Survival rate and potential risk indicators of implant loss in non-smokers and systemically healthy periodontitis patients: An up to 9-year retrospective study.

BACKGROUND: An increasing number of patients with chronic periodontitis (CP) have received implant restoration. However, very few studies have evaluated the probable risk indicators of implant loss in patients with CP. OBJECTIVE: The aim of this study is to evaluate implant long-term survival rates in patients with CP. The results are analyzed to discern potential risk indicators of implant loss. METHODS: A total of 1549 implants were inserted in 827 non-smokers and systemically healthy CP patients between March 2011 and March 2019. Clinical variables (age; sex; implant location; implant diameter; implant length; implant type; bone quality; bone graft, periodontal disease status, and insertion torque) were recorded. Kaplan-Meier survival curves illustrated the cumulative survival rate. The relationship between variables and implant loss was discerned by univariate analysis. Further multivariate Cox proportional hazard regression analysis was carried out for the variables with P < 0.2. RESULTS: The cumulative survival rates were 98.8% after 3 months, 97.9% after 6 months, 97.7% after 1 year, and 97.4% after 2 to 9 years. After adjusting possible confounders, the multivariable Cox regression model revealed statistically significant influences of implant location, history of bone graft, and insertion torque on implant loss. Implants with history of bone graft were more likely to loss. Implants inserted in the anterior area had a higher implant loss risk; insertion torque of <15 Newton-centimeter (Ncm) showed a relatively high risk of being lost. CONCLUSIONS: The study represented public hospital insight into long-term implant results of patients with CP. Under the premise of strict periodontal control, patients with the history of CP exhibited relatively high implant survival rate. Anterior implant location, history of bone graft, and insertion torque <15 Ncm are associated with a lower implant survival rate and could be considered at a higher risk of implant failure in patients with CP.

HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.

BACKGROUND: In precision medicine, deep phenotyping is defined as the precise and comprehensive analysis of phenotypic abnormalities, aiming to acquire a better understanding of the natural history of a disease and its genotype-phenotype associations. Detecting phenotypic relevance is an important task when translating precision medicine into clinical practice, especially for patient stratification tasks based on deep phenotyping. In our previous work, we developed node embeddings for the Human Phenotype Ontology (HPO) to assist in phenotypic relevance measurement incorporating distributed semantic representations. However, the derived HPO embeddings hold only distributed representations for IS-A relationships among nodes, hampering the ability to fully explore the graph. METHODS: In this study, we developed a framework, HPO2Vec+, to enrich the produced HPO embeddings with heterogeneous knowledge resources (i.e., DECIPHER, OMIM, and Orphanet) for detecting phenotypic relevance. Specifically, we parsed disease-phenotype associations contained in these three resources to enrich non-inheritance relationships among phenotypic nodes in the HPO. To generate node embeddings for the HPO, node2vec was applied to perform node sampling on the enriched HPO graphs based on random walk followed by feature learning over the sampled nodes to generate enriched node embeddings. Four HPO embeddings were generated based on different graph structures, which we hereafter label as HPOEmb-Original, HPOEmb-DECIPHER, HPOEmb-OMIM, and HPOEmb-Orphanet. We evaluated the derived embeddings quantitatively through an HPO link prediction task with four edge embeddings operations and six machine learning algorithms. The resulting best embeddings were then evaluated for patient stratification of 10 rare diseases using electronic health records (EHR) collected at Mayo Clinic. We assessed our framework qualitatively by visualizing phenotypic clusters and conducting a use case study on primary hyperoxaluria (PH), a rare disease, on the task of inferring relevant phenotypes given 22 annotated PH related phenotypes. RESULTS: The quantitative link prediction task shows that HPOEmb-Orphanet achieved an optimal AUROC of 0.92 and an average precision of 0.94. In addition, HPOEmb-Orphanet achieved an optimal F1 score of 0.86. The quantitative patient similarity measurement task indicates that HPOEmb-Orphanet achieved the highest average detection rate for similar patients over 10 rare diseases and performed better than other similarity measures implemented by an existing tool, HPOSim, especially for pairwise patients with fewer shared common phenotypes. The qualitative evaluation shows that the enriched HPO embeddings are generally able to detect relationships among nodes with fine granularity and HPOEmb-Orphanet is particularly good at associating phenotypes across different disease systems. For the use case of detecting relevant phenotypic characterizations for given PH related phenotypes, HPOEmb-Orphanet outperformed the other three HPO embeddings by achieving the highest average P@5 of 0.81 and the highest P@10 of 0.79. Compared to seven conventional similarity measurements provided by HPOSim, HPOEmb-Orphanet is able to detect more relevant phenotypic pairs, especially for pairs not in inheritance relationships. CONCLUSION: We drew the following conclusions based on the evaluation results. First, with additional non-inheritance edges, enriched HPO embeddings can detect more associations between fine granularity phenotypic nodes regardless of their topological structures in the HPO graph. Second, HPOEmb-Orphanet not only can achieve the optimal performance through link prediction and patient stratification based on phenotypic similarity, but is also able to detect relevant phenotypes closer to domain expert's judgments than other embeddings and conventional similarity measurements. Third, incorporating heterogeneous knowledge resources do not necessarily result in better performance for detecting relevant phenotypes. From a clinical perspective, in our use case study, clinical-oriented knowledge resources (e.g., Orphanet) can achieve better performance in detecting relevant phenotypic characterizations compared to biomedical-oriented knowledge resources (e.g., DECIPHER and OMIM).

