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Objective: The objective is to understand the role of information management systems in the public health perspective of primary care units more accurately. Methods: A public health information management system for primary medical units, which is based on electronic health records, virtual private network technology, real-time data storage, and other technologies, is designed on the premise of economical and straightforward operation. Besides, Xinhua Community Health Service Center Around the Wulong Street, Longsha District, Qiqihar City is selected as the experimental unit of the public health information management system, and the work efficiency of the system in the public health perspective of the primary medical unit is evaluated after 12 months of system operation. Results: The public health information management system of primary medical units has following comprehensive management functions: health record management, child health, maternal health, health of the elderly, health of patients with chronic diseases, health of severe psychiatric patients, health education, infectious diseases and public emergencies, health events, health supervision, and management information. In addition, after 12 months of information management system operates in the grassroots units, the results show that patients and doctors have a very high satisfaction rate with the system. The system not only cultivates the excellent health and disease prevention awareness of residents but also improves the efficiency of primary care institutions, as well as reducing the number of patients seeking medical cares. Conclusion: The public health information management system of primary health care units based on medical and health information design is rich in functions with prominent work efficiency, which significantly improves the public health of grass-roots medical units. The research is useful and significant for follow-up studies on public health care systems. Keywords: Medical and health, Public health information management system, Primary medical units, Residents’ files

Plesiomonas shigelloides is an important fish pathogen that causes significant losses in aquaculture. Phage therapy is a new approach to overcome the problem of multidrug-resistant bacteria. Herein, a virulent phage of P. shigelloides was isolated from the intestines of grass carp. This phage belongs to the Siphoviridae family and was designated PSP01. The optimal multiplicity of infection of PSP01 was 1 with a latent period of 30 min and a lytic period of 140 min. Good activity was observed over a wide range of temperatures (-20 °C–50 °C), pH values (3–12), and NaCl concentrations (0.1–3.5%). The phage PSP01 lysis cassette is composed of 3 genes, HolPSP, LysPSP-1 and LysPSP-2. Expression of HolPSP or LysPSP-2 in Escherichia coli resulted in bacterial lysis, and a synergistic effect was observed when the HolPSP and LysPSP-1 proteins were co-expressed. In-frame deletion uncovered an important role of the transmembrane domain (TMD) in HolPSP and the signal peptide (SP) in LysPSP-2 for bacterial lysis function. The protective effects of phage PSP01 were investigated by intraperitoneal injection into grass carp infected with P. shigelloides, showing a 33.3% increase in the survival rate of the infected grass carp. Pathological analysis of the spleens from the infected grass carp revealed alleviation of the pathological symptoms. In conclusion, isolation and bacterial lysis investigations of phage PSP01 provide a new tool for the control of fish pathogens and possesses potential for aquaculture applications.

Background Bacterial resistance to antibiotics is generally increasing, which has become a great challenge for treating infectious diseases caused by microbes. Photodynamic antibacterial chemotherapy (PACT) has been considered as a promising method for inactivating bacteria. The combination of antimicrobial agent with PACT may provide efficient way against drug-resistant microbe. This study aims to investigate the synergistic effects of PACT mediated by toluidine blue (TB), combined with gentamicin (GEN) on common pathogens Staphylococcus aureus (S. aureus) and multidrug-resistant S. aureus (MDR S. aureus). Methods Alkaline lysis was used to detect the uptake of TB by S. aureus and MDR S. aureus. Plate counting was applied to evaluate the inhibition efficiency of GEN alone, TB-PACT alone, and work together. Flow cytometry and fluorescence microscopy were performed to examine the permeability of bacterial membranes after different treatments. Intracellular and extracellular reactive oxygen species (ROS) were assessed with the assist of H2DCF-DA and SOSG probes. Results TB-PACT combined with GEN led to more pronounced antibacterial effects in S. aureus and MDR S. aureus, as compared with either alone. TB-PACT treatment permeabilized the bacterial membranes, promoted GEN cellular accumulation and augmented the antibacterial efficiency. The intracellular ROS generation by the combination of TB-PACT and GEN was much higher than that of single treatment groups. Conclusions TB-PACT decreased the GEN cytotoxic threshold and usage, and the synergy of them significantly enhanced the sterilization of S. aureus and MDR S. aureus.

Computed tomography (CT) is an imaging modality widely used for medical diagnosis and treatment. CT images are often corrupted by undesirable artifacts when metallic implants are carried by patients, which creates the problem of metal artifact reduction (MAR). Existing methods for reducing the artifacts due to metallic implants are inadequate for two main reasons. First, metal artifacts are structured and non-local so that simple image domain enhancement approaches would not suffice. Second, the MAR approaches which attempt to reduce metal artifacts in the X-ray projection (sinogram) domain inevitably lead to severe secondary artifact due to sinogram inconsistency. To overcome these difficulties, we propose an end-to-end trainable Dual Domain Network (DuDoNet) to simultaneously restore sinogram consistency and enhance CT images. The linkage between the sigogram and image domains is a novel Radon inversion layer that allows the gradients to back-propagate from the image domain to the sinogram domain during training. Extensive experiments show that our method achieves significant improvements over other single domain MAR approaches. To the best of our knowledge, it is the first end-to-end dual-domain network for MAR.

