COVID-19 Instance Segmentation Network Based on Semantic Information Generating Proposals

The CT images of lungs of COVID-19 patients have distinct pathological features, segmenting the lesion area accurately by the method of deep learning, which is of great significance for the diagnosis and treatment of COVID-19 patients. Instance segmentation has higher sensitivity and can output the Bounding Boxes of the lesion region, however, the traditional instance segmentation method is weak in the segmentation of small lesions, and there is still room for improvement in the segmentation accuracy. We propose a instance segmentation network which is called as Semantic R-CNN. Firstly, a semantic segmentation branch is added on the basis of Mask-RCNN, and utilizing the image processing tool Skimage in Python to label the connected domain for the result of semantic segmentation, extracting the rectangular boundaries of connected domain and using them as Proposals, which will replace the Regional Proposal Network in the instance segmentation. Secondly, the Atrous Spatial Pyramid Pooling is introduced into the Feature Pyramid Network, then improving the feature fusion method in FPN. Finally, the cascade method is introduced into the detection branch of the network to optimize the Proposals. Segmentation experiments were carried out on the pathological lesion segmentation data set of CC-CCII, the average accuracy of the semantic segmentation is 40.56mAP, and compared with the Mask-RCNN, it has improved by 9.98mAP. After fusing the results of semantic segmentation and instance segmentation, the Dice coefficient is 80.7%, the sensitivity is 85.8%, and compared with the Inf-Net, it has increased by 1.6% and 8.06% respectively. The proposed network has improved the segmentation accuracy and reduced the false-negatives. © 2022 ACM.

Computer-Aided Diagnosis of COVID-19 with Joint Instance Segmentation and Classification

To address the difficulty of small lesion area detection of COVID-19 patients in their lung CT images, the author has proposed an end-to-end network which using semantic segmentation to guide instance segmentation, and extending transfer learning to the classification of COVID-19 pneumonia, Common pneumonia and Normal. Firstly, in order to extract richer multi-scale features and increase the weight of low-level features, we have introduced the Atrous Spatial Pyramid Pooling(ASPP) into the Feature Pyramid Network(FPN), and proposed Multi-scale Reverse Attention Feature Pyramid Network, then having added a semantic segmentation branch to guide instance segmentation after the output of ASPP, finally, we have extracted the object category score by detector for auxiliary classification. Segmentation experiments were carried out on the dataset of CC-CCII and COVID-19 infection segmentation dataset, the mean average precision(mAP) is 39.57%, 35.36%, Compared with the COVID-CT-Mask-Net, it has improved by 5.52%, 2.33%, we also carried out classification experiments on the dataset that is from COVIDX-CT, the sensitivity and specificity of the model for detecting COVID-19 in test data are 95.88% and 98.95% respectively. Also, the sensitivity and specificity of the model for detecting Common pneumonia in test data are 98.62% and 99.25% respectively, the sensitivity and specificity of the model for detecting Normal in test data are 99.61% and 99.11% respectively, which are the best results based on this dataset and indicators, this shows that the proposed method can quickly and effectively help the clinician identify and diagnose COVID-19 patient through their lung CT images. © 2022 IEEE.

Ambient fine particulate matter exposures and oxidative protein damage in early pregnant women

The association between oxidative protein damage in early pregnant women and ambient fine particulate matter (PM2.5) is unknown. We estimated the effect of PM2.5 exposures within seven days before blood collection on serum 3-nitrotyrosine (3-NT) and advanced oxidation protein products (AOPP) in 100 women with normal early pregnancy (NEP) and 100 women with clinically recognized early pregnancy loss (CREPL). Temporally-adjusted land use regression model was applied for estimation of maternal daily PM2.5 exposure. Daily nitrogen dioxide (NO2) exposure of each participant was estimated using city-level concentrations of NO2. Single-day lag effect of PM2.5 was analyzed using multivariable linear regression model. Net cumulative effect and distributed lag effect of PM2.5 and NO2 within seven days were analyzed using distributed lag non-linear model. In all 200 subjects, the serum 3-NT were significantly increased with the single-day lag effects (4.72%–8.04% increased at lag 0–2), distributed lag effects (2.32%–3.49% increased at lag 0–2), and cumulative effect within seven days (16.91% increased). The single-day lag effects (7.41%–10.48% increased at lag 0–1), distributed lag effects (3.42%–5.52% increased at lag 0–2), and cumulative effect within seven days (24.51% increased) of PM2.5 significantly increased serum 3-NT in CREPL group but not in NEP group. The distributed lag effects (2.62%–4.54% increased at lag 0–2) and cumulative effect within seven days (20.25% increased) of PM2.5 significantly increased serum AOPP in early pregnant women before the coronavirus disease (COVID-19) pandemic but not after that, similarly to the effects of NO2 exposures. In conclusion, PM2.5 exposures were associated with oxidative stress to protein in pregnant women in the first trimester, especially in CREPL women. Analysis of NO2 exposures suggested that combustion PM2.5 was the crucial PM2.5 component. Wearing masks may be potentially preventive in PM2.5 exposure and its related oxidative protein damage. © 2022 Elsevier Ltd