Detecting Lifestyle Risk Factors for Chronic Kidney Disease With Comorbidities: Association Rule Mining Analysis of Web-Based Survey Data.

BACKGROUND: The rise in the number of patients with chronic kidney disease (CKD) and consequent end-stage renal disease necessitating renal replacement therapy has placed a significant strain on health care. The rate of progression of CKD is influenced by both modifiable and unmodifiable risk factors. Identification of modifiable risk factors, such as lifestyle choices, is vital in informing strategies toward renoprotection. Modification of unhealthy lifestyle choices lessens the risk of CKD progression and associated comorbidities, although the lifestyle risk factors and modification strategies may vary with different comorbidities (eg, diabetes, hypertension). However, there are limited studies on suitable lifestyle interventions for CKD patients with comorbidities. OBJECTIVE: The objectives of our study are to (1) identify the lifestyle risk factors for CKD with common comorbid chronic conditions using a US nationwide survey in combination with literature mining, and (2) demonstrate the potential effectiveness of association rule mining (ARM) analysis for the aforementioned task, which can be generalized for similar tasks associated with noncommunicable diseases (NCDs). METHODS: We applied ARM to identify lifestyle risk factors for CKD progression with comorbidities (cardiovascular disease, chronic pulmonary disease, rheumatoid arthritis, diabetes, and cancer) using questionnaire data for 450,000 participants collected from the Behavioral Risk Factor Surveillance System (BRFSS) 2017. The BRFSS is a Web-based resource, which includes demographic information, chronic health conditions, fruit and vegetable consumption, and sugar- or salt-related behavior. To enrich the BRFSS questionnaire, the Semantic MEDLINE Database was also mined to identify lifestyle risk factors. RESULTS: The results suggest that lifestyle modification for CKD varies among different comorbidities. For example, the lifestyle modification of CKD with cardiovascular disease needs to focus on increasing aerobic capacity by improving muscle strength or functional ability. For CKD patients with chronic pulmonary disease or rheumatoid arthritis, lifestyle modification should be high dietary fiber intake and participation in moderate-intensity exercise. Meanwhile, the management of CKD patients with diabetes focuses on exercise and weight loss predominantly. CONCLUSIONS: We have demonstrated the use of ARM to identify lifestyle risk factors for CKD with common comorbid chronic conditions using data from BRFSS 2017. Our methods can be generalized to advance chronic disease management with more focused and optimized lifestyle modification of NCDs.

Using natural language processing methods to classify use status of dietary supplements in clinical notes.

