Morphological and molecular changes of oestrogen receptor-positive breast cancer following bridging endocrine therapy: a United Kingdom multicentre study.

AIMS: Standard neoadjuvant endocrine therapy (NAET) is used for 6-9 months to downstage hormone-receptor-positive breast cancer. Bridging ET was introduced during the COVID-19 pandemic to delay surgical intervention. There are no data in the literature on the effect of short course therapy on tumour response. We aimed to analyse the effect of bridging ET and validate the previously proposed neoadjuvant ET pathological reporting criteria. METHODS AND RESULTS: This was a multicentre cohort of 256 patients who received bridging ET between March and October 2020. Assessment of paired pre- and post-NAET hormone receptors and HER2 and posttherapy Ki67 expression was done. The median duration of NAET was 45 days. In all, 86% of cases achieved partial pathological response and 9% showed minimal residual disease. Histological response to ET was observed from as early as day 6 posttherapy. Central scarring was noted in 32.8% of cases and lymphocytic infiltrate was seen in 43.4% of cases. Significant changes associated with the duration of ET were observed in tumour grade (21%), with downgrading identified in 12% of tumours (P < 0.001), progesterone receptor (PR) expression with switch to PR-negative status in 26% of cases (P < 0.001), and HER2 status with a switch from HER2-low to HER2-negative status in 32% of cases (P < 0.001). The median patient survival was 475 days, with an overall survival rate of 99.6%. CONCLUSIONS: Changes characteristic of tumour regression and significant changes in PR and HER2 occurred following a short course of NAET. The findings support biomarker testing on pretreatment core biopsies and retesting following therapy.

Synthesis and characterization of photocatalytic polyurethane and poly(methyl methacrylate) microcapsules for the controlled release of methotrexate.

The aim of this work is to prepare ultraviolet (UV) triggered controlled release of compounds from microcapsule systems (MCs). Polyurethane (PU) and poly(methyl methacrylate) (PMMA) microcapsules were studied with/without chemical functionalization using photocatalytic TiO2 nanoparticles (NPs) on their surface. Once TiO2 nanoparticles are illuminated with UV light (λ = 370 nm), they initiate the rupture of the polymeric bonds of the microcapsule and subsequently initiate the encapsulated compound release, methotrexate (MTX) or rhodamine (Rh), in the present work. The size, polydispersity, charge, and yield of all MCs were measured, being the methotrexate drug release for all systems determined and compared with and without functionalization with TiO2 NPs, under dark, visible light and UV illumination in vitro. Finally, the Rh release was characterized using fluorescence microscopy. The TiO2 NPs size is around 10 nm, as determined by X-ray diffraction experiments. The PU MCs average size is around 60 µm, its electric charge +3.11 mV and yield around 85%. As for the PMMA MCs, the average size is around 280 µm, its electric charge -7.2 mV and yield around 25% and 30% for both MTX and Rh, respectively. In general, adding TiO2 NPs or the encapsulated products to the MCs does not affect the size but functionalization with TiO2 NPs lowers the electric charge. Microcapsules functionalized with TiO2 nanoparticles and irradiated with UV light presented the highest release of MTX and Rh. All other samples showed lower drug release levels when studied under the same conditions.

Erratum: Al-Fandi, M.; et al. Novel Selective Detection Method of Tumor Angiogenesis Factors Using Living Nano-Robots. Sensors 2017, 17, 1580.

The authors wish to correct the spelling of the third author's name from Rami Owies to Rami Oweis in their paper published in Sensors [1], doi:10.3390/s17071580, http://www.mdpi.com/1424- 8220/17/7/1580.[...].

Novel Selective Detection Method of Tumor Angiogenesis Factors Using Living Nano-Robots.

This paper reports a novel self-detection method for tumor cells using living nano-robots. These living robots are a nonpathogenic strain of E. coli bacteria equipped with naturally synthesized bio-nano-sensory systems that have an affinity to VEGF, an angiogenic factor overly-expressed by cancer cells. The VEGF-affinity/chemotaxis was assessed using several assays including the capillary chemotaxis assay, chemotaxis assay on soft agar, and chemotaxis assay on solid agar. In addition, a microfluidic device was developed to possibly discover tumor cells through the overexpressed vascular endothelial growth factor (VEGF). Various experiments to study the sensing characteristic of the nano-robots presented a strong response toward the VEGF. Thus, a new paradigm of selective targeting therapies for cancer can be advanced using swimming E. coli as self-navigator miniaturized robots as well as drug-delivery vehicles.

