Association between fatty acid intake and age-related macular degeneration: a meta-analysis.

Objective: The association of age-related macular degeneration (AMD) with the intake of high and low fatty acids (FAs), respectively, remains controversial. To this end, we performed a comprehensive meta-analysis of all the existing studies on the association of various intake levels of FA subtypes with AMD to determine these associations. Methods: A systematic search of PubMed, Web of Science, Cochrane Library, and EMBASE databases was conducted from inception to September 2023. To compare the highest and lowest groups, odds ratio (OR) with 95% confidence intervals (CIs) was analyzed with a random-effects model/fixed-effects model. Results: A high intake of omega-3 LCPUFAs (OR:0.67; 95%CI:[0.51, 0.88]; p = 0.004), DHA (OR:0.80; 95%CI:[0.70, 0.90]; p < 0.001), EPA (OR:0.91; 95%CI:[0.86, 0.97]; p = 0.004), and simultaneous intake of DHA and EPA (OR:0.79; 95%CI:[0.67, 0.93]; p = 0.035) significantly reduced the risk of overall AMD. Conversely, a high intake of trans-FAs (OR: 2.05; 95%CI: [1.29, 3.25]; p = 0.002) was significantly related to an increased risk of advanced AMD compared to the low-intake group. The subgroup analysis results are shown in the articles. Conclusion: Increasing dietary intake of omega-3 LCPUFAs, specifically DHA, and EPA, or the simultaneous intake of DHA and EPA, is significantly associated with a reduced risk of overall AMD. Various subtypes of omega-3 also have a significant association with a reduced risk of different stages of AMD. The high intake of trans-fatty acids (TFAs) is significantly and positively correlated with the risk of advanced AMD. This could further support the idea that consuming foods rich in omega-3 LCPUFAs and reducing consumption of foods rich in TFAs may prevent AMD. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023467227.

Frontal and anterior temporal hypometabolism post chemoradiation in head and neck cancer: A real-world PET study.

BACKGROUND AND PURPOSE: Adverse neurological effects after cancer therapy are common, but biomarkers to diagnose, monitor, or risk stratify patients are still not validated or used clinically. An accessible imaging method, such as fluorodeoxyglucose positron emission tomography (FDG PET) of the brain, could meet this gap and serve as a biomarker for functional brain changes. We utilized FDG PET to evaluate which brain regions are most susceptible to altered glucose metabolism after chemoradiation in patients with head and neck cancer (HNCa). METHODS: Real-world FDG PET images were acquired as standard of care before and after chemoradiation for HNCa in 68 patients. Linear mixed-effects voxelwise models assessed changes after chemoradiation in cerebral glucose metabolism quantified with standardized uptake value ratio (SUVR), covarying for follow-up time and patient demographics. RESULTS: Voxelwise analysis revealed two large clusters of decreased glucose metabolism in the medial frontal and polar temporal cortices following chemoradiation, with decreases of approximately 5% SUVR after therapy. CONCLUSIONS: These findings provide evidence that standard chemoradiation for HNCa can lead to decreased neuronal glucose metabolism, contributing to literature emphasizing the vulnerability of the frontal and anterior temporal lobes, especially in HNCa, where these areas may be particularly vulnerable to indirect radiation-induced injury. FDG PET shows promise as a sensitive biomarker for assessing these changes.

Machine learning for accurate estimation of fetal gestational age based on ultrasound images.

Accurate estimation of gestational age is an essential component of good obstetric care and informs clinical decision-making throughout pregnancy. As the date of the last menstrual period is often unknown or uncertain, ultrasound measurement of fetal size is currently the best method for estimating gestational age. The calculation assumes an average fetal size at each gestational age. The method is accurate in the first trimester, but less so in the second and third trimesters as growth deviates from the average and variation in fetal size increases. Consequently, fetal ultrasound late in pregnancy has a wide margin of error of at least ±2 weeks' gestation. Here, we utilise state-of-the-art machine learning methods to estimate gestational age using only image analysis of standard ultrasound planes, without any measurement information. The machine learning model is based on ultrasound images from two independent datasets: one for training and internal validation, and another for external validation. During validation, the model was blinded to the ground truth of gestational age (based on a reliable last menstrual period date and confirmatory first-trimester fetal crown rump length). We show that this approach compensates for increases in size variation and is even accurate in cases of intrauterine growth restriction. Our best machine-learning based model estimates gestational age with a mean absolute error of 3.0 (95% CI, 2.9-3.2) and 4.3 (95% CI, 4.1-4.5) days in the second and third trimesters, respectively, which outperforms current ultrasound-based clinical biometry at these gestational ages. Our method for dating the pregnancy in the second and third trimesters is, therefore, more accurate than published methods.

