In International immunopharmacology

PURPOSE : Identifying of host-directed targets and molecular markers of immune response for tuberculosis (TB) immunotherapy is urgent and meaningful. Previous studies have demonstrated an important role of autophagy in the course and pathophysiology of TB and is associated with the efficacy of TB treatment. However, its role in TB immunotherapy is still incomplete.

METHODS : The effect of autophagy on intracellular bacteria load was examined in sulforaphane (SFN)-treated THP-1 cells. The immune infiltration was assessed based on public databases. Functional enrichment analysis revealed the pathways involved. LASSO Cox regression analysis was employed to identify hub genes. Moreover, machine learning analysis was used to obtain important targets of TB immunotherapy. Finally, the relationship between hub genes and immune infiltration was assessed, as well as the relevance of chemokines.

RESULTS : We found that SFN reduced intracellular bacteria load by enhancing autophagy in THP-1 cells. Thirty-two autophagy-related genes (ARGs) were identified, three types of immune cells (macrophages, neutrophils, and DC cells) were significantly enriched in TB patients, and 6 hub genes (RAB5A, SQSTM1, MYC, MAPK8, MAPK3, and FOXO1) were closely related to TB immune infiltration. The 32 ARGs were mainly involved in autophagy, apoptosis, and tuberculosis pathways. FOXO1, SQSTM1, and RAB5A were identified as important target genes according to the ranking of variable importance, with FOXO1 being a potential autophagy-related target of TB immunotherapy.

CONCLUSION : This study highlights the association between autophagy-related genes and immune infiltration in TB. Three key genes, especially FOXO1, regulated by SFN, will provide new insights into diagnostic and immunotherapy strategies for clinical tuberculosis.

Xiao Sifang, Zhou Ting, Pan Jianhua, Ma Xiaohua, Shi Guomin, Jiang Binyuan, Xiang Yan-Gen

2023-Mar-15

Autophagy, Immune cell infiltration, Macrophage, Sulforaphane, Tuberculosis

In Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

Assaad Serge, Dov David, Davis Richard, Kovalsky Shahar, Lee Walter T, Kahmke Russel, Rocke Daniel, Cohen Jonathan, Henao Ricardo, Carin Lawrence, Range Danielle Elliott

2023-Feb-13

artificial intelligence, cytopathology, fine needle aspiration, machine learning, mobile imaging, thyroid

In Frontiers in health services

INTRODUCTION : Over 50,000 very low birth weight (VLBW) infants are born each year in the United States. Despite advances in care, these premature babies are subjected to long stays in a neonatal intensive care unit (NICU), and experience high rates of morbidity and mortality. In a large randomized controlled trial (RCT), heart rate characteristics (HRC) monitoring in addition to standard monitoring decreased all-cause mortality among VLBW infants by 22%. We sought to understand the cost-effectiveness of HRC monitoring to improve survival among VLBW infants.

METHODS : We performed a secondary analysis of cost-effectiveness of heart rate characteristics (HRC) monitoring to improve survival from birth to NICU discharge, up to 120 days using data and outcomes from an RCT of 3,003 VLBW patients. We estimated each patient's cost from a third-party perspective in 2021 USD using the resource utilization data gathered during the RCT (NCT00307333) during their initial stay in the NICU and applied to specific per diem rates. We computed the incremental cost-effectiveness ratio and used non-parametric boot-strapping to evaluate uncertainty.

RESULTS : The incremental cost-effectiveness ratio of HRC-monitoring was $34,720 per life saved. The 95th percentile of cost to save one additional life through HRC-monitoring was $449,291.

CONCLUSION : HRC-monitoring appears cost-effective for increasing survival among VLBW infants.

King William E, Carlo Waldemar A, O’Shea T Michael, Schelonka Robert L

2022

artificial intelligence (AI), cost-effectiveness analysis (CEA), heart rate characteristics (HRC), incremental cost-effectiveness ratio (ICER), neonatal intensive care unit (NICU), newborn infant, randomized controlled trial (RCT), very low birth weight (VLBW)

In Computer methods and programs in biomedicine

PURPOSE : Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) is important but challenging for the simulation and measurement of cerebrovascular diseases. Recently, deep learning has promoted the rapid development of cerebrovascular segmentation. However, model optimization relies on voxel or regional punishment and lacks global awareness and interpretation from the texture and edge. To overcome the limitations of the existing methods, we propose a new cerebrovascular segmentation method to obtain more refined structures.

METHODS : In this paper, we propose a new adversarial model that achieves segmentation using segmentation model and filters the results using discriminator. Considering the sample imbalance in cerebrovascular imaging, we separated the TOF-MRA images and utilized high- and low-frequency images to enhance the texture and edge representation. The encoder weight sharing from the segmentation model not only saves the model parameters, but also strengthens the integration and separation correlation. Diversified discrimination enhances the robustness and regularization of the model.

