PLAG1 dampens protein synthesis to promote human hematopoietic stem cell self-renewal.

Hematopoietic stem cell (HSC) dormancy is understood as supportive of HSC function and its long-term integrity. Although regulation of stress responses incurred as a result of HSC activation is recognized as important in maintaining stem cell function, little is understood of the preventive machinery present in human HSCs that may serve to resist their activation and promote HSC self-renewal. We demonstrate that the transcription factor PLAG1 is essential for long-term HSC function and, when overexpressed, endows a 15.6-fold enhancement in the frequency of functional HSCs in stimulatory conditions. Genome-wide measures of chromatin occupancy and PLAG1-directed gene expression changes combined with functional measures reveal that PLAG1 dampens protein synthesis, restrains cell growth and division, and enhances survival, with the primitive cell advantages it imparts being attenuated by addition of the potent translation activator, c-MYC. We find PLAG1 capitalizes on multiple regulatory factors to ensure protective diminished protein synthesis including 4EBP1 and translation-targeting miR-127 and does so independently of stress response signaling. Overall, our study identifies PLAG1 as an enforcer of human HSC dormancy and self-renewal through its highly context-specific regulation of protein biosynthesis and classifies PLAG1 among a rare set of bona fide regulators of messenger RNA translation in these cells. Our findings showcase the importance of regulated translation control underlying human HSC physiology, its dysregulation under activating demands, and the potential if its targeting for therapeutic benefit.

A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document.

Regulatory agencies consistently deal with extensive document reviews, ranging from product submissions to both internal and external communications. Large Language Models (LLMs) like ChatGPT can be invaluable tools for these tasks, however present several challenges, particularly the proprietary information, combining customized function with specific review needs, and transparency and explainability of the model's output. Hence, a localized and customized solution is imperative. To tackle these challenges, we formulated a framework named askFDALabel on FDA drug labeling documents that is a crucial resource in the FDA drug review process. AskFDALabel operates within a secure IT environment and comprises two key modules: a semantic search and a Q&A/text-generation module. The Module S built on word embeddings to enable comprehensive semantic queries within labeling documents. The Module T utilizes a tuned LLM to generate responses based on references from Module S. As the result, our framework enabled small LLMs to perform comparably to ChatGPT with as a computationally inexpensive solution for regulatory application. To conclude, through AskFDALabel, we have showcased a pathway that harnesses LLMs to support agency operations within a secure environment, offering tailored functions for the needs of regulatory research.

Two-Year Results of Ceramic-on-Ceramic Hip Resurfacing in an International Multicenter Cohort.

BACKGROUND: Hip resurfacing arthroplasty (HRA) is a bone-conserving alternative to total hip arthroplasty. We present the 2-year clinical and radiographic follow-up of a novel ceramic-on-ceramic HRA in an international multicenter cohort. METHODS: Patients undergoing HRA between September 2018 and January 2021 were prospectively included. Patient-reported outcome measures (PROMs) in the form of the Forgotten Joint Score, Hip Disability and Osteoarthritis Outcome Score Jr., Western Ontario and McMaster Universities Arthritis Index, Oxford Hip Score, and University of California, Los Angeles, Activity Score were collected preoperatively, and at 1 and 2 years postoperation. Serial radiographs were assessed for migration, component alignment, evidence of osteolysis or loosening, and heterotopic ossification formation. RESULTS: The study identified 200 patients who reached a minimum 2-year follow-up (mean 3.5 years). Of these, 185 completed PROMs follow-up at 2 years. There was a significant improvement in Hip Disability and Osteoarthritis Outcome Score (P < .001) and Oxford Hip Score (P < .001) between the preoperative, 1-year, and 2-year outcomes. Patients had improved activity scores on the University of California, Los Angeles, Active Score (P < .001), with 45% reporting a return to high-impact activity at 2 years. At 1 and 2 years, the Forgotten Joint Score was not significantly different (P = .38). There was no migration, osteolysis, or loosening of any of the implants. No fractures were reported over the 2-year follow-up, with only 1 patient reporting a sciatic nerve palsy. There were 2 revisions, 1 for unexplained pain at 3 months due to acetabular component malposition and 1 at 33.5 months for acetabular implant failure. CONCLUSIONS: The ceramic-on-ceramic resurfacing at 2 years postoperation demonstrates promising results with satisfactory outcomes in all recorded PROMs. Further long-term data are needed to support the widespread adoption of this prosthesis as an alternative to other HRA bearings.