Perception from students regarding online synchronous interactive teaching in the clinical year during COVID-19 pandemic.

AIM: The global pandemic of COVID-19 has led to extensive practice of online learning. Our main objective is to compare different online synchronous interactive learning activities to evaluate students' perceptions. Moreover, we also aim to identify factors influencing their perceptions in these classes. METHODS: A cross-sectional, questionnaire-based study focusing on clinical year medical students' perceptions and feedback was conducted between February 2021 -June 2021 at the University of Hong Kong. Online learning activities were divided into bedside teaching, practical skill session, problem-based learning (PBL) or tutorial, and lecture. A questionnaire based on the Dundee Ready Education Environment Measure (DREEM) was distributed to 716 clinical year students to document their perceptions. RESULTS: One hundred responses were received with a response rate of 15.4% (110/716, including 96 from bedside teaching, 67 from practical skill session, 104 from PBL/tutorial, and 101 from lecture). For the mean score of the DREEM-extracted questionnaire, online PBL/tutorial scored the highest (2.72 ± 0.54), while bedside scored the lowest (2.38 ± 0.68, p = 0.001). Meanwhile, there was no significant difference when we compared different school years (p = 0.39), age (p = 0.37), gender (p = 1.00), year of internet experience (<17 vs ≥17 years p = 0.59), or prior online class experience (p = 0.62). When asked about students' preference for online vs face-to-face classes. Students showed higher preferences for online PBL/tutorial (2.06 ± 0.75) and lectures (2.27 ± 0.81). Distraction remains a significant problem across all four learning activities. A multivariate analysis was performed regarding students' reported behavior in comparison with their perception through the DREEM-extracted questionnaire. The results showed that good audio and video quality had a significant and positive correlation with their perception of online bedside teaching, practical skill sessions, and PBL/tutorial. It also showed that the use of the video camera correlated with an increase in perception scores for lectures. CONCLUSION: The present analysis has demonstrated that students' perception of different online synchronous interactive learning activities varies. Further investigations are required on minimizing distraction during online classes.

A Machine Learning Method for Automated Description and Workflow Analysis of First Trimester Ultrasound Scans.

Obstetric ultrasound assessment of fetal anatomy in the first trimester of pregnancy is one of the less explored fields in obstetric sonography because of the paucity of guidelines on anatomical screening and availability of data. This paper, for the first time, examines imaging proficiency and practices of first trimester ultrasound scanning through analysis of full-length ultrasound video scans. Findings from this study provide insights to inform the development of more effective user-machine interfaces, of targeted assistive technologies, as well as improvements in workflow protocols for first trimester scanning. Specifically, this paper presents an automated framework to model operator clinical workflow from full-length routine first-trimester fetal ultrasound scan videos. The 2D+t convolutional neural network-based architecture proposed for video annotation incorporates transfer learning and spatio-temporal (2D+t) modelling to automatically partition an ultrasound video into semantically meaningful temporal segments based on the fetal anatomy detected in the video. The model results in a cross-validation A1 accuracy of 96.10% , F1=0.95 , precision =0.94 and recall =0.95 . Automated semantic partitioning of unlabelled video scans (n=250) achieves a high correlation with expert annotations ( ρ = 0.95, p=0.06 ). Clinical workflow patterns, operator skill and its variability can be derived from the resulting representation using the detected anatomy labels, order, and distribution. It is shown that nuchal translucency (NT) is the toughest standard plane to acquire and most operators struggle to localize high-quality frames. Furthermore, it is found that newly qualified operators spend 25.56% more time on key biometry tasks than experienced operators.

