Surgery vs. biopsy in the treatment of butterfly glioblastoma: a systematic review and meta-analysis

: Butterfly glioblastomas (bGBM) are grade IV gliomas that spread to bilateral hemispheres by infiltrating the corpus callosum. Data on the effect of surgery are limited to small case series. The aim of this meta-analysis was to compare resection vs. biopsy in terms of survival outcomes and postoperative complications. A systematic review of the literature was conducted using PubMed, EMBASE, and Cochrane databases through March 2021 in accordance with the PRISMA checklist. Pooled hazard ratios were calculated and meta-analyzed in a random-effects model including assessment of heterogeneity. Out of 3367 articles, seven studies were included with 293 patients. Surgical resection was significantly associated with longer overall survival (HR 0.39, 95%CI 0.2-0.55) than biopsy. Low heterogeneity was observed (I2: 0%). In further analysis, the effect persisted in extent of resection subgroups of both ≥80% and <80%. No statistically significant difference between surgery and biopsy was detected in terms of postoperative complications, although these were numerically larger for surgery. In patients with bGBM, surgical resection was associated with longer survival prospects compared with biopsy

Intelligent Telehealth in Pharmacovigilance: A Future Perspective

Pharmacovigilance improves patient safety by detecting and preventing adverse drug events. However, challenges exist that limit adverse drug event detection, resulting in many adverse drug events being underreported or inaccurately reported. One challenge includes having access to large data sets from various sources including electronic health records and wearable medical devices. Artificial intelligence, including machine learning methods, such as natural language processing and deep learning, can detect and extract information about adverse drug events, thus automating the pharmacovigilance process and improving the surveillance of known and documented adverse drug events. In addition, with the increased demand for telehealth services, for managing both acute and chronic diseases, artificial intelligence methods can play a role in detecting and preventing adverse drug events. In this review, we discuss two use cases of how artificial intelligence methods may be useful to improve the quality of pharmacovigilance and the role of artificial intelligence in telehealth practices

A sustainable approach for the degradation kinetics study and stability assay of the SARS-CoV-2 oral antiviral drug Molnupiravir

Abstract Molnupiravir (MPV) is the first direct-acting oral antiviral drug that effectively decreases nasopharyngeal infections with SARS-CoV-2 virus. The stability of MPV was tested by subjecting the drug to various stress conditions. The drug is liable to oxidative, acidic, and alkaline degradation and showed significant stability against thermal degradation. Mass spectrometry identified the degradation products and guided suggestion of the degradation patterns. Interestingly, while inspecting the UV-absorption spectra, we observed no absorbance at 270 nm for the products of the three degradation pathways (c.f. intact MPV). Direct spectrophotometry seemed a solution that perfectly fit the purpose of the stability assay method in our case. It avoids sophisticated instrumentation and complex mathematical data manipulation. The method determined MPV accurately (100.32% ± 1.62) and selectively (99.49% ± 1.63) within the linear range of 1.50 × 10–5–4.0 × 10–4 M and down to a detection limit of 0.48 × 10–5 M. The proposed method is simple and does not require any preliminary separation or derivatization steps. The procedure proved its validity as per the ICH recommendations. The specificity was assessed in the presence of up to 90% degradation products. The study evaluated the greenness profile of the proposed analytical procedure using the National Environmental Methods Index (NEMI), the Analytical Eco-Scale, and the Green Analytical Procedure Index (GAPI). The three metrics unanimously agreed that the developed procedure results in a greener profile than the reported method. The method investigated the degradation reactions' kinetics and evaluated the reaction order, rate constant, and half-life time for each degradation process

Synthesis and Biological Evaluation of Some Novel Thiazole-Based Heterocycles as Potential Anticancer and Antimicrobial Agents

A novel series of thiazole-based heterocycles was synthesized using 1,3-dipolar cycloaddition reactions in the presence of chitosan-grafted-poly(vinylpyridine) as an eco-friendly biopolymeric basic catalyst. The molecular structure of the synthesized compounds was illustrated by spectroscopic and elemental analysis. Various in vitro biological assays were performed to explore the potential antitumor, antimicrobial and hepatoprotective activities of the newly synthesized compounds. The cytotoxic activities were assessed against human hepatocellular carcinoma (HepG-2), colorectal carcinoma (HCT-116) and breast cancer (MCF-7) cell lines and results revealed that all compounds displayed antitumor activities with the chlorine-containing derivatives, 11c and 6g, being the most potent. The majority of the tested thiazole derivatives exhibited satisfactory antibacterial activity towards the used gram positive and gram-negative bacterial species. Moreover, many derivatives showed weak hepatoprotective activity against CCl4-induced hepatotoxicity

