What empirical research has been undertaken on the ethics of clinical research in India?:A systematic scoping review and narrative synthesis

IntroductionThe post-2005 rise in clinical trials and clinical research conducted in India was accompanied by frequent reports of unethical practices, leading to a series of regulatory changes. We conducted a systematic scoping review to obtain an overview of empirical research pertaining to the ethics of clinical trials/research in India.MethodsOur search strategy combined terms related to ethics/bioethics, informed consent, clinical trials/research and India, across nine databases, up to November 2019. Peer-reviewed research exploring ethical aspects of clinical trials/research in India with any stakeholder groups was included. We developed an evidence map, undertook a narrative synthesis and identified research gaps. A consultation exercise with stakeholders in India helped contextualise the review and identify additional research priorities.ResultsTitles/Abstracts of 9699 articles were screened, full text of 282 obtained and 80 were included. Research on the ethics of clinical trials/research covered a wide range of topics, often conducted with little to no funding. Studies predominantly examined what lay (patients/public) and professional participants (eg, healthcare staff/students/faculty) know about topics such as research ethics or understand from the information given to obtain their consent for research participation. Easily accessible groups, namely ethics committee members and healthcare students were frequently researched. Research gaps included developing a better understanding of the recruitment-informed consent process, including the doctor-patient interaction, in multiple contexts and exploring issues of equity and justice in clinical trials/research.ConclusionThe review demonstrates that while a wide range of topics have been studied in India, the focus is largely on assessing knowledge levels across different population groups. This is a useful starting point, but fundamental questions remain unanswered about informed consent processes and broader issues of inequity that pervade the clinical trials/research landscape. A priority-setting exercise and appropriate funding mechanisms to support researchers in India would help improve the clinical trials/research ecosystem

Economic evaluation protocol of a short, all-oral bedaquiline-containing regimen for the treatment of rifampicin-resistant tuberculosis from the STREAM trial

Introduction: A December 2019 WHO rapid communication recommended the use of 9-month all-oral regimens for treating multidrug-resistant tuberculosis (MDR-TB). Besides the clinical benefits, they are thought to be less costly than the injectable-containing regimens, for both the patient and the health system. STREAM is the first randomised controlled trial with an economical evaluation to compare all-oral and injectable-containing 9–11-month MDR-TB treatment regimens. Methods and analysis: Health system costs of delivering a 9-month injectable-containing regimen and a 9-month all-oral bedaquiline-containing regimen will be collected in Ethiopia, India, Moldova and Uganda, using ‘bottom-up’ and ‘top-down’ costing approaches. Patient costs will be collected using questionnaires that have been developed based on the STOP-TB questionnaire. The primary objective of the study is to estimate the cost utility of the two regimens, from a health system perspective. Secondary objectives include estimating the cost utility from a societal perspective as well as evaluating the cost-effectiveness of the regimens, using both health system and societal perspectives. The effect measure for the cost–utility analysis will be the quality-adjusted life years (QALY), while the effect measure for the cost-effectiveness analysis will be the efficacy outcome from the clinical trial. Ethics and dissemination: The study has been evaluated and approved by the Ethics Advisory Group of the International Union Against Tuberculosis and Lung Disease and also approved by ethics committees in all participating countries. All participants have provided written informed consent. The results of the economic evaluation will be published in a peer-reviewed journal. Trial registration number: ISRCTN18148631

Economic evaluation of shortened, bedaquiline-containing treatment regimens for rifampicin-resistant tuberculosis (STREAM stage 2) : a within-trial analysis of a randomised controlled trial