An alternative respiratory sounds classification system utilizing artificial neural networks.

BACKGROUND: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. METHODS: This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification. RESULTS: The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. CONCLUSIONS: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

A PC-aided optical foetal heart rate detection system.

Safe monitoring of foetal heart rate is a valuable tool for the healthy evolution and wellbeing of both foetus and mother. This paper presents a non-invasive optical technique that allows for foetal heart rate detection using a photovoltaic infrared (IR) detector placed on the mother's abdomen. The system presented here consists of a photoplethysmography (PPG) circuit, abdomen circuit and a personal computer equipped with MATLAB. A near IR beam having a wavelength of 880 nm is transmitted through the mother's abdomen and foetal tissue. The received abdominal signal that conveys information pertaining to the mother and foetal heart rate is sensed by a low noise photodetector. The PC receives the signal through the National Instrumentation Data Acquisition Card (NIDAQ). After synchronous detection of the abdominal and finger PPG signals, the designed MATLAB-based software saves, analyses and extracts information related to the foetal heart rate. Extraction is carried out using recursive least squares adaptive filtration. Measurements on eight pregnant women with gestational periods ranging from 35-39 weeks were performed using the proposed system and CTG. Results show a correlation coefficient of 0.978 and a correlation confidence interval between 88-99.6%. The t test results in a p value of 0.034, which is less than 0.05. Low power, low cost, high signal-to-noise ratio, reduction of ambient light effect and ease of use are the main characteristics of the proposed system.

An intelligent healthcare management system: a new approach in work-order prioritization for medical equipment maintenance requests.

The effective maintenance management of medical technology influences the quality of care delivered and the profitability of healthcare facilities. Medical equipment maintenance in Jordan lacks an objective prioritization system; consequently, the system is not sensitive to the impact of equipment downtime on patient morbidity and mortality. The current work presents a novel software system (EQUIMEDCOMP) that is designed to achieve valuable improvements in the maintenance management of medical technology. This work-order prioritization model sorts medical maintenance requests by calculating a priority index for each request. Model performance was assessed by utilizing maintenance requests from several Jordanian hospitals. The system proved highly efficient in minimizing equipment downtime based on healthcare delivery capacity, and, consequently, patient outcome. Additionally, a preventive maintenance optimization module and an equipment quality control system are incorporated. The system is, therefore, expected to improve the reliability of medical equipment and significantly improve safety and cost-efficiency.

Seizure classification in EEG signals utilizing Hilbert-Huang transform.

BACKGROUND: Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. METHOD: Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. RESULTS: The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use. CONCLUSION: An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

A computer-aided ECG diagnostic tool.

Jordan lacks companies that provide local medical facilities with products that are of help in daily performed medical procedures. Because of this, the country imports most of these expensive products. Consequently, a local interest in producing such products has emerged and resulted in serious research efforts in this area. The main goal of this paper is to provide local (the north of Jordan) clinics with a computer-aided electrocardiogram (ECG) diagnostic tool in an attempt to reduce time and work demands for busy physicians especially in areas where only one general medicine doctor is employed and a bulk of cases are to be diagnosed. The tool was designed to help in detecting heart defects such as arrhythmias and heart blocks using ECG signal analysis depending on the time-domain representation, the frequency-domain spectrum, and the relationship between them. The application studied here represents a state of the art ECG diagnostic tool that was designed, implemented, and tested in Jordan to serve wide spectrum of population who are from poor families. The results of applying the tool on randomly selected representative sample showed about 99% matching with those results obtained at specialized medical facilities. Costs, ease of interface, and accuracy indicated the usefulness of the tool and its use as an assisting diagnostic tool.

Medical waste management in Jordan: a study at the King Hussein Medical Center.

As in many other developing countries, the generation of regulated medical waste (RMW) in Jordan has increased significantly over the last few decades. Despite the serious impacts of RMW on humans and the environment, only minor attention has been directed to its proper handling and disposal. This study was conducted in the form of a case study at one of Jordan's leading medical centers, namely, the King Hussein Medical Center (KHMC). Its purpose was to report on the current status of medical waste management at KHMC and propose possible measures to improve it. In general, it was found that the center's administration was reasonably aware of the importance of medical waste management and practiced some of the measures to adequately handle waste generated at the center. However, it was also found that significant voids were present that need to be addressed in the future including efficient segregation, the use of coded and colored bags, better handling and transfer means, and better monitoring and tracking techniques, as well as the need for training and awareness programs for the personnel.