Vascular calcification maladaptively participates in acute phosphate homeostasis.

AIMS: Non-renal extravasation of phosphate from the circulation and transient accumulation into tissues and extracellular fluid is a regulated process of acute phosphate homeostasis that is not well understood. This process is especially relevant in the setting of chronic kidney disease (CKD), where exposure to increased phosphate is prolonged due to inefficient kidney excretion. Furthermore, CKD-associated mineral dysregulation induces pathological accumulation of phosphate causing vascular calcification (VC). Our objective was to determine whether the systemic response to acute phosphate challenges is altered by VC. METHODS AND RESULTS: After bolus phosphate administration, circulating and tissue deposition of this challenge was assessed in two rat models of VC using a radiolabelled phosphate tracer. In an adenine-induced model of CKD (N = 70), animals with VC had a blunted elevation of circulating 33PO4 following oral phosphate administration (P < 0.01), and the discordant deposition could be traced to the calcified arteries (11.4 [7.5-13.1] vs.43.0 [35.5-53.7] pmol/ng tissue, P < 0.001). In a non-CKD model of VC, calcification was induced with 0.5 ug/kg calcitriol and then withdrawn (N = 24). New phosphate uptake by the calcified vasculature correlated to the pre-existing burden of calcification (r = 38, P < 0.001) and was substantially attenuated in the absence of calcification stimulus (P < 0.01). Phosphate accrual was stimulated by the phosphate challenge and not present to the same degree during passive disposition of circulating phosphate. Further, the form of phosphate that deposited to the vasculature was predominately amorphous inorganic phosphate and not that which was bound in matured calciprotein particles. CONCLUSIONS: In the process of calcification, arteries acutely deposit substantial amorphous phosphate while blunting the elevation in the circulation, thereby altering the systemic disposition of phosphate and identifying VC as a participatory mineral homeostatic organ. This study demonstrates the negative vascular consequence of acute fluctuations in circulating phosphate, and supports the importance of phosphate bioavailability and diet management in CKD patients as a mediator of cardiovascular risk.

Artificial intelligence in diagnosis of knee osteoarthritis and prediction of arthroplasty outcomes: a review.

BACKGROUND: Artificial intelligence is an emerging technology with rapid growth and increasing applications in orthopaedics. This study aimed to summarize the existing evidence and recent developments of artificial intelligence in diagnosing knee osteoarthritis and predicting outcomes of total knee arthroplasty. METHODS: PubMed and EMBASE databases were searched for articles published in peer-reviewed journals between January 1, 2010 and May 31, 2021. The terms included: 'artificial intelligence', 'machine learning', 'knee', 'osteoarthritis', and 'arthroplasty'. We selected studies focusing on the use of AI in diagnosis of knee osteoarthritis, prediction of the need for total knee arthroplasty, and prediction of outcomes of total knee arthroplasty. Non-English language articles and articles with no English translation were excluded. A reviewer screened the articles for the relevance to the research questions and strength of evidence. RESULTS: Machine learning models demonstrated promising results for automatic grading of knee radiographs and predicting the need for total knee arthroplasty. The artificial intelligence algorithms could predict postoperative outcomes regarding patient-reported outcome measures, patient satisfaction and short-term complications. Important weaknesses of current artificial intelligence algorithms included the lack of external validation, the limitations of inherent biases in clinical data, the requirement of large datasets in training, and significant research gaps in the literature. CONCLUSIONS: Artificial intelligence offers a promising solution to improve detection and management of knee osteoarthritis. Further research to overcome the weaknesses of machine learning models may enhance reliability and allow for future use in routine healthcare settings.

First Trimester video Saliency Prediction using CLSTMU-NET with Stochastic Augmentation.

