Germline variation contributes to false negatives in CRISPR-based experiments with varying burden across ancestries.

Reducing disparities is vital for equitable access to precision treatments in cancer. Socioenvironmental factors are a major driver of disparities, but differences in genetic variation likely also contribute. The impact of genetic ancestry on prioritization of cancer targets in drug discovery pipelines has not been systematically explored due to the absence of pre-clinical data at the appropriate scale. Here, we analyze data from 611 genome-scale CRISPR/Cas9 viability experiments in human cell line models to identify ancestry-associated genetic dependencies essential for cell survival. Surprisingly, we find that most putative associations between ancestry and dependency arise from artifacts related to germline variants. Our analysis suggests that for 1.2-2.5% of guides, germline variants in sgRNA targeting sequences reduce cutting by the CRISPR/Cas9 nuclease, disproportionately affecting cell models derived from individuals of recent African descent. We propose three approaches to mitigate this experimental bias, enabling the scientific community to address these disparities.

The advent of RNA-based therapeutics for metabolic syndrome and associated conditions: a comprehensive review of the literature.

Metabolic syndrome (MetS) is a prevalent and intricate health condition affecting a significant global population, characterized by a cluster of metabolic and hormonal disorders disrupting lipid and glucose metabolism pathways. Clinical manifestations encompass obesity, dyslipidemia, insulin resistance, and hypertension, contributing to heightened risks of diabetes and cardiovascular diseases. Existing medications often fall short in addressing the syndrome's multifaceted nature, leading to suboptimal treatment outcomes and potential long-term health risks. This scenario underscores the pressing need for innovative therapeutic approaches in MetS management. RNA-based treatments, employing small interfering RNAs (siRNAs), microRNAs (miRNAs), and antisense oligonucleotides (ASOs), emerge as promising strategies to target underlying biological abnormalities. However, a summary of research available on the role of RNA-based therapeutics in MetS and related co-morbidities is limited. Murine models and human studies have been separately interrogated to determine whether there have been recent advancements in RNA-based therapeutics to offer a comprehensive understanding of treatment available for MetS. In a narrative fashion, we searched for relevant articles pertaining to MetS co-morbidities such as cardiovascular disease, fatty liver disease, dementia, colorectal cancer, and endocrine abnormalities. We emphasize the urgency of exploring novel therapeutic avenues to address the intricate pathophysiology of MetS and underscore the potential of RNA-based treatments, coupled with advanced delivery systems, as a transformative approach for achieving more comprehensive and efficacious outcomes in MetS patients.

Recent nanotheranostic approaches in cancer research.

Humanity is suffering from cancer which has become a root cause of untimely deaths of individuals around the globe in the recent past. Nanotheranostics integrates therapeutics and diagnostics to monitor treatment response and enhance drug efficacy and safety. We hereby propose to discuss all recent cancer imaging and diagnostic tools, the mechanism of targeting tumor cells, and current nanotheranostic platforms available for cancer. This review discusses various nanotheranostic agents and novel molecular imaging tools like MRI, CT, PET, SPEC, and PAT used for cancer diagnostics. Emphasis is given to gold nanoparticles, silica, liposomes, dendrimers, and metal-based agents. We also highlight the mechanism of targeting the tumor cells, and the limitations of different nanotheranostic agents in the field of research for cancer treatment. Due to the complexity in this area, multifunctional and hybrid nanoparticles functionalized with targeted moieties or anti-cancer drugs show the best feature for theranostics that enables them to work on carrying and delivering active materials to the desired area of the requirement for early detection and diagnosis. Non-invasive imaging techniques have a specificity of receptor binding and internalization processes of the nanosystems within the cancer cells. Nanotheranostics may provide the appropriate medicine at the appropriate dose to the appropriate patient at the appropriate time.

A novel and innovative cancer classification framework through a consecutive utilization of hybrid feature selection.

