Clinical features and management of individuals admitted to hospital with monkeypox and associated complications across the UK: a retrospective cohort study.

BACKGROUND: The scale of the 2022 global mpox (formerly known as monkeypox) outbreak has been unprecedented. In less than 6 months, non-endemic countries have reported more than 67 000 cases of a disease that had previously been rare outside of Africa. Mortality has been reported as rare but hospital admission has been relatively common. We aimed to describe the clinical and laboratory characteristics and outcomes of individuals admitted to hospital with mpox and associated complications, including tecovirimat recipients. METHODS: In this cohort study, we undertook retrospective review of electronic clinical records and pathology data for all individuals admitted between May 6, and Aug 3, 2022, to 16 hospitals from the Specialist and High Consequence Infectious Diseases Network for Monkeypox. The hospitals were located in ten cities in England and Northern Ireland. Inclusion criteria were clinical signs consistent with mpox and MPXV DNA detected from at least one clinical sample by PCR testing. Patients admitted solely for isolation purposes were excluded from the study. Key outcomes included admission indication, complications (including pain, secondary infection, and mortality) and use of antibiotic and anti-viral treatments. Routine biochemistry, haematology, microbiology, and virology data were also collected. Outcomes were assessed in all patients with available data. FINDINGS: 156 individuals were admitted to hospital with complicated mpox during the study period. 153 (98%) were male and three (2%) were female, with a median age of 35 years (IQR 30-44). Gender data were collected from electronic patient records, which encompassed full formal review of clincian notes. The prespecified options for data collection for gender were male, female, trans, non-binary, or unknown. 105 (71%) of 148 participants with available ethnicity data were of White ethnicity and 47 (30%) of 155 were living with HIV with a median CD4 count of 510 cells per mm3 (IQR 349-828). Rectal or perianal pain (including proctitis) was the most common indication for hospital admission (44 [28%] of 156). Severe pain was reported in 89 (57%) of 156, and secondary bacterial infection in 82 (58%) of 142 individuals with available data. Median admission duration was 5 days (IQR 2-9). Ten individuals required surgery and two cases of encephalitis were reported. 38 (24%) of the 156 individuals received tecovirimat with early cessation in four cases (two owing to hepatic transaminitis, one to rapid treatment response, and one to patient choice). No deaths occurred during the study period. INTERPRETATION: Although life-threatening mpox appears rare in hospitalised populations during the current outbreak, severe mpox and associated complications can occur in immunocompetent individuals. Analgesia and management of superimposed bacterial infection are priorities for patients admitted to hospital. FUNDING: None.

Role of chromosome 1q copy number variation in hepatocellular carcinoma.

Chromosome 1q often has been observed to be amplified in hepatocellular carcinoma. This review summarizes literature reports of multiple genes that have been proposed as possible 1q amplification drivers. These largely fall within 1q21-1q23. In addition, publicly available copy number alteration data from The Cancer Genome Atlas project were used to identify additional candidate genes involved in carcinogenesis. The most frequent location for gene amplification was 1q22, consistent with the results of the literature search. The genes TPM3 and NUF2 were found to be candidates whose amplification and/or mRNA up-regulation was most highly associated with poorer hepatocellular carcinoma outcomes.

Weakly-Supervised Self-Training for Breast Cancer Localization.

The use of deep learning methods has dramatically increased the state-of-the-art performance in image object localization. However, commonly used supervised learning methods require large training datasets with pixel-level or bounding box annotations. Obtaining such fine-grained annotations is extremely costly, especially in the medical imaging domain. In this work, we propose a novel weakly supervised method for breast cancer localization. The essential advantage of our approach is that the model only requires image-level labels and uses a self-training strategy to refine the predicted localization in a step-wise manner. We evaluated our approach on a large, clinically relevant mammogram dataset. The results show that our model significantly improves performance compared to other methods trained similarly.

Inconsistent Performance of Deep Learning Models on Mammogram Classification.

OBJECTIVES: Performance of recently developed deep learning models for image classification surpasses that of radiologists. However, there are questions about model performance consistency and generalization in unseen external data. The purpose of this study is to determine whether the high performance of deep learning on mammograms can be transferred to external data with a different data distribution. MATERIALS AND METHODS: Six deep learning models (three published models with high performance and three models designed by us) were evaluated on four different mammogram data sets, including three public (Digital Database for Screening Mammography, INbreast, and Mammographic Image Analysis Society) and one private data set (UKy). The models were trained and validated on either Digital Database for Screening Mammography alone or a combined data set that included Digital Database for Screening Mammography. The models were then tested on the three external data sets. The area under the receiver operating characteristic curve (auROC) was used to evaluate model performance. RESULTS: The three published models reported validation auROC scores between 0.88 and 0.95 on the validation data set. Our models achieved between 0.71 (95% confidence interval [CI]: 0.70-0.72) and 0.79 (95% CI: 0.78-0.80) auROC on the same validation data set. However, the same evaluation criteria of all six models on the three external test data sets were significantly decreased, only between 0.44 (95% CI: 0.43-0.45) and 0.65 (95% CI: 0.64-0.66). CONCLUSION: Our results demonstrate performance inconsistency across the data sets and models, indicating that the high performance of deep learning models on one data set cannot be readily transferred to unseen external data sets, and these models need further assessment and validation before being applied in clinical practice.