In this paper we develop a multi-modal video analysis algorithm to predict where a sonographer should look next. Our approach uses video and expert knowledge, defined by gaze tracking data, which is acquired during routine first-trimester fetal ultrasound scanning. Specifically, we propose a spatio-temporal convolutional LSTMU-Net neural network (cLSTMU-Net) for video saliency prediction with stochastic augmentation. The architecture design consists of a U-Net based encoder-decoder network and a cLSTM to take into account temporal information. We compare the performance of the cLSTMU-Net alongside spatial-only architectures for the task of predicting gaze in first trimester ultrasound videos. Our study dataset consists of 115 clinically acquired first trimester US videos and a total of 45, 666 video frames. We adopt a Random Augmentation strategy (RA) from a stochastic augmentation policy search to improve model performance and reduce over-fitting. The proposed cLSTMU-Net using a video clip of 6 frames outperforms the baseline approach on all saliency metrics: KLD, SIM, NSS and CC (2.08, 0.28, 4.53 and 0.42 versus 2.16, 0.27, 4.34 and 0.39).

End-to-end First Trimester Fetal Ultrasound Video Automated CRL and NT Segmentation.

This study presents a novel approach to automatic detection and segmentation of the Crown Rump Length (CRL) and Nuchal Translucency (NT), two essential measurements in the first trimester US scan. The proposed method automatically localises a standard plane within a video clip as defined by the UK Fetal Abnormality Screening Programme. A Nested Hourglass (NHG) based network performs semantic pixel-wise segmentation to extract NT and CRL structures. Our results show that the NHG network is faster (19.52% < GFlops than FCN32) and offers high pixel agreement (mean-IoU=80.74) with expert manual annotations.

Skill Characterisation of Sonographer Gaze Patterns during Second Trimester Clinical Fetal Ultrasounds using Time Curves.

We present a method for skill characterisation of sonographer gaze patterns while performing routine second trimester fetal anatomy ultrasound scans. The position and scale of fetal anatomical planes during each scan differ because of fetal position, movements and sonographer skill. A standardised reference is required to compare recorded eye-tracking data for skill characterisation. We propose using an affine transformer network to localise the anatomy circumference in video frames, for normalisation of eye-tracking data. We use an event-based data visualisation, time curves, to characterise sonographer scanning patterns. We chose brain and heart anatomical planes because they vary in levels of gaze complexity. Our results show that when sonographers search for the same anatomical plane, even though the landmarks visited are similar, their time curves display different visual patterns. Brain planes also, on average, have more events or landmarks occurring than the heart, which highlights anatomy-specific differences in searching approaches.

First Trimester Gaze Pattern Estimation Using Stochastic Augmentation Policy Search for Single Frame Saliency Prediction.

While performing an ultrasound (US) scan, sonographers direct their gaze at regions of interest to verify that the correct plane is acquired and to interpret the acquisition frame. Predicting sonographer gaze on US videos is useful for identification of spatio-temporal patterns that are important for US scanning. This paper investigates utilizing sonographer gaze, in the form of gaze-tracking data, in a multimodal imaging deep learning framework to assist the analysis of the first trimester fetal ultrasound scan. Specifically, we propose an encoderdecoder convolutional neural network with skip connections to predict the visual gaze for each frame using 115 first trimester ultrasound videos; 29,250 video frames for training, 7,290 for validation and 9,126 for testing. We find that the dataset of our size benefits from automated data augmentation, which in turn, alleviates model overfitting and reduces structural variation imbalance of US anatomical views between the training and test datasets. Specifically, we employ a stochastic augmentation policy search method to improve segmentation performance. Using the learnt policies, our models outperform the baseline: KLD, SIM, NSS and CC (2.16, 0.27, 4.34 and 0.39 versus 3.17, 0.21, 2.92 and 0.28).

Automatic Polyp Segmentation in Colonoscopy Images Using a Modified Deep Convolutional Encoder-Decoder Architecture.