Background: The STREAM stage 2 trial assessed two bedaquiline-containing regimens for rifampicin-resistant tuberculosis: a 9-month all-oral regimen and a 6-month regimen containing an injectable drug for the first 2 months. We did a within-trial economic evaluation of these regimens. Methods: STREAM stage 2 was an international, phase 3, non-inferiority randomised trial in which participants with rifampicin-resistant tuberculosis were randomly assigned (1:2:2:2) to the 2011 WHO regimen (terminated early), a 9-month injectable-containing regimen (control regimen), a 9-month all-oral regimen with bedaquiline (oral regimen), or a 6-month regimen with bedaquiline and an injectable for the first 2 months (6-month regimen). We prospectively collected direct and indirect costs and health-related quality of life data from trial participants until week 76 of follow-up. Cost-effectiveness of the oral and 6-month regimens versus control was estimated in four countries (oral regimen) and two countries (6-month regimen), using health-related quality of life for cost-utility analysis and trial efficacy for cost-effectiveness analysis. This trial is registered with ISRCTN, ISRCTN18148631. Findings: 300 participants were included in the economic analyses (Ethiopia, 61; India, 142; Moldova, 51; Uganda, 46). In the cost-utility analysis, the oral regimen was not cost-effective in Ethiopia, India, Moldova, and Uganda from either a provider or societal perspective. In Moldova, the oral regimen was dominant from a societal perspective. In the cost-effectiveness analysis, the oral regimen was likely to be cost-effective from a provider perspective at willingness-to-pay thresholds per additional favourable outcome of more than US$4500 in Ethiopia,$1900 in India, $3950 in Moldova, and$7900 in Uganda, and from a societal perspective at thresholds of more than $15 900 in Ethiopia,$3150 in India, and $4350 in Uganda, while in Moldova the oral regimen was dominant. In Ethiopia and India, the 6-month regimen would cost tuberculosis programmes and participants less than the control regimen and was highly likely to be cost-effective in both cost-utility analysis and cost-effectiveness analysis. Reducing the bedaquiline price from$1·81 to $1·00 per tablet made the oral regimen cost-effective in the provider-perspective cost-utility analysis in India and Moldova and dominate over the control regimen in the provider-perspective cost-effectiveness analysis in India. Interpretation: At current costs, the oral bedaquiline-containing regimen for rifampicin-resistant tuberculosis is unlikely to be cost-effective in many low-income and middle-income countries. The 6-month regimen represents a cost-effective alternative if injectable use for 2 months is acceptable. Funding: USAID and Janssen Research & Development

Synthesis of 2‑Acylbenzo[<i>b</i>]thiophenes via Cu-Catalyzed α‑C–H Functionalization of 2‑Halochalcones Using Xanthate

An efficient protocol is described for the synthesis of 2-acylbenzo­[<i>b</i>]­thiophenes from easily accessible 2-iodochalcones through α-C–H functionalization using Cu­(OAc)₂ catalyst and xanthate as sulfur source. Less reactive 2-bromochalcones also yielded the corresponding 2-acylbenzothiophenes in good yield. The reaction proceeds via in situ incorporation of sulfur followed by copper-catalyzed cyclization to generate 2-acylbenzothiophenes without external acyl source. The synthetic importance is showcased by synthesis of 1-(5-hydroxybenzothiophene-2-yl)­ethanone, which is a known pre-mRNA splicing modulator

Copper-Catalyzed Domino Synthesis of 2‑Arylthiochromanones through Concomitant C–S Bond Formations Using Xanthate as Sulfur Source

An efficient domino process for the synthesis of thioflavanones has been described using a copper catalyst without addition of any external ligand. A variety of thioflavanones have been synthesized from easily accessible 2′-iodochalcones or 2′-bromochalcones in excellent yield through in situ incorporation of sulfur using xanthate as an odorless sulfur source. This domino process proceeds through Cu-catalyzed C_(aryl)–S bond formation by the coupling reaction of xanthate with 2′-halochalcones followed by C–S bond cleavage of thioester then S–C bond formation by intramolecular Michael addition

Handcrafted Deep-Feature-Based Brain Tumor Detection and Classification Using MRI Images