Cancer prediction in the early stage is a topic of major interest in medicine since it allows accurate and efficient actions for successful medical treatments of cancer. Mostly cancer datasets contain various gene expression levels as features with less samples, so firstly there is a need to eliminate similar features to permit faster convergence rate of classification algorithms. These features (genes) enable us to identify cancer disease, choose the best prescription to prevent cancer and discover deviations amid different techniques. To resolve this problem, we proposed a hybrid novel technique CSSMO-based gene selection for cancer classification. First, we made alteration of the fitness of spider monkey optimization (SMO) with cuckoo search algorithm (CSA) algorithm viz., CSSMO for feature selection, which helps to combine the benefit of both metaheuristic algorithms to discover a subset of genes which helps to predict a cancer disease in early stage. Further, to enhance the accuracy of the CSSMO algorithm, we choose a cleaning process, minimum redundancy maximum relevance (mRMR) to lessen the gene expression of cancer datasets. Next, these subsets of genes are classified using deep learning (DL) to identify different groups or classes related to a particular cancer disease. Eight different benchmark microarray gene expression datasets of cancer have been utilized to analyze the performance of the proposed approach with different evaluation matrix such as recall, precision, F1-score, and confusion matrix. The proposed gene selection method with DL achieves much better classification accuracy than other existing DL and machine learning classification models with all large gene expression dataset of cancer.

Silico-tuberculosis amidst COVID-19 pandemic: global scenario and Indian perspective.

Inhalation of crystalline silica-rich dust particles can result in the deadly occupational lung disorder called silicosis. The risk of contracting tuberculosis (TB) and the potential for lung cancer increase due to silicosis. This review article aims to bring to light the state of silicosis and TB scenario in the world and India for evaluating hurdles in the present and future to achieve the elimination road map and assess these conditions in the backdrop of the COVID-19 pandemic. A patient with silicosis has a 2.8-2.9 times higher risk of developing pulmonary TB and 3.7 times that of extrapulmonary TB. Incidences of missed cases when TB was misdiagnosed with silicosis due to indifferent clinical manifestations of the two in the initial stages are not uncommon. The duration of silica exposure and silicosis severity are directly related to the propensity to develop TB. As per a study, an average gap of 7.6 years has been noticed in a South African population for silico-tuberculosis to develop post-silicosis. In a study done on mine workers at Jodhpur, Rajasthan, it was seen that there is no definitive relation between patients with silicosis and the possibility of having COVID-19. There is a significant need to integrate the Silicosis control program with the TB elimination program for the government. A few steps can include assessing the workplaces, periodic monitoring of the workers' health, active case surveillance, identification of hotspots, and introducing reforms to curb the spread of dust and particulate matter from industrialised areas be taken in this regard.

Emerging zoonotic diseases and COVID-19 pandemic: global Perspective and Indian Scenario.

The current coronavirus disease 2019 (COVID-19) pandemic is one example of the scores of zoonotic diseases responsible for various outbreaks resulting in the deaths of millions of people for centuries. The COVID-19 pandemic has broken the age-old healthcare infrastructure and led to utter chaos. In the shadow of this pandemic, other zoonotic infections like the nipah virus, monkeypox, and langya virus, to name a few, have been neglected. Hence, outbreaks caused by such zoonotic viruses are rising in their endemic areas, like the Indian subcontinent. The mortality and morbidity due to such zoonoses are greater than usual due to the shortage of healthcare professionals caused by the COVID-19 crisis. Due to the lack of vaccines and therapeutics directed against this viral infection, treatment of patients is limited to supportive management and prevention, making preparedness for these potential zoonotic viral outbreaks essential. This paper highlights some of these zoonotic infections, which perpetuated and wreaked havoc while the world was occupied with containing the COVID-19 pandemic.

3D US-CT/MRI registration for percutaneous focal liver tumor ablations.

PURPOSE: US-guided percutaneous focal liver tumor ablations have been considered promising curative treatment techniques. To address cases with invisible or poorly visible tumors, registration of 3D US with CT or MRI is a critical step. By taking advantage of deep learning techniques to efficiently detect representative features in both modalities, we aim to develop a 3D US-CT/MRI registration approach for liver tumor ablations. METHODS: Facilitated by our nnUNet-based 3D US vessel segmentation approach, we propose a coarse-to-fine 3D US-CT/MRI image registration pipeline based on the liver vessel surface and centerlines. Then, phantom, healthy volunteer and patient studies are performed to demonstrate the effectiveness of our proposed registration approach. RESULTS: Our nnUNet-based vessel segmentation model achieved a Dice score of 0.69. In healthy volunteer study, 11 out of 12 3D US-MRI image pairs were successfully registered with an overall centerline distance of 4.03±2.68 mm. Two patient cases achieved target registration errors (TRE) of 4.16 mm and 5.22 mm. CONCLUSION: We proposed a coarse-to-fine 3D US-CT/MRI registration pipeline based on nnUNet vessel segmentation models. Experiments based on healthy volunteers and patient trials demonstrated the effectiveness of our registration workflow. Our code and example data are publicly available in this r epository.