Colorectal cancer has become the third most commonly diagnosed form of cancer, and has the second highest fatality rate of cancers worldwide. Currently, optical colonoscopy is the preferred tool of choice for the diagnosis of polyps and to avert colorectal cancer. Colon screening is time-consuming and highly operator dependent. In view of this, a computer-aided diagnosis (CAD) method needs to be developed for the automatic segmentation of polyps in colonoscopy images. This paper proposes a modified SegNet Visual Geometry Group-19 (VGG-19), a form of convolutional neural network, as a CAD method for polyp segmentation. The modifications include skip connections, 5 × 5 convolutional filters, and the concatenation of four dilated convolutions applied in parallel form. The CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB databases were used to evaluate the model, and it was found that our proposed polyp segmentation model achieved an accuracy, sensitivity, specificity, precision, mean intersection over union, and dice coefficient of 96.06%, 94.55%, 97.56%, 97.48%, 92.3%, and 95.99%, respectively. These results indicate that our model performs as well as or better than previous schemes in the literature. We believe that this study will offer benefits in terms of the future development of CAD tools for polyp segmentation for colorectal cancer diagnosis and management. In the future, we intend to embed our proposed network into a medical capsule robot for practical usage and try it in a hospital setting with clinicians.

Imaging of knee osteoarthritis: A review of current evidence and clinical guidelines.

BACKGROUND: Knee osteoarthritis (OA) is one of the most common and debilitating degenerative joint diseases worldwide. While radiography is the most commonly used imaging modality, it is associated with drawbacks which newer modalities such as magnetic resonance imaging (MRI) and ultrasound could overcome. Nevertheless, the role of imaging in clinical practice and research in knee OA has not been clearly defined. Furthermore, guidelines on imaging in knee OA from different authoritative bodies have not been compared in previous studies. Therefore, the present review aims to summarise existing evidence and compare guidelines on the use of different imaging modalities in evaluating knee OA. METHODS: This is a narrative review based on a search of published clinical guidelines and the PubMed database for articles published between 1 January 1990 and 31 May 2020. RESULTS: There is no broad consensus on the value of imaging in patients with typical OA presentation. If imaging is required, current evidence and clinical guidelines support the use of radiography and MRI as first- and second-line diagnostic modalities respectively. Since radiographic OA features have limited sensitivity and do not manifest in early stages, MRI is the preferred option for whole-joint evaluation in OA research. Discrepancies exist regarding the use of alternative imaging modalities including ultrasound, computed tomography and nuclear medicine. CONCLUSION: Radiography and MRI are the imaging modalities of choice. Other modalities have their respective advantages, and more research is warranted for the standardisation of image acquisition and interpretation methodology, in order to evaluate their validity, reliability and responsiveness in OA research.

Lessons learnt from the impact of COVID-19 on arthroplasty services in Hong Kong: how to prepare for the next pandemic?

BACKGROUND: Arthroplasty services worldwide have been significantly disrupted by the pandemic of coronavirus disease 2019 (COVID-19). This retrospective comparative study aimed to characterize its impact on arthroplasty services in Hong Kong. METHODS: From January 1 to June 30, 2020, the patients of "COVID-19 cohort" underwent elective total hip or knee replacement in Hong Kong public hospitals. The cohort was compared to the "control cohort" during the same period in 2019. Data analysis was performed to compare the two cohorts' numbers of operations, hospital admission, orthopaedic clinic attendances, and waiting time. RESULTS: A total of 33,111 patient episodes were analyzed. During the study period, the elective arthroplasty operations and hospitalizations decreased by 53 and 54%, respectively (P < 0.05). Reductions were most drastic from February to April, with surgical volume declining by 86% (P < 0.05). The primary arthroplasty operations decreased by 91% (P < 0.05), while the revision operations remained similar. Nevertheless, 14 public hospitals continued performing elective arthroplasty for patients with semi-urgent indications, including infection, progressive bone loss, prosthesis loosening, dislocation or mechanical failure of arthroplasty, and tumor. At the institution with the highest arthroplasty surgical volume, infection (28%) was the primary reason for surgery, followed by prosthesis loosening (22%) and progressive bone loss (17%). The orthopaedic clinic attendances also decreased by 20% (P < 0.05). Increases were observed in waiting time and the total number of patients on the waiting list for elective arthroplasty. CONCLUSIONS: Despite the challenges, public hospitals in Hong Kong managed to continue providing elective arthroplasty services for high-priority patients. Arthroplasty prioritization, infection control measures, and post-pandemic service planning can enhance hospital preparedness to mitigate the impact of current and future pandemics.

Simulating realistic fetal neurosonography images with appearance and growth change using cycle-consistent adversarial networks and an evaluation.