BACKGROUND: Despite widespread use, the safety of dietary supplements is open to doubt due to the fact that they can interact with prescribed medications, leading to dangerous clinical outcomes. Electronic health records (EHRs) provide a potential way for active pharmacovigilance on dietary supplements since a fair amount of dietary supplement information, especially those on use status, can be found in clinical notes. Extracting such information is extremely significant for subsequent supplement safety research. METHODS: In this study, we collected 2500 sentences for 25 commonly used dietary supplements and annotated into four classes: Continuing (C), Discontinued (D), Started (S) and Unclassified (U). Both rule-based and machine learning-based classifiers were developed on the same training set and evaluated using the hold-out test set. The performances of the two classifiers were also compared. RESULTS: The rule-based classifier achieved F-measure of 0.90, 0.85, 0.90, and 0.86 in C, D, S, and U status, respectively. The optimal machine learning-based classifier (Maximum Entropy) achieved F-measure of 0.90, 0.92, 0.91 and 0.88 in C, D, S, and U status, respectively. The comparison result shows that the machine learning-based classifier has a better performance, which is more efficient and scalable especially when the sample size doubles. CONCLUSIONS: Machine learning-based classifier outperforms rule-based classifier in categorization of the use status of dietary supplements in clinical notes. Future work includes applying deep learning methods and developing a hybrid system to approach use status classification task.

Extracting social determinants of health from clinical note text with classification and sequence-to-sequence approaches.

OBJECTIVE: Social determinants of health (SDOH) are nonmedical factors that can influence health outcomes. This paper seeks to extract SDOH from clinical texts in the context of the National NLP Clinical Challenges (n2c2) 2022 Track 2 Task. MATERIALS AND METHODS: Annotated and unannotated data from the Medical Information Mart for Intensive Care III (MIMIC-III) corpus, the Social History Annotation Corpus, and an in-house corpus were used to develop 2 deep learning models that used classification and sequence-to-sequence (seq2seq) approaches. RESULTS: The seq2seq approach had the highest overall F1 scores in the challenge's 3 subtasks: 0.901 on the extraction subtask, 0.774 on the generalizability subtask, and 0.889 on the learning transfer subtask. DISCUSSION: Both approaches rely on SDOH event representations that were designed to be compatible with transformer-based pretrained models, with the seq2seq representation supporting an arbitrary number of overlapping and sentence-spanning events. Models with adequate performance could be produced quickly, and the remaining mismatch between representation and task requirements was then addressed in postprocessing. The classification approach used rules to generate entity relationships from its sequence of token labels, while the seq2seq approach used constrained decoding and a constraint solver to recover entity text spans from its sequence of potentially ambiguous tokens. CONCLUSION: We proposed 2 different approaches to extract SDOH from clinical texts with high accuracy. However, accuracy suffers on text from new healthcare institutions not present in the training data, and thus generalization remains an important topic for future study.

BIRC5 facilitates cisplatin-chemoresistance in a m6 A-dependent manner in ovarian cancer.

Cisplatin-based chemotherapy is the standard treatment for metastatic ovarian cancer (OC). However, chemoresistance continues to pose significant clinical challenges. Recent research has highlighted the baculoviral inhibitor of the apoptosis protein repeat-containing 5 (BIRC5) as a member of the inhibitor of the apoptosis protein (IAP) family. Notably, BIRC5, which has robust anti-apoptotic capabilities, is overexpressed in numerous cancers. Its dysfunction has been linked to challenges in cancer treatment. Yet, the role of BIRC5 in the chemoresistance of OC remains elusive. In our present study, we observed an upregulation of BIRC5 in cisplatin-resistant cell lines. This upregulation was associated with enhanced chemoresistance, which was diminished when the expression of BIRC5 was silenced. Intriguingly, BIRC5 exhibited a high number of N6-methyladenosine (m6 A) binding sites. The modification of m6 A was found to enhance the expression of BIRC5 by recognizing and binding to the 3'-UTR of mRNA. Additionally, the insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) was shown to stabilize BIRC5 mRNA, synergizing with METTL3 and intensifying chemoresistance. Supporting these in vitro findings, our in vivo experiments revealed that tumors were significantly smaller in size and volume when BIRC5 was silenced. This reduction was notably counteracted by co-silencing BIRC5 and overexpressing IGF2BP1. Our results underscored the pivotal role of BIRC5 in chemoresistance. The regulation of its expression and the stability of its mRNA were influenced by m6 A modifications involving both METTL3 and IGF2BP1. These insights presented BIRC5 as a promising potential therapeutic target for addressing cisplatin resistance in OC.

[Effectiveness of adjunctive photodynamic therapy in the treatment of stage â ¢ and â £ periodontitis].