Evolution Characteristics and Influencing Factors of Global Dairy Trade

There are imbalances and uncertainties in the global supply and demand of dairy products, owing to the adverse influence of overall economic changes, dairy prices, agricultural politics, the COVID-19 pandemic, and severe climate. This paper aims to explore the evolving characteristics and influencing factors of the global dairy trade pattern and make recommendations for the sustainable development of the global dairy trade. This paper studies the evolutionary characteristics of the global dairy trade pattern from the perspective of the overall structure, individual characteristics, and core–periphery structure through complex network analysis (CNA), using the countries involved in dairy trade from 2000 to 2020. Furthermore, this study explores the influencing factors of the dairy trade network using a quadratic allocation procedure (QAP). The results indicate that the global dairy trade network has been expanding, with prominent scale-free features and small-world characteristics. Individual countries display obvious heterogeneity, whereas the core import regions of the dairy shift from Europe, East Asia, and America to North America, the Middle East, and East Asia. Contrary to this, there is no significant change in the core export regions. Consequently, the entire dairy trade network represents a clear core–periphery structure. Moreover, the income per capita gaps, geographic distance gaps, and common language always affect the trade value and dairy trade relations across the countries. Meanwhile, economic level gaps and regional trade agreements have become increasingly significant. Thus, the dairy trade may not follow the "border effect”. Lastly, this paper also extends recommendations for the sustainable development of the dairy trade. © 2023 by the authors.

A Joint Feature Aggregation Method for Robust Masked Face Recognition

Masked face recognition becomes an important issue of prevention and monitor in outbreak of COVID-19. Due to loss of facial features caused by masks, unmasked face recognition could not identify the specific person well. Current masked faces methods focus on local features from the unmasked regions or recover masked faces to fit standard face recognition models. These methods only focus on partial information of faces thus these features are not robust enough to deal with complex situations. To solve this problem, we propose a joint feature aggregation method for robust masked face recognition. Firstly, we design a multi-module feature extraction network to extract different features, including local module (LM), global module (GM), and recovery module (RM). Our method not only extracts global features from the original masked faces but also extracts local features from the unmasked area since it is a discriminative part of masked faces. Specially, we utilize a pretrained recovery model to recover masked faces and get some recovery features from the recovered faces. Finally, features from three modules are aggregated as a joint feature of masked faces. The joint feature enhances the feature representation of masked faces thus it is more discriminative and robust than that in previous methods. Experiments show that our method can achieve better performance than previous methods on LFW dataset. © 2022 SPIE.

Pharmacological Activity and Applied Research of Heparin and its Derivatives

Heparin is a kind of glycosaminoglycan drug with a complex structure, which is a mixture of polysaccharides with different chain lengths composed of hexuronic acid, aminohexose and its derivatives.Hexuronic acids are L-aduronic acid and D-glucuronic acid, aminohexose is α -D-glucosamine, and the modification of derivatives includes sulfation and acetylation.As a natural biomacromolecule, heparin has a variety of biological activities.It has been discovered for more than a hundred years and has good anticoagulant effect, which is clinically the first choice for anticoagulant and prevention and treatment of thromboembolic diseases.It has been discovered that there are more than one hundred functional proteins that interact with heparin.Heparin can bind to a variety of proteins and exert a variety of biological activities such as anticoagulant, anti-inflammatory, antiviral, and anti-tumor. The anticoagulant mechanism of heparin has been explained in detail, but its anti-inflammatory, antiviral, anti-tumor and other non-anticoagulant biolo-gical activities are still under extensive research, and these activities also have the potential to be developed into new drugs and new materials.Derivatives which with low anticoagulant activity and high antiangiogenic activity have been developed.In addition, sepsis-induced coagulopathy was common in patients with severe pneumonia caused by COVID-19 during the global outbreak of novel coronavirus epidemic.Heparin is effective in improving coagulation disorders and is likely to provide a better prognosis in patients with severe pneumonia.Due to its better biological activity, it also has potential applications in the field of new materials, such as being a cross-linking agent in the formation of hydrogels, and as a surface modifier of nanoparticles. This article consists of five parts, through which the author will first review the pharmacological activities of heparin in anticoagulation, anti-inflammatory and anti-tumor activities, then introduce the application of heparin in the new coronavirus, and finally give an overview of the application of heparin in new materials.