Classification of Whole Mammogram and Tomosynthesis Images Using Deep Convolutional Neural Networks.

Mammography is the most popular technology used for the early detection of breast cancer. Manual classification of mammogram images is a hard task because of the variability of the tumor. It yields a noteworthy number of patients being called back to perform biopsies, ensuring no missing diagnosis. The convolutional neural network (CNN) has succeeded in a lot of image classification challenges during the recent years. In this paper, we proposed an approach of mammogram and tomosynthesis classification based on CNNs. We had acquired more than 3000 mammograms and tomosynthesis data with approval from an institutional review board at the University of Kentucky. Different models of CNNs were built to classify both the 2-D mammograms and 3-D tomosynthesis, and every classifier was assessed with respect to truth-values generated by histology results from the biopsy and two-year negative mammogram follow-up confirmed by expert radiologists. Our outcomes demonstrated that CNN-based models we had built and optimized utilizing transfer learning and data augmentation have good potential for automatic breast cancer detection based on the mammograms and tomosynthesis data.

Polypharmacology Within the Full Kinome: a Machine Learning Approach.

Protein kinases generate nearly a thousand different protein products and regulate the majority of cellular pathways and signal transduction. It is therefore not surprising that the deregulation of kinases has been implicated in many disease states. In fact, kinase inhibitors are the largest class of new cancer therapies. Understanding polypharmacology within the full kinome, how drugs interact with many different kinases, would allow for the development of safer and more efficacious cancer therapies. A full understanding of these interactions is not experimentally feasible making highly accurate computational predictions extremely useful and important. This work aims at making a machine learning model useful for investigating the full kinome. We evaluate many feature sets for our model and get better performance over molecular docking with all of them. We demonstrate that you can achieve a nearly 60% increase in success rate at identifying binding compounds using our model over molecular docking scores.

Fiber angle and aspect ratio influence the shear mechanics of oriented electrospun nanofibrous scaffolds.

Fibrocartilages, including the knee meniscus and the annulus fibrosus (AF) of the intervertebral disc, play critical mechanical roles in load transmission across joints and their function is dependent upon well-defined structural hierarchies, organization, and composition. All, however, are compromised in the pathologic transformations associated with tissue degeneration. Tissue engineering strategies that address these key features, for example, aligned nanofibrous scaffolds seeded with mesenchymal stem cells (MSCs), represent a promising approach for the regeneration of these fibrous structures. While such engineered constructs can replicate native tissue structure and uniaxial tensile properties, the multidirectional loading encountered by these tissues in vivo necessitates that they function adequately in other loading modalities as well, including shear. As previous findings have shown that native tissue tensile and shear properties are dependent on fiber angle and sample aspect ratio, respectively, the objective of the present study was to evaluate the effects of a changing fiber angle and sample aspect ratio on the shear properties of aligned electrospun poly(ε-caprolactone) (PCL) scaffolds, and to determine how extracellular matrix deposition by resident MSCs modulates the measured shear response. Results show that fiber orientation and sample aspect ratio significantly influence the response of scaffolds in shear, and that measured shear strains can be predicted by finite element models. Furthermore, acellular PCL scaffolds possessed a relatively high shear modulus, 2-4 fold greater than native tissue, independent of fiber angle and aspect ratio. It was further noted that under testing conditions that engendered significant fiber stretch, the aggregate resistance to shear was higher, indicating a role for fiber stretch in the overall shear response. Finally, with time in culture, the shear modulus of MSC laden constructs increased, suggesting that deposited ECM contributes to the construct shear properties. Collectively, these findings show that aligned electrospun PCL scaffolds are a promising tool for engineering fibrocartilage tissues, and that the shear properties of both acellular and cell-seeded formulations can match or exceed native tissue benchmarks.

Electrical synapses control hippocampal contributions to fear learning and memory.

The role of electrical synapses in synchronizing neuronal assemblies in the adult mammalian brain is well documented. However, their role in learning and memory processes remains unclear. By combining Pavlovian fear conditioning, activity-dependent immediate early gene expression, and in vivo electrophysiology, we discovered that blocking neuronal gap junctions within the dorsal hippocampus impaired context-dependent fear learning, memory, and extinction. Theta rhythms in freely moving rats were also disrupted. Our results show that gap junction-mediated neuronal transmission is a prominent feature underlying emotional memories.