Purpose: We present an original method for simulating realistic fetal neurosonography images specifically generating third-trimester pregnancy ultrasound images from second-trimester images. Our method was developed using unpaired data, as pairwise data were not available. We also report original insights on the general appearance differences between second- and third-trimester fetal head transventricular (TV) plane images. Approach: We design a cycle-consistent adversarial network (Cycle-GAN) to simulate visually realistic third-trimester images from unpaired second- and third-trimester ultrasound images. Simulation realism is evaluated qualitatively by experienced sonographers who blindly graded real and simulated images. A quantitative evaluation is also performed whereby a validated deep-learning-based image recognition algorithm (ScanNav®) acts as the expert reference to allow hundreds of real and simulated images to be automatically analyzed and compared efficiently. Results: Qualitative evaluation shows that the human expert cannot tell the difference between real and simulated third-trimester scan images. 84.2% of the simulated third-trimester images could not be distinguished from the real third-trimester images. As a quantitative baseline, on 3000 images, the visibility drop of the choroid, CSP, and mid-line falx between real second- and real third-trimester scans was computed by ScanNav® and found to be 72.5%, 61.5%, and 67%, respectively. The visibility drop of the same structures between real second-trimester and simulated third-trimester was found to be 77.5%, 57.7%, and 56.2%, respectively. Therefore, the real and simulated third-trimester images were consider to be visually similar to each other. Our evaluation also shows that the third-trimester simulation of a conventional GAN is much easier to distinguish, and the visibility drop of the structures is smaller than our proposed method. Conclusions: The results confirm that it is possible to simulate realistic third-trimester images from second-trimester images using a modified Cycle-GAN, which may be useful for deep learning researchers with a restricted availability of third-trimester scans but with access to ample second trimester images. We also show convincing simulation improvements, both qualitatively and quantitatively, using the Cycle-GAN method compared with a conventional GAN. Finally, the use of a machine learning-based reference (in the case ScanNav®) for large-scale quantitative image analysis evaluation is also a first to our knowledge.

Current and future molecular diagnostics of gastric cancer.

Introduction: Gastric cancer (GC) is the fifth most common cancer and confers the second-highest mortality among other cancers. Improving the survival rates of GC patients requires prompt and accurate diagnosis and effective treatment which is often preceded by the poorly understood pathogenic mechanisms. Area covered: This literature review aims to summarize current understanding of genetic and molecular alterations that promote carcinogenesis including (1) activation of oncogenes, (2) overexpression of growth factors, receptors and matrix metalloproteinases, (3) inactivation of tumor suppressor genes, DNA repair genes, and cell adhesion molecules and (4) alterations of cell-cycle regulators that regulate biological characteristics of cancer cells. Moreover, the significance of molecular biomarkers such as micro-RNAs (miRNAs) and long non-coding RNAs (lncRNAs) and advanced molecular techniques including droplet digital polymerase chain reaction (ddPCR), quantitative PCR (qPCR) and next-generation sequencing (NGS) are also discussed. Expert opinion: A GC-specific panel of biomarkers based on the NGS or ddPCR has the potential for diagnosis, prognosis, and monitoring treatment response in GC patients. Despite the requirements for validation in larger population in clinical studies, race-specific differences in the gene panel have also to be examined by performing the clinical trials in subjects with different races.

The potential of circulating cell free RNA as a biomarker in cancer.

Introduction: It is now clear that circulating cell-free ribonucleic acids (ccfRNAs), including messenger RNA (mRNA) and miRNA, are potential cancer biomarkers. As ccfmiRNA is relatively more stable than ccfmRNA, research should concentrate on developing novel methods to preserve the stability of ccfmRNA and standardization of the protocol which includes extraction, detection, and multicenter validation. Areas covered: This literature review concentrates on the potential of ccfRNA being used as a biomarker in cancer, with special focus on mRNAs and microRNAs (miRNAs). Expert opinion: With the advancement of high-throughput technologies such as RNA sequencing, a panel of biomarkers will be used for the diagnosis, prognosis and therapeutic monitoring of cancer patients. In order to achieve this important target, bioinformatics education to pathologists, scientists, and technologists in molecular diagnostic laboratories is essential. Moreover, the panel of these new ccfRNAs biomarkers has to obtain approval or clearance from an authority such as the US Food and Drug Administration (FDA), and the standard of utilizing these new protocols has to be recognized via accreditation exercise. Therefore, there is still a long way to go before an extensively use of ccfRNA biomarkers in cancer patients can be realized.