The Use of Hyaluronic Acid in Non-surgical Rhinoplasty: A Systematic Review of Complications, Clinical, and Patient-Reported Outcomes.

INTRODUCTION: In cosmetic practices, non-surgical rhinoplasty using filler injections has become increasingly common. Nevertheless, the outcome and overall complications have not been studied as a systematic review in the literature. This study provides a high-quality systematic review of studies reporting clinical and patient-reported outcomes following non-surgical rhinoplasty with hyaluronic acid (HA) to further guide practitioners. METHODS: This systematic review was conducted in accordance with PRISMA guidelines and was registered in PROSPERO. The search was conducted using MEDLINE, EMBASE, and Cochrane. The literature retrieval was conducted by three independent reviewers, and the remaining articles were screened by two independent reviewers. The quality of included articles was assessed using the MINORS and methodological quality and synthesis of case series and case reports tools. RESULTS: A total of 874 publications were found based on the search criteria. A total of 3928 patients were reviewed for this systematic review from 23 full-text articles. For non-surgical rhinoplasty, Juvéderm ultra was the most commonly used HA filler. The nasal tip was most commonly injected (13 studies), followed by the columella (12 studies). Nasal hump deformities are the most common reason for non-surgical rhinoplasty. All studies showed high patient satisfaction. Among all patients reviewed, eight developed major complications. CONCLUSION: Non-surgical rhinoplasty performed with HA has minimal side effects and a short recovery period. Furthermore, non-surgical rhinoplasty with HA results in high satisfaction. To strengthen the presently available evidence, further well-designed RCTs are needed. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors https://www.springer.com/00266.

Co-surgeon versus Single-surgeon Outcomes in Free Tissue Breast Reconstruction: A Meta-analysis.

BACKGROUND: Autologous breast reconstruction offers superior long-term patient reported outcomes compared with implant-based reconstruction. Universal adoption of free tissue transfer has been hindered by procedural complexity and long operative time with microsurgery. In many specialties, co-surgeon (CS) approaches are reported to decrease operative time while improving surgical outcomes. This systematic review and meta-analysis synthesizes the available literature to evaluate the potential benefit of a CS approach in autologous free tissue breast reconstruction versus single-surgeon (SS). METHODS: A systematic review and meta-analysis was conducted using PubMed, Embase, and MEDLINE from inception to December 2022. Published reports comparing CS to SS approaches in uni- and bilateral autologous breast reconstruction were identified. Primary outcomes included operative time, postoperative outcomes, processes of care, and financial impact. Risk of bias was assessed and outcomes were characterized with effect sizes. RESULTS: Eight retrospective studies reporting on 9,425 patients were included. Compared with SS, CS approach was associated with a significantly shorter operative time (SMD -0.65, 95% confidence interval [CI] -1.01 to -0.29, p < 0.001), with the largest effect size in bilateral reconstructions (standardized mean difference [SMD] -1.02, 95% CI -1.37 to -0.67, p < 0.00001). CS was also associated with a significant decrease in length of hospitalization (SMD -0.39, 95% CI -0.71 to -0.07, p = 0.02). Odds of flap failure or surgical complications including surgical site infection, hematoma, fat necrosis, and reexploration were not significantly different. CONCLUSION: CS free tissue breast reconstruction significantly shortens operative time and length of hospitalization compared with SS approaches without compromising postoperative outcomes. Further research should model processes and financial viability of its adoption in a variety of health care models.

Trends in the use of ankle arthrodesis and total ankle replacements in Australia over the past 20 years.