This paper presents an approach to predict the deformation of the lungs and surrounding organs during respiration. The framework incorporates a computational model of the respiratory system, which comprises an anatomical model extracted from computed tomography (CT) images at end-expiration (EE), and a biomechanical model of the respiratory physiology, including the material behavior and interactions between organs. A personalization step is performed to automatically estimate patient-specific thoracic pressure, which drives the biomechanical model. The zone-wise pressure values are obtained by using a trust-region optimizer, where the estimated motion is compared to CT images at end-inspiration (EI). A detailed convergence analysis in terms of mesh resolution, time stepping and number of pressure zones on the surface of the thoracic cavity is carried out. The method is then tested on five public datasets. Results show that the model is able to predict the respiratory motion with an average landmark error of $3.40 \pm 1.0$ mm over the entire respiratory cycle. The estimated 3-D lung motion may constitute as an advanced 3-D surrogate for more accurate medical image reconstruction and patient respiratory analysis.

Invasive cardiac angiography (catheterization) is still the standard in clinical practice for diagnosing coronary artery disease (CAD) but it involves a high amount of risk and cost. New generations of CT scanners can acquire high-quality images of coronary arteries which allow for an accurate identification and delineation of stenoses. Recently, computational fluid dynamics (CFD) simulation has been applied to coronary blood flow using geometric lumen models extracted from CT angiography (CTA). The computed pressure drop at stenoses proved to be indicative for ischemia-causing lesions, leading to non-invasive fractional flow reserve (FFR) derived from CTA. Since the diagnostic value of non-invasive procedures for diagnosing CAD relies on an accurate extraction of the lumen, a precise segmentation of the coronary arteries is crucial. As manual segmentation is tedious, time-consuming and subjective, automatic procedures are desirable. We present a novel fully-automatic method to accurately segment the lumen of coronary arteries in the presence of calcified and non-calcified plaque. Our segmentation framework is based on three main steps: boundary detection, calcium exclusion and surface optimization. A learning-based boundary detector enables a robust lumen contour detection via dense ray-casting. The exclusion of calcified plaque is assured through a novel calcium exclusion technique which allows us to accurately capture stenoses of diseased arteries. The boundary detection results are incorporated into a closed set formulation whose minimization yields an optimized lumen surface. On standardized tests with clinical data, a segmentation accuracy is achieved which is comparable to clinical experts and superior to current automatic methods.

The diversity in appearance of diseased lung tissue makes automatic segmentation of lungs from CT with severe pathologies challenging. To overcome this challenge, we rely on contextual constraints from neighboring anatomies to detect and segment lung tissue across a variety of pathologies. We propose an algorithm that combines statistical learning with these anatomical constraints to seek a segmentation of the lung consistent with adjacent structures, such as the heart, liver, spleen, and ribs. We demonstrate that our algorithm reduces the number of failed detections and increases the accuracy of the segmentation on unseen test cases with severe pathologies.

Automatic segmentation of lung tissue in thoracic CT scans is useful for diagnosis and treatment planning of pulmonary diseases. Unlike healthy lung tissue that is easily identifiable in CT scans, diseased lung parenchyma is hard to segment automatically due to its higher attenuation, inhomogeneous appearance, and inconsistent texture. We overcome these challenges through a multi-layer machine learning approach that exploits geometric structures both within and outside the lung (e.g., ribs, spine). In the coarsest layer, a set of stable landmarks on the surface of the lung are detected through a hierarchical detection network (HDN) that is trained on hundreds of annotated CT volumes. These landmarks are used to robustly initialize a coarse statistical model of the lung shape. Subsequently, a region-dependent boundary refinement uses a discriminative appearance classifier to refine the surface, and finally a region-driven level set refinement is used to extract the fine-scale detail. Through this approach we demonstrate robustness to a variety of lung pathologies.

During radiotherapy of lung tumors, the respiratory motion must be tracked to reduce radiation of healthy tissue. This is usually done by using a respiratory surrogate, but with limited accuracy. We investigate how patient-specific finite element models (FEM) of respiratory mechanics can predict the motion of the lungs. First, the anatomical models of the lungs and thorax are extracted from CT images automatically. Then, a biomechanical model is used to simulate the respiratory motion based on a novel thorax/lung interaction force that simulates the pleural cavity. Our model is not driven by image forces but by thoracic pressures personalized using a multivariate optimizer. The proposed model is validated on three DIR-Lab datasets, yielding a promising internal landmark error of 3.33 ± 0.60 mm. Our model may represent a tool for lung deformation prediction and therapy guidance.