Targeting discriminatory SNPs in Salmonella enterica serovar Heidelberg genomes using RNase H2-dependent PCR.

We report a novel RNase H2-dependent PCR (rhPCR) genotyping assay for a small number of discriminatory single-nucleotide polymorphisms (SNPs) that identify lineages and sub-lineages of the highly clonal pathogen Salmonella Heidelberg (SH). Standard PCR primers targeting numerous SNP locations were initially designed in silico, modified to be RNase H2-compatible, and then optimized by laboratory testing. Optimization often required repeated cycling through variations in primer design, assay conditions, reagent concentrations and selection of alternative SNP targets. The final rhPCR assay uses 28 independent rhPCR reactions to target 14 DNA bases that can distinguish 15 possible lineages and sub-lineages of SH. On evaluation, the assay correctly identified the 12 lineages and sub-lineages represented in a panel of 75 diverse SH strains. Non-specific amplicons were observed in 160 (15.2%) of the 1050 reactions, but due to their low intensity did not compromise assay performance. Furthermore, in silico analysis of 500 closed genomes from 103 Salmonella serovars and laboratory rhPCR testing of five prevalent Salmonella serovars including SH indicated the assay can identify Salmonella isolates as SH, since only SH isolates generated amplicons from all 14 target SNPs. The genotyping results can be fully correlated with whole genome sequencing (WGS) data in silico. This fast and economical assay, which can identify SH isolates and classify them into related or unrelated lineages and sub-lineages, has potential applications in outbreak identification, source attribution and microbial source tracking.

Determination of Dacarbazine Φ-Order Photokinetics, Quantum Yields, and Potential for Actinometry.

The characterization of drugs' photodegradation kinetics is more accurately achieved by means of the recently developed Φ-order kinetics than by the zero-, first-, and/or second-order classical treatments. The photodegradation of anti-cancer dacarbazine (DBZ) in ethanol has been investigated and found to obey Φ-order kinetics when subjected to continuous and monochromatic irradiation of various wavelengths. Its photochemical efficiency was proven to be wavelength dependent in the 220-350 nm range, undergoing a 50-fold increase. Albeit this variation was well defined by a sigmoid pattern, the overall photoreactivity of DBZ was proven to depend also on the contributions of reactants and experimental attributes. The usefulness of DBZ to serve as a drug-actinometer has been investigated using the mathematical framework of Φ-order kinetics. It has been shown that DBZ in ethanol can represent a good candidate for reliable actinometry in the range 270-350 nm. A detailed and easy-to-implement procedure has been proposed for DBZ actinometry. This procedure could advantageously be implemented prior to the determination of the photodegradation quantum yields. This approach might be found useful for the development of many drug actinometers as alternatives to quinine hydrochloride.

Actinometric and Φ-order photodegradation properties of anti-cancer Sunitinib.

The photodegradation reaction of Sunitinib (SUT), occurring via Z-E photoisomerisation, has been evaluated in this study using the recently developed Φ-order kinetics. In ethanol, the forward (Z → E) photoreaction of SUT was invariant with irradiation (its quantum yield, Φ(E-->Z)(λ)(irr) ≈ 0.019) in contrast to the E → Z isomerisation whose Φ(E-->Z)(λ)(irr) undergoes a 30-fold, sigmoid-shaped, increase with increasing irradiation wavelength. This situation limited usefully the extent of Z-SUT photodegradation at the photostationary state to a maximum of c.a. 30% of the initial concentration. Nevertheless, these results support a strong recommendation for a complete protection of SUT from light at all stages. Furthermore, a SUT-actinometer was developed and was proven to be useful for the 320-480 nm spectral range. The latter wavelength interval defined as well SUT photodegradation causative range. The formalism of Φ-order kinetics proves to be a useful investigative tool for drugs' photodegradation studies.