BACKGROUND: End stage ankle osteoarthritis (OA) is debilitating. Surgical management consists of either ankle arthrodesis (AA) or a total ankle replacement (TAR). The purpose of this study is to assess the trends in operative intervention for end stage ankle OA in an Australian population. METHODS: This is a retrospective epidemiological study of 15,046 surgeries. Data were collected from publicly available national registries including the Australian Medicare Database and Australian Orthopaedic Association National Joint Replacement Registrar from 2001 to 2020. RESULTS: There was a significant increase in all ankle surgeries performed across the period of interest. AA remained the more commonly performed procedure throughout the course of the study (11,946 cases, 79.4%) and was never surpassed by TAR (3100, 20.6%). The overall proportions demonstrated no significant changes from 2001 to 2020. CONCLUSION: The incidence of ankle surgeries continues to increase with the ageing and increasingly comorbid population of Australia. Despite demonstrating no significant overall change in the ratio of TAR and AA in our study population and period, there are noticeable trends within the timeframe, with a recent surge favouring TAR in the last 5 years.

Classifying Free Texts Into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents.

The US Food and Drug Administration (FDA) regulatory process often involves several reviewers who focus on sets of information related to their respective areas of review. Accordingly, manufacturers that provide submission packages to regulatory agencies are instructed to organize the contents using a structure that enables the information to be easily allocated, retrieved, and reviewed. However, this practice is not always followed correctly; as such, some documents are not well structured, with similar information spreading across different sections, hindering the efficient access and review of all of the relevant data as a whole. To improve this common situation, we evaluated an artificial intelligence (AI)-based natural language processing (NLP) methodology, called Bidirectional Encoder Representations from Transformers (BERT), to automatically classify free-text information into standardized sections, supporting a holistic review of drug safety and efficacy. Specifically, FDA labeling documents were used in this study as a proof of concept, where the labeling section structure defined by the Physician Label Rule (PLR) was used to classify labels in the development of the model. The model was subsequently evaluated on texts from both well-structured labeling documents (i.e., PLR-based labeling) and less- or differently structured documents (i.e., non-PLR and Summary of Product Characteristic [SmPC] labeling.) In the training process, the model yielded 96% and 88% accuracy for binary and multiclass tasks, respectively. The testing accuracies observed for the PLR, non-PLR, and SmPC testing data sets for the binary model were 95%, 88%, and 88%, and for the multiclass model were 82%, 73%, and 68%, respectively. Our study demonstrated that automatically classifying free texts into standardized sections with AI language models could be an advanced regulatory science approach for supporting the review process by effectively processing unformatted documents.

A Weakly Supervised Deep Learning Framework for Whole Slide Classification to Facilitate Digital Pathology in Animal Study.

The pathology of animal studies is crucial for toxicity evaluations and regulatory assessments, but the manual examination of slides by pathologists remains time-consuming and requires extensive training. One inherent challenge in this process is the interobserver variability, which can compromise the consistency and accuracy of a study. Artificial intelligence (AI) has demonstrated its ability to automate similar examinations in clinical applications with enhanced efficiency, consistency, and accuracy. However, training AI models typically relies on costly pixel-level annotation of injured regions and is often not available for animal pathology. To address this, we developed the PathologAI system, a "weakly" supervised approach for WSI classification in rat images without explicit lesion annotation at the pixel level. Using rat liver imaging data from the Open TG-GATEs system, PathologAI was applied to predict necrosis of n = 816 WSIs (377 controls). TG-GATEs studied 170 compounds at three dose levels (low, middle, and high) for four time points (3, 7, 14, and 28 days). PathologAI first preprocessed WSIs at the tile level to generate a high-level representation with a Generative Adversarial Network architecture. The prediction of liver necrosis relied on an ensemble model of 5 CNN classifiers trained on 335 WSIs. The ensemble model achieved notable classification accuracy on the holdout test set: 87% among 87 control slides free of findings, 83% among 120 controls with spontaneous necrosis, 67% among 147 treated animals with spontaneous minimal or slight necrosis, and 59% among 127 treated animals with minimal or slight necrosis caused by the treatment. Importantly, PathologAI was able to discriminate WSIs with spontaneous necrosis from those with treatment related necrosis and discriminated mild lesion level findings (slight vs minimal) and between treatment dose levels. PathologAI could provide an inexpensive and rapid screening tool to assist the digital pathology analysis in preclinical applications and general toxicological studies.

