Objectives Breast cancer poses a significant global health challenge, characterized by complex origins and the potential for life-threatening metastasis. The critical need for early and accurate detection is underscored by the 685,000 lives claimed by the disease worldwide in 2020. Deep learning has made strides in advancing the prompt diagnosis of breast cancer. However, obstacles persist, such as dealing with high-dimensional data and the risk of overfitting, necessitating fresh approaches to improve accuracy and real-world applicability.
Methods In response to these challenges, we propose BCED-Net, which stands for Breast Cancer Ensemble Diagnosis Network. This innovative framework leverages transfer learning and the extreme gradient boosting (XGBoost) classifier on the Breast Cancer RSNA dataset. Our methodology involved feature extraction using pre-trained models—namely, Resnet50, EfficientnetB3, VGG19, Densenet121, and ConvNeXtTiny—followed by the concatenation of the extracted features. Our most promising configuration combined features extracted from deep convolutional neural networks—namely Resnet50, EfficientnetB3, and ConvNeXtTiny—that were classified using the XGBoost classifier.
Results The ensemble approach demonstrated strong overall performance with an accuracy of 0.89. The precision, recall, and F1-score values, which were all at 0.86, highlight a balanced trade-off between correctly identified positive instances and the ability to capture all actual positive samples.
Conclusion BCED-Net represents a significant leap forward in addressing persistent issues such as the high dimensionality of features and the risk of overfitting.
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Objectives
The purpose of this study was to evaluate the performance indicators for obstetrics and gynecology wards in Iran. Methods
This study was designed as a cross-sectional study and was carried out in obstetrics and gynecology wards of Afshar and Shahid Sadoughi hospitals in 2015. The information required for the performance indicators was gathered through questionnaires, statistical forms, and direct observation. In several tables, performance rating and performance status are presented using the Likert scale index based on the expected limits. Results
According to the results of the categories of input indicators, the bed occupancy rate of Afshar Hospital’s obstetrics and gynecology ward was, at 83%, higher than expected (79%), and that in Shahid Sadoughi Hospital (at 69%) was lower than expected. For medicinal methods and nonmedicinal methods of pain alleviation, the index process at Afshar Hospital was much lower than expected (40%). In Afshar Hospital, patient satisfaction at discharge was about 66.74%. Conclusion
Effective steps can be taken to improve the input and output criteria: allocating appropriate physical space, examining the reasons for low bed occupancy rate by using complex analytical models, and in order to study the reasons for large number of cesarean section childbirth, it was recommended to place more emphasis on training of pregnant mothers and to inform them about the side effects of cesarean section and advantages of natural childbirth.
Objectives
This study provides information to aid decision making for managers and the staff of national university hospitals through analyzing their financial statements. Methods
In order to analyze the finances of national university hospitals, this study used the report of final accounts announced by each hospital from 2009 to 2012 as baseline data. The research participants were 10 national university hospitals. Results
According to the results of the analysis, most hospitals (except for a few) had medical expenses exceeding their medical revenues, resulting in a net deficit; however, there were significant differences amongst the hospitals. The result of adjustments based on a standard size of 100 beds showed that most hospitals had medical revenue deficits, and there were significant differences between hospitals in terms of medical revenues and medical costs. Conclusion
It is not clear whether an expansion of national university hospitals is always beneficial for increasing net revenues, and it is necessary to establish a differentiation strategy to increase profitability by securing financial soundness instead of externally-oriented growth.
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Objectives
This paper provides information for decision making of the managers and the staff of national university hospitals. Methods
In order to conduct a financial analysis of national university hospitals, this study uses reports on the final accounts of 10 university hospitals from 2008 to 2011. Results
The results of comparing 2008 and 2011 showed that there was a general decrease in total assets, an increase in liabilities, and a decrease in total medical revenues, with a continuous deficit in many hospitals. Moreover, as national university hospitals have low debt dependence, their management conditions generally seem satisfactory. However, some individual hospitals suffer severe financial difficulties and thus depend on short-term debts, which generally aggravate the profit and loss structure. Various indicators show that the financial state and business performance of national university hospitals have been deteriorating. Conclusion
These research findings will be used as important basic data for managers who make direct decisions in this uncertain business environment or by researchers who analyze the medical industry to enable informed decision-making and optimized execution. Furthermore, this study is expected to contribute to raising government awareness of the need to foster and support the national university hospital industry.
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Objectives
To develop and optimize quantitative HPLC method using 2,3-naphthalenedicarboxaldehyde (NDA) after simple and efficient solid phase extraction to determine the histamine in a biopharmaceutical (Histobulin™). Methods
The HPLC method was established using NDA-induced Histobulin and compared with the recently reported HPLC method using o-phthaldehyde (OPA). The validated NDA-applied HPLC method was adjusted to 15 lots of Histobulin and compared by the current lot-release-test method using fluorimetry in recovery of histamine and reproducibility. Results
Analyses of six HPLC chromatograms using NDA and OPA each were compared. NDA produced a more stable chromatogram baseline than OPA, and showed better stability. The HPLC analysis was validated in accuracy (91–103%), precision (interday/intraday assay CV ≤2.30%), and linearity of dose–response curve (R<sup>2</sup> ≥ 0.9919). The detection limit was 0.0076 μg/mL and the quantitative limit was 0.0229 μg/mL. The amount of histamine per 12 mg of immunoglobulin was determined to be 0.17 ± 0.016 μg by the HPLC and 0.025 ± 0.013 μg by the current lot-release-test method using fluorimetry. Conclusion
NDA derivatization showed better stability compared with the OPA method. Therefore the newly established NDA-derivatizated HPLC method may be more suitable than the fluorimetric method in lot-release-tests of biopharmaceuticals.
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