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field

Stereotactic radiosurgery versus whole-brain radiotherapy after intracranial metastasis resection: a systematic review and meta-analysis

Background: In patients with one to three brain metastases who undergo resection, options for post-operative treatments include whole-brain radiotherapy (WBRT) or stereotactic radiosurgery (SRS) of the resection cavity. In this meta-analysis, we sought to compare the efficacy of each post-operative radiation modality with respect to tumor recurrence and survival. Methods: Pubmed, Embase and Cochrane databases were searched through June 2016 for cohort studies reporting outcomes of SRS or WBRT after metastasis resection. Pooled effect estimates were calculated using fixed-effect and random-effect models for local recurrence, distant recurrence, and overall survival. Results: Eight retrospective cohort studies with 646 patients (238 with SRS versus 408 with WBRT) were included in the analysis. Comparing SRS to WBRT, the overall crude risk ratio using the fixed-effect model was 0.59 for local recurrence (95%-CI: 0.32–1.09, I2: 3.35%, P-heterogeneity = 0.36, 3 studies), 1.09 for distant recurrence (95%-CI: 0.74–1.60, I2: 50.5%, P-heterogeneity = 0.13; 3 studies), and 2.99 for leptomeningeal disease (95% CI 1.55–5.76; I2: 14.4% p-heterogeneity: 0.28; 2 studies). For the same comparison, the risk ratio for median overall survival was 0.47 (95% CI: 0.41–0.54; I2: 79.1%, P-heterogeneity < 0.01; 4 studies) in a fixed-effect model, but was no longer significant (0.63; 95%-CI: 0.40–1.00) in a random-effect model. SRS was associated with a lower risk of leukoencephalopathy (RR: 0.15, 95% CI: 0.07–0.33, 1 study), yet with a higher risk of radiation-necrosis (RR: 19.4, 95% CI: 1.21–310, 1 study). Conclusion: Based on retrospective cohort studies, the results of this study suggest that SRS of the resection cavity may offer comparable survival and similar local and distant control as adjuvant WBRT, yet may be associated with a higher risk for developing leptomeningeal disease. Future research on SRS should focus on achieving a better understanding of the various factors that may favor SRS over WBRT

Performance of a Web-Based Reference Database With Natural Language Searching Capabilities: Usability Evaluation of DynaMed and Micromedex With Watson

BackgroundEvidence-based point-of-care information (POCI) tools can facilitate patient safety and care by helping clinicians to answer disease state and drug information questions in less time and with less effort. However, these tools may also be visually challenging to navigate or lack the comprehensiveness needed to sufficiently address a medical issue. ObjectiveThis study aimed to collect clinicians’ feedback and directly observe their use of the combined POCI tool DynaMed and Micromedex with Watson, now known as DynaMedex. EBSCO partnered with IBM Watson Health, now known as Merative, to develop the combined tool as a resource for clinicians. We aimed to identify areas for refinement based on participant feedback and examine participant perceptions to inform further development. MethodsParticipants (N=43) within varying clinical roles and specialties were recruited from Brigham and Women’s Hospital and Massachusetts General Hospital in Boston, Massachusetts, United States, between August 10, 2021, and December 16, 2021, to take part in usability sessions aimed at evaluating the efficiency and effectiveness of, as well as satisfaction with, the DynaMed and Micromedex with Watson tool. Usability testing methods, including think aloud and observations of user behavior, were used to identify challenges regarding the combined tool. Data collection included measurements of time on task; task ease; satisfaction with the answer; posttest feedback on likes, dislikes, and perceived reliability of the tool; and interest in recommending the tool to a colleague. ResultsOn a 7-point Likert scale, pharmacists rated ease (mean 5.98, SD 1.38) and satisfaction (mean 6.31, SD 1.34) with the combined POCI tool higher than the physicians, nurse practitioner, and physician’s assistants (ease: mean 5.57, SD 1.64, and satisfaction: mean 5.82, SD 1.60). Pharmacists spent longer (mean 2 minutes, 26 seconds, SD 1 minute, 41 seconds) on average finding an answer to their question than the physicians, nurse practitioner, and physician’s assistants (mean 1 minute, 40 seconds, SD 1 minute, 23 seconds). ConclusionsOverall, the tool performed well, but this usability evaluation identified multiple opportunities for improvement that would help inexperienced users