An abnormal growth of cells in the brain, often known as a brain tumor, has the potential to develop into cancer. Carcinogenesis of glial cells in the brain and spinal cord is the root cause of gliomas, which are the most prevalent type of primary brain tumor. After receiving a diagnosis of glioblastoma, it is anticipated that the average patient will have a survival time of less than 14 months. Magnetic resonance imaging (MRI) is a well-known non-invasive imaging technology that can detect brain tumors and gives a variety of tissue contrasts in each imaging modality. Until recently, only neuroradiologists were capable of performing the tedious and time-consuming task of manually segmenting and analyzing structural MRI scans of brain tumors. This was because neuroradiologists have specialized training in this area. The development of comprehensive and automatic segmentation methods for brain tumors will have a significant impact on both the diagnosis and treatment of brain tumors. It is now possible to recognize tumors in photographs because of developments in computer-aided design (CAD), machine learning (ML), and deep learning (DL) approaches. The purpose of this study is to develop, through the application of MRI data, an automated model for the detection and classification of brain tumors based on deep learning (DLBTDC-MRI). Using the DLBTDC-MRI method, brain tumors can be detected and characterized at various stages of their progression. Preprocessing, segmentation, feature extraction, and classification are all included in the DLBTDC-MRI methodology that is supplied. The use of adaptive fuzzy filtering, often known as AFF, as a preprocessing technique for photos, results in less noise and higher-quality MRI scans. A method referred to as “chicken swarm optimization” (CSO) was used to segment MRI images. This method utilizes Tsallis entropy-based image segmentation to locate parts of the brain that have been injured. In addition to this, a Residual Network (ResNet) that combines handcrafted features with deep features was used to produce a meaningful collection of feature vectors. A classifier developed by combining DLBTDC-MRI and CSO can finally be used to diagnose brain tumors. To assess the enhanced performance of brain tumor categorization, a large number of simulations were run on the BRATS 2015 dataset. It would appear, based on the findings of these trials, that the DLBTDC-MRI method is superior to other contemporary procedures in many respects

Handcrafted Deep-Feature-Based Brain Tumor Detection and Classification Using MRI Images

An abnormal growth of cells in the brain, often known as a brain tumor, has the potential to develop into cancer. Carcinogenesis of glial cells in the brain and spinal cord is the root cause of gliomas, which are the most prevalent type of primary brain tumor. After receiving a diagnosis of glioblastoma, it is anticipated that the average patient will have a survival time of less than 14 months. Magnetic resonance imaging (MRI) is a well-known non-invasive imaging technology that can detect brain tumors and gives a variety of tissue contrasts in each imaging modality. Until recently, only neuroradiologists were capable of performing the tedious and time-consuming task of manually segmenting and analyzing structural MRI scans of brain tumors. This was because neuroradiologists have specialized training in this area. The development of comprehensive and automatic segmentation methods for brain tumors will have a significant impact on both the diagnosis and treatment of brain tumors. It is now possible to recognize tumors in photographs because of developments in computer-aided design (CAD), machine learning (ML), and deep learning (DL) approaches. The purpose of this study is to develop, through the application of MRI data, an automated model for the detection and classification of brain tumors based on deep learning (DLBTDC-MRI). Using the DLBTDC-MRI method, brain tumors can be detected and characterized at various stages of their progression. Preprocessing, segmentation, feature extraction, and classification are all included in the DLBTDC-MRI methodology that is supplied. The use of adaptive fuzzy filtering, often known as AFF, as a preprocessing technique for photos, results in less noise and higher-quality MRI scans. A method referred to as &ldquo;chicken swarm optimization&rdquo; (CSO) was used to segment MRI images. This method utilizes Tsallis entropy-based image segmentation to locate parts of the brain that have been injured. In addition to this, a Residual Network (ResNet) that combines handcrafted features with deep features was used to produce a meaningful collection of feature vectors. A classifier developed by combining DLBTDC-MRI and CSO can finally be used to diagnose brain tumors. To assess the enhanced performance of brain tumor categorization, a large number of simulations were run on the BRATS 2015 dataset. It would appear, based on the findings of these trials, that the DLBTDC-MRI method is superior to other contemporary procedures in many respects