PURPOSE: To explore the clinical effect of subgingival scaling and root planning (SRP) with adjunctive photodynamic therapy (PDT) in the treatment of stage Ⅲ and Ⅳ periodontitis. METHODS: According to 2018 Classification of Periodontitis, patients diagnosed as stage Ⅲ and Ⅳ periodontitis were recruited. One week after supragingival scaling, probing depth (PD), bleeding on probing (BOP) and gingival index (GI) were recorded as the baseline. All patients were divided into 3 groups, SRP group received whole mouth SRP treatment; PDT1 group: PDT at all sites with PD≥5 mm immediately after SRP; PDT2 group received another PDT at the test sites 6 weeks after full mouth SRP+PDT. PD, GI and the positive rates of BOP were compared 3 months and 6 months after treatment. SPSS 22.0 software package was used for data analysis. RESULTS: Thirty patients and 1 289 test sites were included in this trial. There were 10 patients in group SRP, PDT1 and PDT2, and the number of tests sites were 476, 36.9%, 384, 29.8% and 429 33.3%, respectively. The PD, GI and the positive rates of BOP in the three groups were reduced at 3-months and 6-months of follow-up (P<0.05), there was no significant difference between 3-months and 6-months of follow-up. At the site of PD≥5 mm, group PDT1 and PDT2 could significantly reduce GI and the positive rates of BOP at the test sites(P<0.05). When PD≥7 mm, significant PD reduction was observed in group PDT2(P<0.05). CONCLUSIONS: In the treatment of stage Ⅲ and Ⅳ periodontitis, PDT assisted with SRP therapy can achieve better clinical effect than SRP alone.

Molecular characterization of matrix metalloproteinase gene family across primates.

Deregulation of matrix metalloproteinases (MMPs) contributes considerably to cancers, psychiatric disorders, macular degeneration and bone diseases. The use of humans in the development of MMPs as prognostic biomarkers and therapeutic targets is complicated by many factors, while primate models can be useful alternatives for this purpose. Here, we performed genome-enabled identification of putative MMPs across primate species, and comprehensively investigated the genes. Phylogenetic topology of the MMP family showed each type formulates a distinct clade, and was further clustered to classes, largely agreeing with classification based on biochemical properties and domain organization. Across primates, the excess of candidate sites of positive selection was detected for MMP-19, in addition to 1-3 sites in MMP-8, MMP-10 and MMP-26. MMP-26 showed Ka/Ks value above 1 between human and chimpanzee copies. We observed two copies of MMP-19 in the old-world monkey genomes, suggesting gene duplication at the early stage of or prior to the emergence of the lineage. Furin-activatable MMPs demonstrate the most variable properties regarding Domain organization and gene structure. During human aging, MMP-11 showed gradually decreased expression in testis, so as MMP-2, MMP-14, MMP15 and MMP-28 in ovary, while MMP-7 and MMP-21 showed elevated expression, implying their distinct roles in different reproductive organs. Co-expression clusters were formed among human MMPs both within and across classes, and expression correlation was observed in MMP genes across primates. Our results illuminate the utilization of MMPs for the discovery of prognostic biomarkers and therapeutic targets for aging-related diseases and carry new messages on MMP classification.

Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text.

OBJECTIVE: We sought to demonstrate the feasibility of utilizing deep learning models to extract safety signals related to the use of dietary supplements (DSs) in clinical text. MATERIALS AND METHODS: Two tasks were performed in this study. For the named entity recognition (NER) task, Bi-LSTM-CRF (bidirectional long short-term memory conditional random field) and BERT (bidirectional encoder representations from transformers) models were trained and compared with CRF model as a baseline to recognize the named entities of DSs and events from clinical notes. In the relation extraction (RE) task, 2 deep learning models, including attention-based Bi-LSTM and convolutional neural network as well as a random forest model were trained to extract the relations between DSs and events, which were categorized into 3 classes: positive (ie, indication), negative (ie, adverse events), and not related. The best performed NER and RE models were further applied on clinical notes mentioning 88 DSs for discovering DSs adverse events and indications, which were compared with a DS knowledge base. RESULTS: For the NER task, deep learning models achieved a better performance than CRF, with F1 scores above 0.860. The attention-based Bi-LSTM model performed the best in the RE task, with an F1 score of 0.893. When comparing DS event pairs generated by the deep learning models with the knowledge base for DSs and event, we found both known and unknown pairs. CONCLUSIONS: Deep learning models can detect adverse events and indication of DSs in clinical notes, which hold great potential for monitoring the safety of DS use.

