 AS-common DEGs, respectively. Immune infiltration analysis demonstrated that the degree of macrophage and mast cell enrichment differed significantly in different regions of AS. The CAS, FAS, IPAS, and AAS could be distinguished into two different biologically functional and stable molecular clusters based on macrophage polarization gene expression profiles, especially for cardiomyopathy and glycolipid metabolic processes. Hub genes for 6 AS (ADAP2, CSF3R, FABP5, ITGAX, MYOC, and SPP1), 4 IPAS (CLECL1, DIO2, F2RL2, and GUCY1A2), and 3 AAS (RPL21, RPL26, and RPL10A) were obtained based on module gene, gender stratification, machine learning algorithms, and single-gene difference analysis, respectively, and these genes were effective in differentiating between different regions of AS. This study demonstrates that there are similarities and heterogeneities in the pathogenesis of AS between different arterial beds.

PurposeTo study the role of prolonged prophylactic antibiotic therapy (PAT) in the prevention of Inter-costal drain (ICD) related infectious complications in patients with Blunt Trauma Chest (BTC).MethodsPatients of age 15 years and above with BTC requiring ICD were included. Patients with penetrating chest injuries, associated injuries/illnesses requiring antibiotic administration, need for mechanical ventilation, known pulmonary disease or immuno-compromised status and need for open thoracotomy were excluded. 120 patients were randomized equally to two groups; no prolonged PAT group (Group A) and prolonged PAT group (group B). Both group patients received one shot of injectable antibiotic prior to ICD insertion. Primary outcome measure was comparison of ICD related infectious complications (pneumonia, empyema and SSI) and secondary outcome measures included the duration of ICD, Length of Hospital stay (LOS) and in-hospital mortality in both the groups.ResultsInfectious complications (pneumonia, empyema and SSI) were seen in only one patient in antibiotic group, and none in no antibiotic group (p value = 0.500). Other complications such as post ICD pain scores, respiratory failure requiring ventilatory support, retained hemothorax or recurrent pneumothorax, did not show any statistical difference between both groups. Also, no significant difference was seen in both the groups in terms of mean duration of ICD (p value = 0.600) and LOS (p value = 0.259).mConclusionOverall prevalence of ICD related infectious complications are low in BTC patients. Definitive role of prolonged prophylactic antibiotics in reducing infectious complications and other associated co morbidities in BTC patients with ICDs could not be established.Trial registry detailsClinical Trial Registry, India (Trial registered at ctri.nic.in/clinical trials/login.php, number REF/2019/021704 dated 18/10/2019).