Volume 02 · Issue 03 · 2022

Cervical cancer is a prominent disease in women, with a high mortality rate worldwide. This cancer continues to be a challenge to concisely diagnose, especially in its early stages. The aim of this study was to propose a unique cybernetic system which showcased the human-machine collaboration forming a superintelligence framework that ultimately allowed for greater clinical care strategies.

In this work, we applied machine learning (ML) models on 650 patients’ data collected from Hospital Universitario de Caracas in Caracas, Venezuela, where ethical approval and informed consent were granted. The data were hosted at the University of California at Irvine (UCI) database for cancer prediction by using data purely from a patient questionnaire that include key cervical cancer drivers such as questions on sexually transmitted diseases and time since first intercourse in order to design a clinical prediction machine that can predict various stages of cervical cancer. Two contrasting methods are explored in the design of a ML-driven prediction machine in this study, namely, a probabilistic method using Gaussian mixture models (GMM), and fuzziness-based reasoning using the fuzzy c-means (FCM) clustering on the data from 650 patients.

The models were validated using a K-Fold validation method, and the results show that both methods could be feasibly deployed in a clinical setting, with the probabilistic method (produced accuracies of 80+%/classifier dependent) allowing for more detail in the grading of a potential cervical cancer prediction, albeit at the cost of greater computation power; the FCM approach (produced accuracies around 90+%/classifier dependent) allows for a more parsimonious modelling with a slightly reduced prediction depth in comparison. As part of the novelty of this work, a clinical cybernetic system is also proposed to host the prediction machine, which allows for a human-machine collaborative interaction and an enhanced decision support platform to augment overall care strategies.

The present study showcased how the use of prediction machines can contribute towards early detection and prioritised care of patients with cervical cancer, while also allowing for cost-saving benefits when compared with routine cervical cancer screening. Further work in this area would now involve additional validation of the proposed clinical cybernetic loop and further improvement to the prediction machine by exploring non-linear dimensional embedding and clustering methods.

Colorectal cancer (CRC) is a type of malignant gastroenteric tumors associated with a high mortality rate worldwide. Calycosin, a natural phytoestrogen, possesses potent anti-cancer properties. We structurally modified calycosin to improve its physicochemical properties, and generated a novel small molecule termed CA028.

By using network pharmacology, followed by gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis and molecular docking, we aimed to predict and disclose the biological functions and mechanism of CA028 in the treatment of CRC through bioinformatic analyses.

By searching the online Swiss Target Prediction and TargetNet databases, we identified 150 genes shared by CA028 and CRC. Using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software, we identified 14 hub-functional genes, namely the FYN proto-oncogene, a Src family tyrosine kinase (FYN), mitogen-activated protein kinase 1 (MAPK1), MAPK8, MAPK14, Rac family small GTPase 1 (RAC1), epidermal growth factor receptor (EGFR), protein tyrosine kinase 2 (PTK2), sphingosine-1-phosphate receptor 1 (S1PR1), S1PR2, Janus kinase 1 (JAK1), JAK2, the RELA proto-oncogene NF-κB subunit (RELA), bradykinin receptor B1 (BDKRB1), and BDKRB2. Additionally, biological docking analysis using the Autodock Vina software revealed that FYN and MAPK1 were the main pharmacological proteins of CA028 against CRC. The gene ontology analysis using R-language packages further revealed the anti-CRC functions of CA028, including biological processes, cell components, and molecular pathways.

CA028 exhibits effective pharmacological activity against CRC by suppressing the proliferation of CRC cells and improving the tumor microenvironment. Importantly, certain predicted genes (e.g., FYN and MAPK1) may be the pharmacological targets of CA028 in the treatment of CRC.

The incidence of colorectal cancer is increasing worldwide, and it currently ranks third among all cancers. Moreover, pathological diagnosis is becoming increasingly arduous. Artificial intelligence has demonstrated the ability to fully excavate image features and assist doctors in making decisions. Large panoramic pathological sections contain considerable amounts of pathological information. In this study, we used large panoramic pathological sections to establish a deep learning model to assist pathologists in identifying cancerous areas on whole-slide images of rectal cancer, as well as for T staging and prognostic analysis.

We collected 126 cases of primary rectal cancer from the Affiliated Hospital of Qingdao University West Coast Hospital District (internal dataset) and 42 cases from Shinan and Laoshan Hospital District (external dataset) that had tissue surgically removed from January to September 2019. After sectioning, staining, and scanning, a total of 2350 hematoxylin-eosin-stained whole-slide images were obtained. The patients in the internal dataset were randomly divided into a training cohort (n =88 ) and a test cohort (n =38 ) at a ratio of 7:3. We chose DeepLabV3+ and ResNet50 as target models for our experiment. We used the Dice similarity coefficient, accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and area under the curve (AUC) to evaluate the performance of the artificial intelligence platform in the test set and validation set. Finally, we followed up patients and examined their prognosis and short-term survival to corroborate the value of T-staging investigations.

In the test set, the accuracy of image segmentation was 95.8%, the Dice coefficient was 0.92, the accuracy of automatic T-staging recognition was 86%, and the ROC AUC value was 0.93. In the validation set, the accuracy of image segmentation was 95.3%, the Dice coefficient was 0.90, the accuracy of automatic classification was 85%, the ROC AUC value was 0.92, and the image analysis time was 0.2 s. There was a difference in survival in patients with local recurrence or distant metastasis as the outcome at follow-up. Univariate analysis showed that T stage, N stage, preoperative carcinoembryonic antigen (CEA) level, and tumor location were risk factors for postoperative recurrence or metastasis in patients with rectal cancer. When these factors were included in a multivariate analysis, only preoperative CEA level and N stage showed significant differences.

The deep convolutional neural networks we have establish can assist clinicians in making decisions of T-stage judgment and improve diagnostic efficiency. Using large panoramic pathological sections enables better judgment of the condition of tumors and accurate pathological diagnoses, which has certain clinical application value.

Hand hygiene can be a simple, inexpensive, and effective method for preventing the spread of infectious diseases. However, a reliable and consistent method for monitoring adherence to the guidelines within and outside healthcare settings is challenging. The aim of this study was to provide an approach for monitoring handwashing compliance and quality in hospitals and communities.

We proposed a deep learning algorithm comprising three-dimensional convolutional neural networks (3D CNNs) and used 230 standard handwashing videos recorded by healthcare professionals in the hospital or at home for training and internal validation. An assessment scheme with a probability smoothing method was also proposed to optimize the neural network’s output to identify the handwashing steps, measure the exact duration, and grade the standard level of recognized steps. Twenty-two videos by healthcare professionals in another hospital and 28 videos recorded by civilians in the community were used for external validation.

Using a deep learning algorithm and an assessment scheme, combined with a probability smoothing method, each handwashing step was recognized (ACC ranged from 90.64% to 98.87% in the hospital and from 87.39% to 96.71% in the community). An assessment scheme measured each step’s exact duration, and the intraclass correlation coefficients were 0.98 (95% CI: 0.97-0.98) and 0.91 (95% CI: 0.88-0.93) for the total video duration in the hospital and community, respectively. Furthermore, the system assessed the quality of handwashing, similar to the expert panel (kappa = 0.79 in the hospital; kappa = 0.65 in the community).

This work developed an algorithm to directly assess handwashing compliance and quality from videos, which is promising for application in healthcare settings and communities to reduce pathogen transmission.