Multiscale codec network based CT image segmentation for human lung disease derived of COVID-19

Objective: The corona virus disease 2019 (COVID-19), also known as severe acute respiratory syndrome coronavirus (SARS-CoV-2), has rapidly spread throughout the world as a result of the increased mobility of populations in a globalized world, wreaking havoc on people's daily lives, the global economy, and the global healthcare system. The novelty and dissemination speed of COVID-19 compelled researchers around the world to move quickly, using all resources and capabilities to analyse and characterize the novel coronavirus in terms of transmission routes and viral latency. Early and effective screening of COVID-19 patients and corresponding medical treatment, care and isolation to cut off the transmission route of the novel coronavirus are the key to prevent the spread of the epidemic. Due to the rapid infection of COVID-19, it is very important to screen COVID-19 threats based on precise segmenting lesions in lung CT images, which can be a low cost and quick response method nowadays. Rapid and accurate segmentation of coronavirus pneumonia CT images is of great significance for auxiliary diagnosis and patient monitoring. Currently, the main method for COVID-19 screening is the reverse transcription polymerase chain reaction like reverse transcription-polymerase chain reaction(RT-PCR) analysis. But, RT-PCR is time consuming to provide the diagnosis results, and the false negative rate is relatively high. Another effective method for COVID-19 screening is computed tomography (CT) technology. The CT scanning technology has high sensitivity and enhanced three-dimensional representation of infection visualization. Computed tomography (CT) has been used as an important method for the diagnosis and treatment of patients with COVID-19, the chest CT images of patients with COVID-19 mostly show multifocal, patchy, peripheral distribution, and ground glass opacity (GGO) which is mostly seen in the lower lobes of both lungs;a high degree of suspicion for novel coronavirus's infection can be obtained if more GGO than consolidation is found on CT images;therefore, detection of GGO in CT slices regions can provide clinicians with important information and help in the fight against COVID-19. The current analysis of COVID-19 pneumonia lesions has low segmentation accuracy and insufficient attention to false negatives. Method: Our accurate segmentation model based on small data set. In view of the complexity and variability of the targeted area of COVID-19 pneumonia, we improved Inf-Net and proposed a multi-scale encoding and decoding network (MED-Net) based on deep learning method. The computational cost may be caused by multi-scale encoding and decoding. The network extends the encoder-decoder structure in FC-Net, in which the decoder part is on the left column;The middle column is atrous spatial pyramid pooling (ASPP) structure;The right column is a multi-scale parallel decoder which is based on the improvement of parallel partial decoder. In this network structure, HarDNet68 is adopted as the backbone in terms of high resource utilization and fast computing speed, which can be as a simplified version of DenseNet, reduces DenseNet based hierarchical connections to get cascade loss deduction. HardNet68 is mainly composed of five harmonious dense blocks (HDB). Based on 5 different scales, We extract multiscale features from the first convolution layer and the 5 HDB sequential steps of HarDNet68 via a five atrous spatial pyramid pooling (ASPP). Meanwhile, as a new decoding component, a multiscale parallel partial decoder (MPPD) is based on the parallel decoder (PPD), which can aggregate the features between different levels in parallel. By decoding the branches of three different receptive fields, we have dealt with information loss issues in the encoder part and the difficulty of small lesions segmentation. Our deep supervision mechanism has melted the multi-scale decoder into the true positive and true negative samples analyses, for improving the sensitivity of the model. Result: Current COVID-19 CT Segmentation provides compl ted segmentation labels as a small data set. This research is improved based on Inf-Net, and the model structure is simple, the edge attention module(EA) is not introduced, and the reverse attention module(RA) is not quoted, only one MPPD is used to optimize the network stricture. The quantitative results show that MED-Net can effectively cope with the problems of fewer samples in the small dataset, the texture, size and position of the segmentation target vary greatly. On the data set with only 50 training images and 50 test images, the Dice coefficient is 73.8%, the sensitivity is 77.7%, and the specificity is 94.3%. Compared with the previous work, it has increased by 8.21%, 12.28% and 7.76% respectively. Among them, Dice coefficient and sensitivity have reached the most advanced level based on the same division mode of this data set. Simultaneously the qualitative results address that the segmentation result of the proposed model is closer to ground-truth in this experiment. We also conducted ablation experiments, that the use of MPPD has obvious effects to deal with small lesions area segmentation and improving segmentation accuracy. Conclusion: Our analysis shows that the proposed method can effectively improve the segmentation accuracy of the lesions in the CT images of the COVID-19 derived lungs disease. Our segmentation accuracy of MED-Net is qualified. The quantitative and qualitative results demonstrate that MED-Net is relatively effective in controlling edges and details, which can capture rich context information, and improve sensitivity. MED-Net can also effectively resolve the small lesions segmentation issue. For COVID-19 CT Segmentation data set, it has several of qualified evaluation indicators based on end-to-end learning. The potential of automatic segmentation of COVID-19 pneumonia is further facilitated. © 2022, Editorial Office of Journal of Image and Graphics. All right reserved.