Normalizing Dietary Supplement Product Names Using the RxNorm Model.

The use of dietary supplements (DSs) is increasing in the U.S. As such, it is crucial for consumers, clinicians, and researchers to be able to find information about DS products. However, labeling regulations allow great variability in DS product names, which makes searching for this information difficult. Following the RxNorm drug name normalization model, we developed a rule-based natural language processing system to normalize DS product names using pattern templates. We evaluated the system on product names extracted from the Dietary Supplement Label Database. Our system generated 136 unique templates and obtained a coverage of 72%, a 32% increase over the existing RxNorm model. Manual review showed that our system achieved a normalization accuracy of 0.86. We found that the normalization of DS product names is feasible, but more work is required to improve the generalizability of the system.

Using word embeddings to expand terminology of dietary supplements on clinical notes.

OBJECTIVE: The objective of this study is to demonstrate the feasibility of applying word embeddings to expand the terminology of dietary supplements (DS) using over 26 million clinical notes. METHODS: Word embedding models (ie, word2vec and GloVe) trained on clinical notes were used to predefine a list of top 40 semantically related terms for each of 14 commonly used DS. Each list was further evaluated by experts to generate semantically similar terms. We investigated the effect of corpus size and other settings (ie, vector size and window size) as well as the 2 word embedding models on performance for DS term expansion. We compared the number of clinical notes (and patients they represent) that were retrieved using the word embedding expanded terms to both the baseline terms and external DS sources exandped terms. RESULTS: Using the word embedding models trained on clinical notes, we could identify 1-12 semantically similar terms for each DS. Using the word embedding exandped terms, we were able to retrieve averagely 8.39% more clinical notes and 11.68% more patients for each DS compared with 2 sets of terms. The increasing corpus size results in more misspellings, but not more semantic variants brand names. Word2vec model is also found more capable of detecting semantically similar terms than GloVe. CONCLUSION: Our study demonstrates the utility of word embeddings on clinical notes for terminology expansion on 14 DS. We propose that this method can be potentially applied to create a DS vocabulary for downstream applications, such as information extraction.

Evaluating the Impact of Dictionary Updates on Automatic Annotations Based on Clinical NLP Systems.

Concept encoding, which maps text spans to concepts in standard terminologies, is a critical component in clinical natural language processing (NLP) systems to allow semantic interoperability with other clinical applications. A majority of clinical NLP systems adopt dictionary or lexicon based approaches and the performance of concept encoding is often evaluated using a human created gold standard generated with reference to the most up-to-date standard terminologies available at the time of gold standard creation. With the advance of medical science, standard terminologies or dictionaries can evolve. However, it remains unknown whether the dictionary updates will impact the performance of concept encoding. In this study, we evaluated the annotation performance of two clinical NLP systems, cTAKES and MedXN based on updated dictionaries to gain further insights. Specifically, we compared the automatic annotation results with previously manually generated gold standards. The results of our study demonstrate the annotation changes based on dictionary updates in clinical NLP systems and that it is necessary to do temporal management for gold standards, which raises the need for appropriate terminology management tools for back version compatibility to update gold standards.

Evaluating Automatic Methods to Extract Patients' Supplement Use from Clinical Reports.

The widespread prevalence of dietary supplements has drawn extensive attention due to the safety and efficacy issue. Clinical notes document a great amount of detailed information on dietary supplement usage, thus providing a rich source for clinical research on supplement safety surveillance. Identification the use status of dietary supplements is one of the initial steps for the ultimate goal of the supplement safety surveillance. In this study, we built rule-based and machine learning-based classifiers to automatically classify the use status of supplements into four categories: Continuing (C), Discontinued (D), Started (S), and Unclassified (U). In comparison to the machine learning classifier trained on the same datasets, the rule-based classifier showed a better performance with F-measure in the C, D, S, U status of 0.93, 0.98, 0.95, and 0.83, respectively. We further analyzed the errors generated by the rule-based classifier. The classifier can be potentially applied to extract supplement information from clinical notes for supporting research and clinical practice related to patient safety on supplement usage.

Classifying Supplement Use Status in Clinical Notes.

