SECRETS: Subject-efficient clinical randomized controlled trials using synthetic intervention.

Background: The parallel-group randomized controlled trial (RCT) is commonly used in Phase-3 clinical trials to establish treatment effectiveness but requires hundreds-to-thousands of subjects, making it difficult to implement, which leads to high Phase-3 trial failure rates. One approach to increasing power of a trial is to augment data collected from an RCT with external data from prospective studies or prior RCTs. However, this requires that external data be comparable to data from the study of interest, a condition that does not hold for new interventions or populations being studied. Another approach is to lower sample size requirements by using the cross-over design, which measures individual treatment effects (ITEs) to remove inter-subject variability; however, this design is only suitable for chronic conditions and interventions with effects that wash out rapidly. Method: We propose a novel and practical framework called SECRETS (Subject-Efficient Clinical Randomized Controlled Trials using Synthetic Intervention) to increase power of any parallel-group RCT by simulating the cross-over design using only data collected from the study. SECRETS first estimates ITEs across all subjects recruited to the RCT by using a state-of-the-art counterfactual estimation algorithm called synthetic intervention (SI). Since SI induces dependencies among the ITEs, we introduce a novel hypothesis testing strategy to test for treatment effectiveness. Results: We show that SECRETS can increase the power of an RCT while maintaining comparable significance levels; in particular, on three real-world clinical RCTs (Phase-3 trials), SECRETS increases power over the baseline method by 6 - 54% (average: 21.5%, standard deviation: 15.8%), thereby reducing the number of subjects needed to obtain a typically desired statistical operating point of 80% power and 5% significance level by 25 - 76% (10-3,957 fewer subjects per arm). Our analyses show that SECRETS increases power by consistently reducing the variance of the average treatment effect, thereby mimicking the effects of a cross-over design. Conclusion: SECRETS increases subject efficiency of an RCT by simulating the cross-over design using only data collected from the RCT; therefore, it is a feasible solution for increasing the trial's power, especially under settings where satisfying sample size requirements is difficult.

Nanomedicines for the management of diabetic nephropathy: present progress and prospects.

Diabetic nephropathy (DN) is a serious microvascular consequence of diabetes mellitus (DM), posing an encumbrance to public health worldwide. Control over the onset and progress of DN depend heavily on early detection and effective treatment. DN is a major contributor to end-stage renal disease, and a complete cure is yet to be achieved with currently available options. Though some therapeutic molecules have exhibited promise in treating DN complications, their poor solubility profile, low bioavailability, poor permeation, high therapeutic dose and associated toxicity, and low patient compliance apprehend their clinical usefulness. Recent research has indicated nano-systems as potential theranostic platforms displaying futuristic promise in the diagnosis and treatment of DN. Early and accurate diagnosis, site-specific delivery and retention by virtue of ligand conjugation, and improved pharmacokinetic profile are amongst the major advantages of nano-platforms, defining their superiority. Thus, the emergence of nanoparticles has offered fresh approaches to the possible diagnostic and therapeutic strategies regarding DN. The present review corroborates an updated overview of different types of nanocarriers regarding potential approaches for the diagnosis and therapy of DN.

Genome editing technologies, mechanisms and improved production of therapeutic phytochemicals: Opportunities and prospects.

Plants produce a large number of secondary metabolites, known as phytometabolites that may be employed as medicines, dyes, poisons, and insecticides in the field of medicine, agriculture, and industrial use, respectively. The rise of genome management approaches has promised a factual revolution in genetic engineering. Targeted genome editing in living entities permits the understanding of the biological systems very clearly, and also sanctions to address a wide-ranging objective in the direction of improving features of plant and their yields. The last few years have introduced a number of unique genome editing systems, including transcription activator-like effector nucleases, zinc finger nucleases, and miRNA-regulated clustered regularly interspaced short palindromic repeats/Cas9 (CRISPR/Cas9). Genome editing systems have helped in the transformation of metabolic engineering, allowing researchers to modify biosynthetic pathways of different secondary metabolites. Given the growing relevance of editing genomes in plant research, the exciting novel methods are briefly reviewed in this chapter. Also, this chapter highlights recent discoveries on the CRISPR-based modification of natural products in different medicinal plants.