What Should Teachers Do? Visibility of Faculty and TA Support across Remote and Traditional Learning

The COVID-19 pandemic has altered best practices for instructors and teaching assistants (TAs) to support student learning in engineering. This does not necessarily mean that instructional support has diminished as a consequence of the transition to remote learning. In this study, instructional support was explored using quantitative and qualitative methods of data analysis. Surveys from over 600 students in sophomore and junior level courses in engineering at a large public institution were collected in the Spring of 2020 and compared to results from similar courses offered prior to the start of the COVID-19 crisis. Likert-scale items, as well as short answer items, that independently measured faculty support and TA support were analyzed in this study. Initial t-tests indicated that perceptions of faculty support were not significantly different between remote and traditional learning. To consider the possibility that failure to reject the null hypothesis was due to course-by-course variations, additional t-tests were used to compare student perceptions of faculty support across pairs of courses taught in both settings. Post-hoc tests showed that faculty support was significantly higher in the remote learning setting in three of seven pairs of courses and significantly lower in the remote learning setting in the four remaining courses (p < 0.05). Similarly, in considering TA support, an initial t-test indicated that perceptions of TA support were not significantly different in remote learning compared to traditional learning, but in course-by-course comparisons, students believed they were offered significantly higher TA support in remote learning in three pairs of classes and significantly lower TA support in one pair of classes (p < 0.05) with three classes indicating no significant difference. Students in both settings were also asked to identify one thing that faculty could do and one thing that TAs could do to better support their learning. Inductive coding of these short answer responses revealed that while in traditional learning, students emphasized faculty support in in-class and out of class delivery of materials, in remote learning, the emphasis shifted to needs for support in out of class delivery and out of class interactions. For TAs, student expectations were balanced between in-class delivery and out-of-class interactions in traditional learning but their needs for more out of class interactions dominated their concerns in remote learning. Overall, for faculty, about 20% of students requested greater availability in both remote and in-person settings. For TAs, 44% of students requested greater availability of and access to their TAs in remote learning, compared to 18% in in-person settings. The analysis of both Likert-scale and short answer data regarding TA and faculty support in this study reinforces the importance of availability of instructional support regardless of setting. As students, TAs, and faculty continue to navigate the uncharted waters of the traditional college education system gone online, the nature of connection differs yet its importance remains the same. © American Society for Engineering Education, 2021

Differences in Perceptions of Instructional Support between U.S. and International Students Before and During COVID-19