Ultrasonography is a valuable technique for diagnosing breast cancer. Computer-aided tumor detection and segmentation in ultrasound images can reduce labor cost and streamline clinic workflows. In this paper, we propose a fully automatic system to detect and segment breast tumors in 2D ultrasound images. Our system, based on database-guided techniques, learns the knowledge of breast tumor appearance exemplified by expert annotations. For tumor detection, we train a classifier to discriminate between tumors and their background. For tumor segmentation, we propose a discriminative graph cut approach, where both the data fidelity and compatibility functions are learned discriminatively. The performance of the proposed algorithms is demonstrated on a large set of 347 images, achieving a mean contour-to-contour error of 3.75 pixels with about 4.33 seconds.

Distinguishing cysts from other tumors is a routine clinical practice for diagnosing breast cancer. It has shown that more accurate diagnosis can be achieved by combining elasticity images with traditional B-mode ultrasound images [1]. In this paper, we propose a fully automatic system to detect cysts jointly in both B-mode and elasticity images. It is based on database-guided techniques that learn the knowledge of cyst appearance automatically from B-mode and elasticity images in a database. Further, for a detected cyst in a query image, the cysts with similar image appearance in the database are retrieved to improve diagnostic accuracy and confidence. In the experiment, we show that our system achieves high sensitivity and specificity in cyst diagnosis.

With Kohonen's self-organizing map based brain MRI image segmentation, there are still some regions which are not partitioned accurately, particularly in the transitional regions of gray matter and white matter, or cerebrospinal fluid and gray matter. In this paper, we propose a dynamically growing hierarchical self-organizing map integrated with a multi-scale feature vector to overcome the problem mentioned above, which uses the spatial relationships between image pixels and using multi-scale processing method to reduce the noise effect and the classification ambiguity. The efficacy of our approach is validated by extensive experiments using both simulated and real MRI images.

This paper presents an integrated method for adaptive segmentation of brain tissues in three-dimensional (3-D) MRI (Magnetic Resonance Imaging) images. The method intends to do the volume segmentation in a slice-by-slice manner. Firstly, some slices in the volume are segmented using an automatic algorithm composed of watershed, fuzzy clustering (Fuzzy C-Means) and re-segmentation. Then their adjacent slices can be segmented conveniently by propagating the information of them. The information is consisted of watershed lines and thresholds obtained from the re-segmentation approach. This integrated approach yields a robust and precise segmentation. The efficacy of the proposed algorithm is validated using extensive experiments.

This paper presents an integrated algorithm for MRI (Magnetic Resonance Imaging) brain tissues segmentation. The method is composed of four stages. Noise in the MRI images is first reduced by a versatile wavelet-based filter. Then, the watershed algorithm is applied to brain tissues as an initial segmenting method. Because the result of classical watershed algorithm on grey-scale textured images such as tissue images is over-segmentation. The third stage is a merging process for the over-segmentation regions using fuzzy clustering algorithm (Fuzzy C-Means). But there are still some regions which are not divided completely due to the low contrast in them, particularly in the transitional regions of gray matter and white matter, or cerebrospinal fluid and gray matter. We exploited a method base on Minimum Covariance Determinant (MCD) estimator to detect the regions needed segmentation again, and then partition them by a supervised k-Nearest Neighbor (kNN) classifier. This integrated approach yields a robust and precise segmentation. The efficacy of the proposed algorithm is validated using extensive experiments.

An integrated method using the adaptive segmentation of brain tissues in Magnetic Resonance Imaging (MRI) images is proposed in this paper. Firstly, we give a template of brain to remove the extra-cranial tissues. Subsequently, watershed algorithm is applied to brain tissues as an initial segmenting method. Normally, result of classical watershed algorithm on gray-scale textured images such as tissue images is over-segmentation. The following procedure is a merging process for the over-segmentation regions using fuzzy clustering algorithm (Fuzzy C-Means). But there are still some regions which are not partitioned completely, particularly in the transitional regions between gray matter and white matter. So we proposed a rule-based re-segmentation processing approach to partition these regions. This integrated scheme yields a robust and precise segmentation. The efficacy of the proposed algorithm is validated using extensive experiments.

ALFIA (Automated Lymphatic Function Imaging Analysis), an algorithm providing quantitative analysis of investigational near-infrared fluorescence lymphatic images, is described. Images from nine human subjects were analyzed for apparent lymphatic propagation velocities and propulsion periods using manual analysis and ALFIA. While lymphatic propulsion was more easily detected using ALFIA than with manual analysis, statistical analyses indicate no significant difference in the apparent lymphatic velocities although ALFIA tended to calculate longer propulsion periods. With the base ALFIA algorithms validated, further automation can now proceed to provide a clinically relevant analytic tool for quantitatively assessing lymphatic function in humans.