Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in Low-Cost Diagnostic Screening.

OBJECTIVE: Chronic kidney disease (CKD) is a major public health concern worldwide. High costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity and mortality rates in CKD patients, particularly in less developed countries. Thus, early diagnosis aided by vital parameter analytics using affordable computer-aided diagnosis could not only reduce diagnosis costs but improve patient management and outcomes. METHODS: In this study, we developed machine learning models using selective key pathological categories to identify clinical test attributes that will aid in accurate early diagnosis of CKD. Such an approach will save time and costs for diagnostic screening. We have also evaluated the performance of several classifiers with k-fold cross-validation on optimized datasets derived using these selected clinical test attributes. RESULTS: Our results suggest that the optimized datasets with important attributes perform well in diagnosis of CKD using our proposed machine learning models. Furthermore, we evaluated clinical test attributes based on urine and blood tests along with clinical parameters that have low costs of acquisition. The predictive models with the optimized and pathologically categorized attributes set yielded high levels of CKD diagnosis accuracy with random forest (RF) classifier being the best performing. CONCLUSIONS: Our machine learning approach has yielded effective predictive analytics for CKD screening which can be developed as a resource to facilitate improved CKD screening for enhanced and timely treatment plans

Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19

Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom–comorbidity combinations, it was observed that Pneumonia–Hypertension, Pneumonia–Diabetes, and Acute Respiratory Distress Syndrome (ARDS)–Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resource

Malignant melanoma and bone resorption

The cellular and humoral mechanisms accounting for osteolysis in skeletal metastases of malignant melanoma are uncertain. Osteoclasts, the specialised multinucleated cells that carry out bone resorption, are derived from monocyte/macrophage precursors. We isolated tumour-associated macrophages (TAMs) from metastatic (lymph node/skin) melanomas and cultured them in the presence and absence of osteoclastogenic cytokines and growth factors. The effect of tumour-derived fibroblasts and melanoma cells on osteoclast formation and resorption was also analysed. Melanoma TAMs (CD14+/CD51−) differentiated into osteoclasts (CD14−/CD51+) in the presence of receptor activator for nuclear factor κB ligand (RANKL) and macrophage-colony stimulating factor. Tumour-associated macrophage-osteoclast differentiation also occurred via a RANKL-independent pathway when TAMs were cultured with tumour necrosis factor-α and interleukin (IL)-1α. RT–PCR showed that fibroblasts isolated from metastatic melanomas expressed RANKL messenger RNA and the conditioned medium of cultured melanoma fibroblasts was found to be capable of inducing osteoclast formation in the absence of RANKL; this effect was inhibited by the addition of osteoprotegerin (OPG). We also found that cultured human SK-Mel-29 melanoma cells produce a soluble factor that induces osteoclast differentiation; this effect was not inhibited by OPG. Our findings indicate that TAMs in metastatic melanomas can differentiate into osteoclasts and that melanoma fibroblasts and melanoma tumour cells can induce osteoclast formation by RANKL-dependent and RANKL-independent mechanisms, respectively

Cellular mechanisms of bone resorption in breast carcinoma

The cellular mechanisms that account for the increase in osteoclast numbers and bone resorption in skeletal breast cancer metastasis are unclear. Osteoclasts are marrow-derived cells which form by fusion of mononuclear phagocyte precursors that circulate in the monocyte fraction. In this study we have determined whether circulating osteoclast precursors are increased in number or have an increased sensitivity to humoral factors for osteoclastogenesis in breast cancer patients with skeletal metastases (± hypercalcaemia) compared to patients with primary breast cancer and age-matched normal controls. Monocytes were isolated and cocultured with UMR 106 osteoblastic cells in the presence of 1,25 dihydroxyvitamin D3[1,25(OH)2D3] and human macrophage colony stimulating factor (M-CSF) on coverslips and dentine slices. Limiting dilution experiments showed that there was no increase in the number of circulating osteoclast precursors in breast cancer patients with skeletal metastases (± hypercalcaemia) compared to controls. Osteoclast precursors in these patients also did not exhibit increased sensitivity to 1,25(OH)2D3 or M-CSF in terms of osteoclast formation. The addition of parathyroid hormone-related protein and interleukin-6 did not increase osteoclast formation. The addition of the supernatant of cultured breast cancer cell lines (MCF-7 and MDA-MB-435), however, significantly increased monocyte-osteoclast formation in a dose-dependent fashion. These results indicate that the increase in osteoclast formation in breast cancer is not due to an increase in the number/nature of circulating osteoclast precursors. They also suggest that tumour cells promote osteoclast formation in the bone microenvironment by secreting soluble osteoclastogenic factor(s). © 2001 Cancer Research Campaign http://www.bjcancer.co

Jaw and Long Bone Marrows Have a Different Osteoclastogenic Potential

Osteoclasts, the multinucleated bone-resorbing cells, arise through fusion of precursors from the myeloid lineage. However, not all osteoclasts are alike; osteoclasts at different bone sites appear to differ in numerous respects. We investigated whether bone marrow cells obtained from jaw and long bone differed in their osteoclastogenic potential. Bone marrow cells from murine mandible and tibiae were isolated and cultured for 4 and 6 days on plastic or 6 and 10 days on dentin. Osteoclastogenesis was assessed by counting the number of TRAP+ multinucleated cells. Bone marrow cell composition was analyzed by FACS. The expression of osteoclast- and osteoclastogenesis-related genes was studied by qPCR. TRAP activity and resorptive activity of osteoclasts were measured by absorbance and morphometric analyses, respectively. At day 4 more osteoclasts were formed in long bone cultures than in jaw cultures. At day 6 the difference in number was no longer observed. The jaw cultures, however, contained more large osteoclasts on plastic and on dentin. Long bone marrow contained more osteoclast precursors, in particular the myeloid blasts, and qPCR revealed that the RANKL:OPG ratio was higher in long bone cultures. TRAP expression was higher for the long bone cultures on dentin. Although jaw osteoclasts were larger than long bone osteoclasts, no differences were found between their resorptive activities. In conclusion, bone marrow cells from different skeletal locations (jaw and long bone) have different dynamics of osteoclastogenesis. We propose that this is primarily due to differences in the cellular composition of the bone site-specific marrow

A RhoA-FRET Biosensor Mouse for Intravital Imaging in Normal Tissue Homeostasis and Disease Contexts.

The small GTPase RhoA is involved in a variety of fundamental processes in normal tissue. Spatiotemporal control of RhoA is thought to govern mechanosensing, growth, and motility of cells, while its deregulation is associated with disease development. Here, we describe the generation of a RhoA-fluorescence resonance energy transfer (FRET) biosensor mouse and its utility for monitoring real-time activity of RhoA in a variety of native tissues in vivo. We assess changes in RhoA activity during mechanosensing of osteocytes within the bone and during neutrophil migration. We also demonstrate spatiotemporal order of RhoA activity within crypt cells of the small intestine and during different stages of mammary gestation. Subsequently, we reveal co-option of RhoA activity in both invasive breast and pancreatic cancers, and we assess drug targeting in these disease settings, illustrating the potential for utilizing this mouse to study RhoA activity in vivo in real time

Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression.

Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment