Systematic detection of co-infection and intra-host recombination in more than 2 million global SARS-CoV-2 samples

Systematic monitoring of SARS-CoV-2 co-infections between different lineages and assessing the risk of intra-host recombinant emergence are crucial for forecasting viral evolution. Here we present a comprehensive analysis of more than 2 million SARS-CoV-2 raw read datasets submitted to the European COVID-19 Data Portal to identify co-infections and intra-host recombination. Co-infection was observed in 0.35% of the investigated cases. Two independent procedures were implemented to detect intra-host recombination. We show that sensitivity is predominantly determined by the density of lineage-defining mutations along the genome, thus we used an expanded list of mutually exclusive defining mutations of specific variant combinations to increase statistical power. We call attention to multiple challenges rendering recombinant detection difficult and provide guidelines for the reduction of false positives arising from chimeric sequences produced during PCR amplification. Additionally, we identify three recombination hotspots of Delta – Omicron BA.1 intra-host recombinants.</p

The COVID-19 Data Portal: accelerating SARS-CoV-2 and COVID-19 research through rapid open access data sharing.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic will be remembered as one of the defining events of the 21st century. The rapid global outbreak has had significant impacts on human society and is already responsible for millions of deaths. Understanding and tackling the impact of the virus has required a worldwide mobilisation and coordination of scientific research. The COVID-19 Data Portal (https://www.covid19dataportal.org/) was first released as part of the European COVID-19 Data Platform, on April 20th 2020 to facilitate rapid and open data sharing and analysis, to accelerate global SARS-CoV-2 and COVID-19 research. The COVID-19 Data Portal has fortnightly feature releases to continue to add new data types, search options, visualisations and improvements based on user feedback and research. The open datasets and intuitive suite of search, identification and download services, represent a truly FAIR (Findable, Accessible, Interoperable and Reusable) resource that enables researchers to easily identify and quickly obtain the key datasets needed for their COVID-19 research

Sorafenib from palliative to neoadjuvant chemotherapy in hepatocellular carcinoma with major vascular invasion: experience of two cases

Hepatocellular carcinoma (HCC) is the fifth most common malignancy worldwide and the incidence is higher in cirrhosis. Treatment options depend on tumor stage, status of liver function, and the general condition of the patient. Major vascular invasion is a contraindication for liver transplantation. Sorafenib has been found to be useful in association with transarterial chemoembolization as an effective chemotherapeutic agent to prolong survival in inoperable HCCs. Here we describe our experience where sorafenib was used as palliation but later turned out to be a neoadjuvant. Both cases had major portal vein thrombosis and received sorafenib as palliative therapy. After a mean use of 6 months, both patients had marked tumor response and proceeded to have liver transplantations. Both cases are tumor-free at a median follow up of 13 months

Does situs inversus totalis preclude liver donation in living donor liver transplantation? A series of 3 cases from single institution

Introduction: Liver transplantation (LT) is the gold standard for decompensated Chronic Liver Disease (CLD) in individuals satisfying the selection criteria. Organ scarcity is the rate limiting step in liver transplantation across the globe. Expanding the donor pool is practiced by transplant surgeons across the globe in view of perennial donor organ scarcity and ever increasing organ demand. Presentation of case: We have presented series of 3 cases of liver transplantation (LT) with modified left lobe (conventional right) graft from a situs inversus donor and implanting it as a conventional right lobe with a modified technique. The grafts had Type 1, Type 2 and Type 3 biliary anatomies. One graft had inferior hepatic veins also. All three patients had uneventful recoveries. The follow up period range is 4 years to 8 months. Discussion: There are multiple case reports in the literature involving situs inversus donors in liver transplantation. Various techniques have also been described. We describe simple and effective technique which has proved successful to our patients. Conclusion: SIT donors can be safely accepted for living donor liver transplantation. It is a technically challenging procedure both for donor liver harvesting and implantation in recipient. This is the first case series of LT using modified left lobe graft (conventional right) from a SIT donor with 2 different techniques. Biliary anastomosis is the tricky part of the operation

A Dicationic Bismuth(III) Lewis Acid: Catalytic Hydrosilylation of Olefins

Invited for the cover of this issue is Ajay Venugopal from the Indian Institute of Science Education and Research Thiruvananthapuram. The cover image shows dication Tp(Me2)Bi](2+) catalyzing olefin hydrosilylation under mild conditions

Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm

Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment. Convolutional neural networks (CNNs) have significantly improved the accuracy of tumor detection and classification in medical imaging compared to traditional methods. This study proposes a comprehensive classification technique for identifying breast cancer, utilizing a synthesized CNN, an enhanced optimization algorithm, and transfer learning. The primary goal is to assist radiologists in rapidly identifying anomalies. To overcome inherent limitations, we modified the Ant Colony Optimization (ACO) technique with opposition-based learning (OBL). The Enhanced Ant Colony Optimization (EACO) methodology was then employed to determine the optimal hyperparameter values for the CNN architecture. Our proposed framework combines the Residual Network-101 (ResNet101) CNN architecture with the EACO algorithm, resulting in a new model dubbed EACO–ResNet101. Experimental analysis was conducted on the MIAS and DDSM (CBIS-DDSM) mammographic datasets. Compared to conventional methods, our proposed model achieved an impressive accuracy of 98.63%, sensitivity of 98.76%, and specificity of 98.89% on the CBIS-DDSM dataset. On the MIAS dataset, the proposed model achieved a classification accuracy of 99.15%, a sensitivity of 97.86%, and a specificity of 98.88%. These results demonstrate the superiority of the proposed EACO–ResNet101 over current methodologies