Maternal smoking leads to glucose and lipid metabolic disorders and hepatic damage in the offspring, potentially due to mitochondrial oxidative stress. Mitoquinone mesylate (MitoQ) is a mitochondrial targeted antioxidant with high bioavailability. This study aimed to examine the impact of maternal cigarette smoke exposure (SE) on offspring’s metabolic profile and hepatic damage, and whether maternal MitoQ supplementation during gestation can affect these changes. Female Balb/c mice (eight weeks) were either exposed to air or SE for six weeks prior to mating and throughout gestation and lactation. A subset of the SE dams were supplied with MitoQ in the drinking water (500 µmol/L) during gestation and lactation. Intraperitoneal glucose tolerance test was performed in the male offspring at 12 weeks and the livers and plasma were collected at 13 weeks. Maternal SE induced glucose intolerance, hepatic steatosis, mitochondrial oxidative stress and related damage in the adult offspring. Maternal MitoQ supplementation reduced hepatic mitochondrial oxidative stress and improved markers of mitophagy and mitochondrial biogenesis. This may restore hepatic mitochondrial health and was associated with an amelioration of glucose intolerance, hepatic steatosis and pathological changes induced by maternal SE. MitoQ supplementation may potentially prevent metabolic dysfunction and hepatic pathology induced by intrauterine SE.

Radiodynamic therapy (RDT) is a recent extension of conventional photodynamic therapy, in which visible/near infrared light irradiation is replaced by a well-tolerated dose of high-energy X-rays. This enables greater tissue penetration to allow non-invasive treatment of large, deep-seated tumors. We report here the design and testing of a drug delivery system for RDT that is intended to enhance intra- or peri-nuclear localization of the photosensitizer, leading to DNA damage and resulting clonogenic cell kill. This comprises a photosensitizer (Verteporfin, VP) incorporated into poly (lactic-co-glycolic acid) nanoparticles (PLGA NPs) that are surface-functionalized with a cell-penetrating HIV trans-activator of transcription (TAT) peptide. In addition to a series of physical and photophysical characterization studies, cytotoxicity tests in pancreatic (PANC-1) cancer cells in vitro under 4 Gy X-ray exposure from a clinical 6 MV linear accelerator (LINAC) showed that TAT targeting of the nanoparticles markedly enhances the effectiveness of RDT treatment, particularly when assessed by a clonogenic, i.e., DNA damage-mediated, cell kill.

Relatively little is known about the transgenerational effects of chronic maternal exposure to low-level traffic-related air pollution (TRAP) on the offspring lung health, nor are the effects of removing such exposure before pregnancy. Female BALB/c mice were exposed to PM_2.5 (PM_2.5, 5 µg/day) for 6 weeks before mating and during gestation and lactation; in a subgroup, PM was removed when mating started to model mothers moving to cleaner areas during pregnancy to protect their unborn child (Pre-exposure). Lung pathology was characterised in both dams and offspring. A subcohort of female offspring was also exposed to ovalbumin to model allergic airways disease. PM_2.5 and Pre-exposure dams exhibited airways hyper-responsiveness (AHR) with mucus hypersecretion, increased mitochondrial reactive oxygen species (ROS) and mitochondrial dysfunction in the lungs. Female offspring from PM_2.5 and Pre-exposure dams displayed AHR with increased lung inflammation and mitochondrial ROS production, while males only displayed increased lung inflammation. After the ovalbumin challenge, AHR was increased in female offspring from PM_2.5 dams compared with those from control dams. Using an in vitro model, the mitochondria-targeted antioxidant MitoQ reversed mitochondrial dysfunction by PM stimulation, suggesting that the lung pathology in offspring is driven by dysfunctional mitochondria. In conclusion, chronic exposure to low doses of PM_2.5 exerted transgenerational impairment on lung health.

Clinical OSSN diagnostics by non-invasive spectral imaging of eye autofluorescence must be rapid enough to be comfortable for patients - without compromising accuracy. This requires identifying optimized spectral signatures of OSSN based on a minimal number of spectrally defined images. Here, we identified such signatures using a data-driven methodology of swarm intelligence. Ten patients with histopathological diagnosis of ocular surface squamous neoplasia (OSSN) were recruited. Their unstained biopsy OSSN specimens were investigated using a custom-built autofluorescence multispectral microscopy imaging system. The images were taken in 38 spectral channels spanning specific excitation (340 nm-510 nm) and emission (420 nm-650 nm) wavelength ranges. To identify optimized spectral signatures of OSSN from a small number of channels, swarm intelligence was combined with discriminative cluster analysis. This study established an optimized spectral signature of OSSN derived from multispectral data taken in 38 channels. Depending on the critical nature of the application and the consequences of misclassification error, two optimized spectral signatures with 5 and 10 channels were obtained which reduced the imaging time to 20 and 40 seconds, a reduction by 75% and 80 %, respectively. The K-nearest neighbor classifier was then built using OSSN spectral signatures and optimized to successfully detect OSSN with ~1% and ~14% misclassification error using 10 and 5 channels, respectively. Our study found an optimized spectral signature of OSSN allowing rapid diagnostic imaging in clinical settings and demonstrates the feasibility of using optimized multispectral autofluorescence spectral signatures to detect and determine boundaries of OSSN.

In this study, differentiation of pterygium vs. ocular surface squamous neoplasia based on multispectral autofluorescence imaging technique was investigated. Fifty (N = 50) patients with histopathological diagnosis of pterygium (PTG) and/or ocular surface squamous neoplasia (OSSN) were recruited. Fixed unstained biopsy specimens were imaged by multispectral microscopy. Tissue autofluorescence images were obtained with a custom-built fluorescent microscope with 59 spectral channels, each with specific excitation and emission wavelength ranges, suitable for the most abundant tissue fluorophores such as elastin, flavins, porphyrin, and lipofuscin. Images were analyzed using a new classification framework called fused-classification, designed to minimize interpatient variability, as an established support vector machine learning method. Normal, PTG, and OSSN regions were automatically detected and delineated, with accuracy evaluated against expert assessment by a specialist in OSSN pathology. Signals from spectral channels yielding signals from elastin, flavins, porphyrin, and lipofuscin were significantly different between regions classified as normal, PTG, and OSSN (p < 0.01). Differential diagnosis of PTG/OSSN and normal tissue had accuracy, sensitivity, and specificity of 88 ± 6%, 84 ± 10% and 91 ± 6%, respectively. Our automated diagnostic method generated maps of the reasonably well circumscribed normal/PTG and OSSN interface. PTG and OSSN margins identified by our automated analysis were in close agreement with the margins found in the H&E sections. Such a map can be rapidly generated on a real time basis and potentially used for intraoperative assessment.