RESULTS : The adversarial model was tested using two cerebrovascular datasets. It scored 82.26% and 73.38%, respectively, ranking first on both datasets. The results show that our method not only outperforms the recent cerebrovascular segmentation model, but also surpasses the common adversarial models.

CONCLUSION : Our adversarial model focuses on improving the extraction ability of the model on texture and edge, thereby achieving awareness of the global cerebrovascular topology. Therefore, we obtained an accurate and robust cerebrovascular segmentation. This framework has potential applications in many imaging fields, particularly in the application of sample imbalance. Our code is available at the website https://github.com/MontaEllis/ISA-model.

Chen Cheng, Zhou Kangneng, Lu Tong, Ning Huansheng, Xiao Ruoxiu

2023-Mar-11

Adversarial model, Cerebrovascular segmentation, Deep learning, TOF-MRA

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : With the rapid development of information dissemination technology, the amount of events information contained in massive texts now far exceeds the intuitive cognition of humans, and it is hard to understand the progress of events in order of time. Temporal information runs through the whole process of beginning, proceeding, and ending of events, and plays an important role in many natural language processing applications, such as information extraction, question answering, and text summary. Accurately extracting temporal information from Chinese texts and automatically mapping the temporal expressions in natural language to the time axis are crucial to understanding the development of events and dynamic changes in them.

METHODS : This study proposes a method integrating machine learning with linguistic features (IMLLF) for extraction and normalization of temporal expressions in Chinese texts to achieve the above objectives. Linguistic features are constructed by analyzing the expression rules of temporal information, and are combined with machine learning to map the natural language form of time onto a one-dimensional timeline. The web text dataset we build is divided into five parts for five-fold cross-validation, to compare the influence of different combinations of linguistic features and different methods. In the open medical dialog dataset, based on the training model obtained from the web text dataset, 200 disease descriptions are randomly selected each time for three rounds of experiments.

RESULTS : The F1 of multi-feature fusion is 95.2%, which is better than the single-feature and double-feature combination. The results of experiments showed that the proposed IMLLF method can improve the accuracy of recognition of temporal information in Chinese to a greater extent than classical methods, with an F1-score of over 95% on the web text dataset and medical conversation dataset. In terms of the normalization of time expressions, the accuracy of the IMLLF method is higher than 93%.

CONCLUSIONS : IMLLF has better results in extracting and normalizing time expressions on the web text dataset and the medical conversation dataset, which verifies the universality of IMLLF to identify and quantify temporal information. IMLLF method can accurately map the time information to the time axis, which is convenient for doctors to intuitively see when and what happened to the patient, and helps to make better medical decisions.

Wang Shunli, Li Rui, Wu Huayi

2023-Mar-11

Extraction and normalization of temporal expressions, Linguistic features, Online medical conversation, Temporal reasoning

In Journal of clinical oncology : official journal of the American Society of Clinical Oncology

PURPOSE : Image-derived artificial intelligence-based short-term risk models for breast cancer have shown high discriminatory performance compared with traditional lifestyle/familial-based risk models. The long-term performance of image-derived risk models has not been investigated.

METHODS : We performed a case-cohort study of 8,604 randomly selected women within a mammography screening cohort initiated in 2010 in Sweden for women age 40-74 years. Mammograms, age, lifestyle, and familial risk factors were collected at study entry. In all, 2,028 incident breast cancers were identified through register matching in May 2022 (206 incident breast cancers were found in the subcohort). The image-based model extracted mammographic features (density, microcalcifications, masses, and left-right breast asymmetries of these features) and age from study entry mammograms. The Tyrer-Cuzick v8 risk model incorporates self-reported lifestyle and familial risk factors and mammographic density to estimate risk. Absolute risks were estimated, and age-adjusted AUC model performances (aAUCs) were compared across the 10-year period.

RESULTS : The aAUCs of the image-based risk model ranged from 0.74 (95% CI, 0.70 to 0.78) to 0.65 (95% CI, 0.63 to 0.66) for breast cancers developed 1-10 years after study entry; the corresponding Tyrer-Cuzick aAUCs were 0.62 (95% CI, 0.56 to 0.67) to 0.60 (95% CI, 0.58 to 0.61). For symptomatic cancers, the aAUCs for the image-based model were ≥0.75 during the first 3 years. Women with high and low mammographic density showed similar aAUCs. Throughout the 10-year follow-up, 20% of all women with breast cancers were deemed high-risk at study entry by the image-based risk model compared with 7.1% using the lifestyle familial-based model (P < .01).

CONCLUSION : The image-based risk model outperformed the Tyrer-Cuzick v8 model for both short-term and long-term risk assessment and could be used to identify women who may benefit from supplemental screening and risk reduction strategies.

Eriksson Mikael, Czene Kamila, Vachon Celine, Conant Emily F, Hall Per

2023-Mar-17