Clinical notes contain rich information about supplement use that is critical for detecting adverse interactions between supplements and prescribed medications. It is important to know the context in which supplements are mentioned in clinical notes to be able to correctly identify patients that either currently take the supplement or did so in the past. We applied text mining methods to automatically classify supplement use into four status categories: Continuing (C), Discontinued (D), Started (S), and Unclassified (U). We manually classified 1,300 sentences into these categories, which were further split as training (1000 sentences) and testing (300 sentences) sets. We evaluated the 7 types of feature sets and 5 algorithms, and the best model (SVM with unigram, bigram and indicator word within certain distance) performed F-measure of 0.906, 0.913, 0.914, 0.715 for status C, D, S, U, respectively on the testing set. This study demonstrates the feasibility of using text mining methods to classify supplement use status from clinical notes.

Detecting Signals of Interactions Between Warfarin and Dietary Supplements in Electronic Health Records.

Drug and supplement interactions (DSIs) have drawn widespread attention due to their potential to affect therapeutic response and adverse event risk. Electronic health records provide a valuable source where the signals of DSIs can be identified and characterized. We detected signals of interactions between warfarin and seven dietary supplements, viz., alfalfa, garlic, ginger, ginkgo, ginseng, St. John's Wort, and Vitamin E by analyzing structured clinical data and unstructured clinical notes from the University of Minnesota Clinical Data Repository. A machine learning-based natural language processing module was further developed to classify supplement use status and applied to filter out irrelevant clinical notes. Cox proportional hazards models were fitted, controlling for a set of confounding factors: age, gender, and Charlson Index of Comorbidity. There was a statistically significant association of warfarin concurrently used with supplements which can potentially increase the risk of adverse events, such as gastrointestinal bleeding.

Classification of Use Status for Dietary Supplements in Clinical Notes.

Clinical notes contain rich information about dietary supplements, which are critical for detecting signals of dietary supplement side effects and interactions between drugs and supplements. One of the important factors of supplement documentation is usage status, such as started and discontinuation. Such information is usually stored in the unstructured clinical notes. We developed a rule-based classifier to identify supplement usage status in clinical notes. The categories referring to the patient's status of supplement use were classified into four classes: Continuing (C), Discontinued (D), Started (S), and Unclassified (U). Clinical notes containing 10 of the most commonly consumed supplements (i.e., alfalfa, echinacea, fish oil, garlic, ginger, ginkgo, ginseng, melatonin, St. John's Wort, and Vitamin E) were retrieved from the University of Minnesota Clinical Data Repository. The gold standard was defined by manually annotating 1000 randomly selected sentences or statements mentioning at least one of these 10 supplements. The rules in the classifier was initially developed on two-thirds of the set of 7 supplements (i.e., alfalfa, garlic, ginger, ginkgo, ginseng, St. John's Wort, and Vitamin E); the performance was evaluated on the remaining one-third of this set. To evaluate the generalizability of rules, we further validated the second testing set on other 3 supplements (i.e., echinacea, fish oil, and melatonin). The performance of the classifier achieved F-measures of 0.95, 0.97, 0.96, and 0.96 for status C, D, S, and U on 7 supplements, respectively. The classifier also showed good generalizability when it was applied to the other 3 supplements with F-measures of 0.96 for C, 0.96 for D, 0.95 for S, and 0.89 for U. This study demonstrated that the classifier can accurately classify supplement usage status, which can be further integrated as a module into the existing natural language processing pipeline for supporting dietary supplement knowledge discovery.

Anti-inflammatory properties of lipoxin A4 protect against diabetes mellitus complicated by focal cerebral ischemia/reperfusion injury.

Lipoxin A4 can alleviate cerebral ischemia/reperfusion injury by reducing the inflammatory reaction, but it is currently unclear whether it has a protective effect on diabetes mellitus complicated by focal cerebral ischemia/reperfusion injury. In this study, we established rat models of diabetes mellitus using an intraperitoneal injection of streptozotocin. We then induced focal cerebral ischemia/reperfusion injury by occlusion of the middle cerebral artery for 2 hours and reperfusion for 24 hours. After administration of lipoxin A4 via the lateral ventricle, infarction volume was reduced, the expression levels of pro-inflammatory factors tumor necrosis factor alpha and nuclear factor-kappa B in the cerebral cortex were decreased, and neurological functioning was improved. These findings suggest that lipoxin A4 has strong neuroprotective effects in diabetes mellitus complicated by focal cerebral ischemia/reperfusion injury and that the underlying mechanism is related to the anti-inflammatory action of lipoxin A4.