DINI: data imputation using neural inversion for edge applications.

The edge computing paradigm has recently drawn significant attention from industry and academia. Due to the advantages in quality-of-service metrics, namely, latency, bandwidth, energy efficiency, privacy, and security, deploying artificial intelligence (AI) models at the network edge has attracted widespread interest. Edge-AI has seen applications in diverse domains that involve large amounts of data. However, poor dataset quality plagues this compute regime owing to numerous data corruption sources, including missing data. As such systems are increasingly being deployed in mission-critical applications, mitigating the effects of corrupted data becomes important. In this work, we propose a strategy based on data imputation using neural inversion, DINI. It trains a surrogate model and runs data imputation in an interleaved fashion. Unlike previous works, DINI is a model-agnostic framework applicable to diverse deep learning architectures. DINI outperforms state-of-the-art methods by at least 10.7% in average imputation error. Applying DINI to mission-critical applications can increase prediction accuracy to up to 99% (F1 score of 0.99), resulting in significant gains compared to baseline methods.

Exploring the role of antibiotics and steroids in managing respiratory diseases.

Respiratory diseases (RDs), such as chronic obstructive pulmonary disease, cystic fibrosis, asthma, and pneumonia, are associated with significant morbidity and mortality. Treatment usually consists of antibiotics and steroids. Relevant published literature reviews, studies, and clinical trials were accessed from institutional and electronic databases. The keywords used were respiratory diseases, steroids, antibiotics, and combination of steroids and antibiotics. Selected articles and literature were carefully reviewed. Antibiotics are often prescribed as the standard therapy to manage RDs. Types of causative respiratory pathogens, spectrum of antibiotics activity, route of administration, and course of therapy determine the type of antibiotics that are prescribed. Despite being associated with good clinical outcome, treatment failure and recurrence rate are still high. In addition, antibiotic resistance has been widely reported due to bacterial mutations in response to the use of antibiotics, which render them ineffective. Nevertheless, there has been a growing demand for corticosteroids (CS) and antibiotics to treat a wide variety of diseases, including various airway diseases, due to their immunosuppressive and anti-inflammatory properties. The use of CS is well established and there are different formulations based on the diseases, such as topical administration, tablets, intravenous injections, and inhaled preparations. Both antibiotics and CS possess similar properties in terms of their anti-inflammatory effects, especially regulating cytokine release. Thus, the current review examines and discusses the different applications of antibiotics, CS, and their combination in managing various RDs. Drawbacks of these interventions are also discussed.

An overview of vaccine development for COVID-19.

The COVID-19 pandemic continues to endanger world health and the economy. The causative SARS-CoV-2 coronavirus has a unique replication system. The end point of the COVID-19 pandemic is either herd immunity or widespread availability of an effective vaccine. Multiple candidate vaccines - peptide, virus-like particle, viral vectors (replicating and nonreplicating), nucleic acids (DNA or RNA), live attenuated virus, recombinant designed proteins and inactivated virus - are presently under various stages of expansion, and a small number of vaccine candidates have progressed into clinical phases. At the time of writing, three major pharmaceutical companies, namely Pfizer and Moderna, have their vaccines under mass production and administered to the public. This review aims to investigate the most critical vaccines developed for COVID-19 to date.

Anti-Cancerous Effect of Rutin Against HPV-C33A Cervical Cancer Cells via G0/G1 Cell Cycle Arrest and Apoptotic Induction.

BACKGROUND: Nowadays, the potential therapeutic role of various bioflavonoids including Curcumin, Luteolin and Resveratrol has currently been well-documented in a vast range of fatal complications including synaptic failure and cancers. These bioflavonoids are widely being implemented for the treatment of various cancers as they possess anti-cancerous, anti-oxidant and anti-inflammatory properties. Moreover, they are also used as a better alternative to conventional therapies since; these are non-toxic to cells and having no or least side effects. Notably, the pertinent therapeutic role of Rutin in cervical cancer is still unsettled however, its anti-cancerous role has already been reported in other cancers including prostate and colon cancer. Rutin (Vitamin P or Rutoside) is a polyphenolics flavonoid exhibiting multi-beneficial roles against several carcinomas. OBJECTIVE: Despite the evidence for its several biological activities, the anticancer effects of Rutin on human cervical cancer (C33A) cells remain to be explored. In this study, the anticancer potential of Rutin was investigated by employing the key biomarkers such as nuclear condensation reactive oxygen species (ROS), apoptosis, and changes in mitochondrial membrane potential (MMP). RESULTS: Our findings showed that Rutin treatment reduced the cell viability, induced significant increase in ROS production and nuclear condensation in dose-dependent manner. Moreover, Rutin provoked apoptosis by inducing decrease in MMP and activation of caspase-3. Cell cycle analysis further confirmed the efficacy of Rutin by showing cell cycle arrest at G0/G1 phase. CONCLUSION: Thus, our study is envisaged to open up interests for elucidating Rutin as an anticancerous agent against cervical cancer.

Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare.

Machine learning is being used in a wide range of application domains to discover patterns in large datasets. Increasingly, the results of machine learning drive critical decisions in applications related to healthcare and biomedicine. Such health-related applications are often sensitive, and thus, any security breach would be catastrophic. Naturally, the integrity of the results computed by machine learning is of great importance. Recent research has shown that some machine-learning algorithms can be compromised by augmenting their training datasets with malicious data, leading to a new class of attacks called poisoning attacks. Hindrance of a diagnosis may have life-threatening consequences and could cause distrust. On the other hand, not only may a false diagnosis prompt users to distrust the machine-learning algorithm and even abandon the entire system but also such a false positive classification may cause patient distress. In this paper, we present a systematic, algorithm-independent approach for mounting poisoning attacks across a wide range of machine-learning algorithms and healthcare datasets. The proposed attack procedure generates input data, which, when added to the training set, can either cause the results of machine learning to have targeted errors (e.g., increase the likelihood of classification into a specific class), or simply introduce arbitrary errors (incorrect classification). These attacks may be applied to both fixed and evolving datasets. They can be applied even when only statistics of the training dataset are available or, in some cases, even without access to the training dataset, although at a lower efficacy. We establish the effectiveness of the proposed attacks using a suite of six machine-learning algorithms and five healthcare datasets. Finally, we present countermeasures against the proposed generic attacks that are based on tracking and detecting deviations in various accuracy metrics, and benchmark their effectiveness.

MedMon: securing medical devices through wireless monitoring and anomaly detection.

Rapid advances in personal healthcare systems based on implantable and wearable medical devices promise to greatly improve the quality of diagnosis and treatment for a range of medical conditions. However, the increasing programmability and wireless connectivity of medical devices also open up opportunities for malicious attackers. Unfortunately, implantable/wearable medical devices come with extreme size and power constraints, and unique usage models, making it infeasible to simply borrow conventional security solutions such as cryptography. We propose a general framework for securing medical devices based on wireless channel monitoring and anomaly detection. Our proposal is based on a medical security monitor (MedMon) that snoops on all the radio-frequency wireless communications to/from medical devices and uses multi-layered anomaly detection to identify potentially malicious transactions. Upon detection of a malicious transaction, MedMon takes appropriate response actions, which could range from passive (notifying the user) to active (jamming the packets so that they do not reach the medical device). A key benefit of MedMon is that it is applicable to existing medical devices that are in use by patients, with no hardware or software modifications to them. Consequently, it also leads to zero power overheads on these devices. We demonstrate the feasibility of our proposal by developing a prototype implementation for an insulin delivery system using off-the-shelf components (USRP software-defined radio). We evaluate its effectiveness under several attack scenarios. Our results show that MedMon can detect virtually all naive attacks and a large fraction of more sophisticated attacks, suggesting that it is an effective approach to enhancing the security of medical devices.