The COVID-19 public health crisis has influenced the way American higher education institutions operate and support student success. As a result of the crisis, institutions that traditionally provided in-person instruction abruptly moved to a virtual space with little preparation time in the spring of 2020. Considering the critical roles that both faculty and teaching assistants (TAs) play in student learning and engagement, this study explored the contribution that this abrupt transition to remote learning made in international students' perceptions of faculty and TA support, and positive emotional engagement, compared to U.S. students. Data collected from surveys in in-person settings prior to COVID-19 and in spring of 2020 immediately after COVID-19 impacted the delivery of higher education (N = 1,212) were used to study if and how the remote setting influenced international student perceptions of faculty and TA support and positive emotional engagement. The pre-COVID surveys were collected from students enrolled in sophomore and junior-level engineering courses prior to 2020, and the remaining surveys were collected from students enrolled in remote learning courses in the spring of 2020. Seven of the courses were the same in both the remote and in-person survey populations, and the remaining five courses were similar (in mechanical or electrical engineering and involving significant TA support). The data were analyzed cross-sectionally using hierarchical linear models. All models considered demographics (gender and citizenship status), behavioral engagement, and emotional engagement variables. The study found that international students' perceived level of faculty support was more sensitive to their level of self-efficacy than that of their U.S. peers. International students' perceptions of TA support were also found to be generally higher than that of U.S. students. Finally, international students' positive emotional engagement was higher than that of U.S. peers, more sensitive to participation, and less sensitive to perceptions of TA support. Faculty and TA support are both important to student learning and this is particularly true for international students. Contrary to the perception that remote learning is substandard compared to traditional learning, this study suggests that students overall felt that the instructional team provided adequate support during the COVID-19 crisis. This study was not able to explain whether this effect will “wear off” as remote learning continues, and students become less charitable in their assessments. Although this data was collected from only a single institution, it suggests that what engineering faculty and TAs did in the first term of remote learning worked;and carrying forward those practices into future remote instruction and instruction beyond the COVID-19 pandemic may be recommended. © American Society for Engineering Education, 2021

Integrated innovation of Chinese and western medicine: Target-combined Holistic Treatment

With the development of biomedicine, and significant success achieved by targeted therapy in the treatment of complex and refractory diseases such as cancers, clinical treatment and drug discovery based on molecular targets become the main direction of modern medical research. Guided by holistic concept, traditional Chinese medicine (TCM) has a unique advantage with regards to the prevention and treatment of complex diseases through its comprehensive effect. However, at present, TCM research is insufficient and problems such as inaccurate clinical findings, lack of dominant diseases, unclear evaluation of efficacy, and unclear mechanisms, cannot be solved easily. Therefore, the authors believe that the development of TCM should adopt an innovative approach that integrates the advantages of TCM and western medicine, and positively influences the development of TCM and even future medicine. This article proposes Target-combined Holistic Treatment (THT), a new concept based on the integration of TCM and western medicine. While being guided by the holistic and systematic perspective of TCM, THT also factors in the latest advances in both TCM and modern biomedical research, and uses macro-micro combination, target-syndrome combination, and internal and external codivision, as the primary strategies to establish a clinical diagnosis model that combines disease differentiation, syndrome differentiation, and target differentiation. It also uses drug ingredients/components to enable comprehensive interventions against multiple targets linked to a disease, and establishes target medication and research strategy of TCM, so as to improve its pertinence, efficacy, and safety, and ensure precise and effective medication. The theories and methods of THT fully integrate the advantages of TCM and western medicine, factoring in both macro-control and micro intervention, and also systematic confrontation, precision treatment, and embracing TCM theory and modern technology. This can guide the further development of TCM, western medicine, as well as the combination of TCM and western medicine. It will therefore help promote the integration and innovation of both TCM and western medicine, and the in-depth development of TCM. The theories and methods of THT can be used to guide not only the whole process of clinical diagnosis but also the development of innovative drugs. THT has been used to guide the formulation of TCM diagnosis and treatment for COVID-19 and has shown remarkable results. We have developed a Chinese herbal prescription "Keguan-1", to prevent and treat COVID-19 based on the theory and method of THT, and use the doctrine of "resist foreign aggression and pacify the interior" of TCM against COVID-19. Using the findings of research in modern medicine, THT can innovate and develop TCM based treatments, and thus aid in the treatment of various diseases, and also help promote the integration and development of TCM and western medicine. © 2021, Science Press. All right reserved.

