BACKGROUND: With the advent of massively parallel sequencing (MPS), DNA analysis can now be performed in a genomewide manner. Recent studies have demonstrated the high precision of MPS for quantifying fetal DNA in maternal plasma. In addition, paired-end sequencing can be used to determine the size of each sequenced DNA fragment. We applied MPS in a high-resolution investigation of the clearance profile of circulating fetal DNA.METHODS: Using paired-end MPS, we analyzed serial samples of maternal plasma collected from 13 women after cesarean delivery. We also studied the transrenal excretion of circulating fetal DNA in 3 of these individuals by analyzing serial urine samples collected after delivery.RESULTS: The clearance of circulating fetal DNA occurred in 2 phases, with different kinetics. The initial rapid phase had a mean half-life of approximately 1 h, whereas the subsequent slow phase had a mean half-life of approximately 13 h. The final disappearance of circulating fetal DNA occurred at about 1 to 2 days postpartum. Although transrenal excretion was involved in the clearance of circulating fetal DNA, it was not the major route. Furthermore, we observed significant changes in the size profiles of circulating maternal DNA after delivery, but we did not observe such changes in circulating fetal DNA.CONCLUSIONS: MPS of maternal plasma and urinary DNA permits high-resolution study of the clearance profile of circulating fetal DNA.

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’ anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research.

Photobiomodulation (PBM) using low-level laser therapy (LLLT) is a treatment that is increasingly used in oncology. Studies reported enhancement of wound healing with reduction in pain, tissue swelling and inflammatory conditions such as radiation dermatitis, oral mucositis, and lymphedema. However, factors such as wavelength, energy density and irradiation frequency influence the cellular mechanisms of LLLT. Moreover, the effects of LLLT vary according to cell types. Thus, controversy arose as a result of poor clinical response reported in some studies that may have used inadequately planned treatment protocols. Since LLLT may enhance tumor cell proliferation, these will also need to be considered before clinical use. This review aims to summarize the current knowledge of the cellular mechanisms of LLLT by considering its effects on cell proliferation, metabolism, angiogenesis, apoptosis and inflammation. With a better understanding of the cellular mechanisms, bridging findings from laboratory studies to clinical application can be improved.

Aortocaval compression (ACC) can result in haemodynamic disturbances and uteroplacental hypoperfusion in parturients. Its detection is difficult because in most patients, sympathetic compensation results in no signs or symptoms. However, profound hypotension may develop after sympathectomy during regional anaesthesia. In this prospective observational study, we aimed to detect ACC by analysing haemodynamic changes in term parturients who were positioned sequentially at different angles of lateral tilt. We studied haemodynamic changes in 157 non-labouring term parturients who were positioned in random order at 0°, 7.5°, 15°, and full left lateral tilt. Cardiac output (CO), stroke volume, and systemic vascular resistance were derived using suprasternal Doppler. Non-invasive arterial pressure (AP) measured in the upper and lower limbs was analysed to detect aortic compression. CO was on average 5% higher when patients were tilted at ≥15° compared with <15°. In a subgroup of patients (n=11), CO decreased by more than 20%, without changes in systolic AP, when they were tilted to <15° which was considered attributable to severe inferior vena caval compression. Only one patient in the supine position had aortic compression with the systolic AP in the upper limb 25 mm Hg higher than the lower limb. Patients with ACC can be identified by the CO changes from serial measurements between supine, 15°, or full lateral tilt. Our findings suggest that in non-labouring parturients, ACC is asymptomatic and can be effectively minimized by the use of a left lateral tilt of 15° or greater.

Spontaneous preterm birth (SPB, before 37 gestational weeks) is a major cause of perinatal mortality and morbidity, but its pathogenesis remains unclear. Studies on SPB have been hampered by the limited availability of markers for SPB in predelivery clinical samples that can be easily compared with gestational age-matched normal controls. We hypothesize that SPB involves aberrant placental RNA expression, and that such RNA transcripts can be detected in predelivery maternal plasma samples, which can be compared with gestational age-matched controls. Using gene expression microarray to profile essentially all human genes, we observed that 426 probe signals were changed by >2.9-fold in the SPB placentas, compared with the spontaneous term birth (STB) placentas. Among the genes represented by those probes, we observed an over-representation of functions in RNA stabilization, extracellular matrix binding, and acute inflammatory response. Using RT-quantitative PCR, we observed differences in the RNA concentrations of certain genes only between the SPB and STB placentas, but not between the STB and term elective cesarean delivery placentas. Notably, 36 RNA transcripts were observed at placental microarray signals higher than a threshold, which indicated the possibility of their detection in maternal plasma. Among them, the IL1RL1 mRNA was tested in plasma samples taken from 37 women. It was detected in 6 of 10 (60%) plasma samples collected during the presentation of preterm labor (≤32.9 weeks) in women eventually giving SPB, but was detected in only 1 of 27 (4%) samples collected during matched gestational weeks from women with no preterm labor (Fisher exact test, p = 0.00056). We have identified 36 SPB-associated RNA transcripts, which are possibly detectable in maternal plasma. We have illustrated that the IL1RL1 mRNA was more frequently detected in predelivery maternal plasma samples collected from women resulting in SPB than the gestational-age matched controls.

Radiomics, which integrates the comprehensive characterization of imaging phenotypes with machine learning algorithms, is increasingly recognized for its potential in the diagnosis and prognosis of oncological conditions. However, the repeatability and reproducibility of radiomic features are critical challenges that hinder their widespread clinical adoption. This review aims to address the paucity of discussion regarding the factors that influence the reproducibility and repeatability of radiomic features and their subsequent impact on the application of radiomic models. We provide a synthesis of the literature on the repeatability and reproducibility of CT/MR-based radiomic features, examining sources of variation, the number of reproducible features, and the availability of individual feature repeatability indices. We differentiate sources of variation into random effects, which are challenging to control but can be quantified through simulation methods such as perturbation, and biases, which arise from scanner variability and inter-reader differences and can significantly affect the generalizability of radiomic model performance in diverse settings. Four suggestions for repeatability and reproducibility studies are suggested: (1) detailed reporting of variation sources, (2) transparent disclosure of calculation parameters, (3) careful selection of suitable reliability indices, and (4) comprehensive reporting of reliability metrics. This review underscores the importance of random effects in feature selection and harmonizing biases between development and clinical application settings to facilitate the successful translation of radiomic models from research to clinical practice.