A Review on Mechanistic Insight of Plant Derived Anticancer Bioactive Phytocompounds and Their Structure Activity Relationship.

Cancer is a disorder that rigorously affects the human population worldwide. There is a steady demand for new remedies to both treat and prevent this life-threatening sickness due to toxicities, drug resistance and therapeutic failures in current conventional therapies. Researchers around the world are drawing their attention towards compounds of natural origin. For decades, human beings have been using the flora of the world as a source of cancer chemotherapeutic agents. Currently, clinically approved anticancer compounds are vincristine, vinblastine, taxanes, and podophyllotoxin, all of which come from natural sources. With the triumph of these compounds that have been developed into staple drug products for most cancer therapies, new technologies are now appearing to search for novel biomolecules with anticancer activities. Ellipticine, camptothecin, combretastatin, curcumin, homoharringtonine and others are plant derived bioactive phytocompounds with potential anticancer properties. Researchers have improved the field further through the use of advanced analytical chemistry and computational tools of analysis. The investigation of new strategies for administration such as nanotechnology may enable the development of the phytocompounds as drug products. These technologies have enhanced the anticancer potential of plant-derived drugs with the aim of site-directed drug delivery, enhanced bioavailability, and reduced toxicity. This review discusses mechanistic insights into anticancer compounds of natural origins and their structural activity relationships that make them targets for anticancer treatments.

Benchmarking of SpCas9 variants enables deeper base editor screens of BRCA1 and BCL2.

Numerous rationally-designed and directed-evolution variants of SpCas9 have been reported to expand the utility of CRISPR technology. Here, we assess the activity and specificity of WT-Cas9 and 10 SpCas9 variants by benchmarking their PAM preferences, on-target activity, and off-target susceptibility in cell culture assays with thousands of guides targeting endogenous genes. To enhance the coverage and thus utility of base editing screens, we demonstrate that the SpCas9-NG and SpG variants are compatible with both A > G and C > T base editors, more than tripling the number of guides and assayable residues. We demonstrate the performance of these technologies by screening for loss-of-function mutations in BRCA1 and Venetoclax-resistant mutations in BCL2, identifying both known and new mutations that alter function. We anticipate that the tools and methodologies described here will facilitate the investigation of genetic variants at a finer and deeper resolution for any locus of interest.

Deep learning segmentation of general interventional tools in two-dimensional ultrasound images.

PURPOSE: Many interventional procedures require the precise placement of needles or therapy applicators (tools) to correctly achieve planned targets for optimal diagnosis or treatment of cancer, typically leveraging the temporal resolution of ultrasound (US) to provide real-time feedback. Identifying tools in two-dimensional (2D) images can often be time-consuming with the precise position difficult to distinguish. We have developed and implemented a deep learning method to segment tools in 2D US images in near real-time for multiple anatomical sites, despite the widely varying appearances across interventional applications. METHODS: A U-Net architecture with a Dice similarity coefficient (DSC) loss function was used to perform segmentation on input images resized to 256 × 256 pixels. The U-Net was modified by adding 50% dropouts and the use of transpose convolutions in the decoder section of the network. The proposed approach was trained with 917 images and manual segmentations from prostate/gynecologic brachytherapy, liver ablation, and kidney biopsy/ablation procedures, as well as phantom experiments. Real-time data augmentation was applied to improve generalizability and doubled the dataset for each epoch. Postprocessing to identify the tool tip and trajectory was performed using two different approaches, comparing the largest island with a linear fit to random sample consensus (RANSAC) fitting. RESULTS: Comparing predictions from 315 unseen test images to manual segmentations, the overall median [first quartile, third quartile] tip error, angular error, and DSC were 3.5 [1.3, 13.5] mm, 0.8 [0.3, 1.7]°, and 73.3 [56.2, 82.3]%, respectively, following RANSAC postprocessing. The predictions with the lowest median tip and angular errors were observed in the gynecologic images (median tip error: 0.3 mm; median angular error: 0.4°) with the highest errors in the kidney images (median tip error: 10.1 mm; median angular error: 2.9°). The performance on the kidney images was likely due to a reduction in acoustic signal associated with oblique insertions relative to the US probe and the increased number of anatomical interfaces with similar echogenicity. Unprocessed segmentations were performed with a mean time of approximately 50 ms per image. CONCLUSIONS: We have demonstrated that our proposed approach can accurately segment tools in 2D US images from multiple anatomical locations and a variety of clinical interventional procedures in near real-time, providing the potential to improve image guidance during a broad range of diagnostic and therapeutic cancer interventions.