A food system revolution for China in the post-pandemic world

The COVID-19 pandemic is worsening food shortages in food deficit countries, such as China, which rely on import for domestic food consumption. We argue that fundamental revolution in China's livestock system can meet about 50% of its consumption of livestock products and thereby reduce the country's reliance on imports. Three food system revolutions that can greatly reduce China's reliance on imports are technically and economically feasible, and generate high eco-system benefits: (1) organic or inorganic based microbial feed protein production to substitute imported feed protein, (2) vegetation greening and fodder production through grassland restoration to reduce import of ruminant animal products, and (3) insect protein based fish-plant production and offshore marine restoration to replace red meat consumption and increase recycling of manure. Together these revolutions can accelerate progress towards multiple Sustainable Development Goals in exporting countries. © 2021

Epidemiological characteristics of novel coronavirus confirmed cases and asymptomatic infected patients in Guangzhou

Objective: To analyze the epidemiological characteristics of novel coronavirus positive cases including confirmed cases with clinical symptoms and asymptomatic infected cases in Guangzhou. Methods: Epidemiological data were collected on the nucleic acid positive cases of COVID-19 in Guangzhou from January to September 2020. The epidemiological characteristics, the distribution of time intervals between the confirmed/isolation date and the date of the first positive detection were analyzed, at last the influencing factors for the confirmed cases and asymptomatic infected persons were discussed. Results: From January 7 to September 4 in 2020, a total of 1 097 nucleic acid positive cases were identified, including 658 confirmed cases (59.98%) and 439 asymptomatic infected cases (40.02%). Among the 658 confirmed cases, the median age was 42 years old, the cases indicated two significant peaks. one of the peaks was related to the imported and associated cases from Hubei province, and the other peak was connected with individuals from overseas. In terms of 439 asymptomatic infected cases, the median age was 32 years old. There were two stages in these cases. The first stage followed the second peak of confirmed cases, and the second stage overlapped with the confirmed cases in Guangzhou when the epidemic was in a period of normal prevention and control, mainly related to imported cases from abroad. The asymptomatic infected persons accounted for 57.32% in all the imported infected cases. In both of asymptomatic and symptomatic cases, the positive rate of pharyngeal swabs was higher than that of nasopharyngeal swabs and anal swabs. There were statistically significant differences in age, source of infection and gender composition between confirmed cases and asymptomatic infected persons (P<0.05). Older age groups were more likely to have clinical symptoms, with ≥40 years being the risk factor for confirmed cases (OR=2.334, P=0.001), and 20-39 years less likely to have clinical symptoms (OR=0.620, P=0.047), compared with the 0-19 years old group. Compared with those infected in China, those infected abroad were less likely to develop clinical symptoms and became confirmed cases (OR=0.723, P=0.013). Women were more likely to have clinical symptoms than men (OR=1.574, P=0.001). Conclusions: At present, asymptomatic infected persons and confirmed patients with clinical symptoms co-existed, and the number of asymptomatic infected patients was higher than that of confirmed cases in Guangzhou. High age, domestic infection and female may be risk factors for confirmed cases. It was of great value to further explore these underlying mechanisms for the prevention and treatment of the COVID-19.

The Practice and Exploration of the Pharmacy Counseling Services on Internet-based Medicine Model during the Period of Coronavirus Disease 2019

OBJECTIVE: To explore the novel models of internet-based pharmacy services during the period of coronavirus disease 2019 (COVID-19). METHODS: The process specification, management system, qualification and quality control were discussed and astablished. Retrieving 2-month of internet pharmacy counseling records, and conducting statistical analysis on the relevant information, which includes the counseling content, drug types, numbers, cases and patient evaluation survey. RESULTS: This new model built an internet pharmacy counseling platform in Peking Union Medical College Hospital (PUMCH) on March 2020. Relevant systems and standardized processes has be established. After the implementation of the internet-specific pharmacy counseling service, a total of 615 qualified counseling records were collected by April 12, 2020. The number of online pharmacists, the numbers of counseling, daily pharmacist visits have increased continuously. CONCLUSION: In conclusion, the implementation of internet pharmacy counseling services during the period of coronavirus disease 2019 (COVID-19) solves the drug-related demands from patients as well as reduces the potential risk of cross-infections among patients. This model contributes to the establishment of a series of new pharmacy services to fight against the COVID-19. In addition, it also plays an important role in helping specialist pharmacists provide convenient, patient-centered pharmacy services.