Development of benchmark datasets for text mining and sentiment analysis to accelerate regulatory literature review.

In the field of regulatory science, reviewing literature is an essential and important step, which most of the time is conducted by manually reading hundreds of articles. Although this process is highly time-consuming and labor-intensive, most output of this process is not well transformed into machine-readable format. The limited availability of data has largely constrained the artificial intelligence (AI) system development to facilitate this literature reviewing in the regulatory process. In the past decade, AI has revolutionized the area of text mining as many deep learning approaches have been developed to search, annotate, and classify relevant documents. After the great advancement of AI algorithms, a lack of high-quality data instead of the algorithms has recently become the bottleneck of AI system development. Herein, we constructed two large benchmark datasets, Chlorine Efficacy dataset (CHE) and Chlorine Safety dataset (CHS), under a regulatory scenario that sought to assess the antiseptic efficacy and toxicity of chlorine. For each dataset, ∼10,000 scientific articles were initially collected, manually reviewed, and their relevance to the review task were labeled. To ensure high data quality, each paper was labeled by a consensus among multiple experienced reviewers. The overall relevance rate was 27.21% (2,663 of 9,788) for CHE and 7.50% (761 of 10,153) for CHS, respectively. Furthermore, the relevant articles were categorized into five subgroups based on the focus of their content. Next, we developed an attention-based classification language model using these two datasets. The proposed classification model yielded 0.857 and 0.908 of Area Under the Curve (AUC) for CHE and CHS dataset, respectively. This performance was significantly better than permutation test (p < 10E-9), demonstrating that the labeling processes were valid. To conclude, our datasets can be used as benchmark to develop AI systems, which can further facilitate the literature review process in regulatory science.

Re-revision Knee Arthroplasty in a Tertiary Center: Infection and Multiple Previous Surgeries Were Associated With Poor Early Clinical and Functional Outcomes.

BACKGROUND: The incidence of re-revision knee arthroplasty (re-revision KA) is increasing and associated with high complication and failure rates. The aim of this study was to investigate re-revision rates, complications, and patient-reported outcomes following re-revision KA and factors associated with poor outcome. METHODS: This was a retrospective cohort study of 206 patients (250 knees) undergoing re-revision KA at a major revision center from 2015 to 2018. The mean follow-up was 26 months (range, 0 to 61) and mean age at re-revision KA was 69 years (range, 31 to 91 years). The main indications for surgery were prosthetic joint infection (PJI) (n = 171/250, 68.4%) and aseptic loosening (n = 25/250, 10.0%). We compared re-revision rates, joint function, and complications for aseptic and infective indications. Logistic regressions were performed to identify risk factors for further reoperation. RESULTS: The estimated re-revision rates at 2 years were 28.7% (95% confidence interval [CI]: 22.7-35.9) and at 4 years were 42.0% (95% CI: 32.8-52.6). Mean Oxford Knee Score was 26 points (range, 1 to 48). Mean EuroQoL-5D-5L utility was 0.539 (range, -0.511 to 1.000). Multivariable analyses demonstrated that PJI (Odds Ratio [OR] 2.39, 95% CI 1.06-5.40, P = .036), greater number of previous surgeries (OR 1.18, 95% CI 1.04-1.33, P = .008), and higher Elixhauser score (OR 1.06, 95% CI 1.01-1.13, P = .045) were independently associated to further surgery. CONCLUSION: Re-revision KA carried a high risk of early failure. Multiple revised joints and patients with more comorbidities had worse function. Patients undergoing re-revision KA for PJI should be counseled to expect higher failure rates and complications than patients who have aseptic indications.

Long-Term Results of Alumina Ceramic-On-Ceramic Bearings in Cementless Total Hip Arthroplasty: A 20-Year Minimum Follow-Up.

BACKGROUND: Alumina ceramic-on-ceramic bearings are used in total hip arthroplasty (THA) because of their wear-resistant and inert properties. In this study, we assessed the clinical and radiographic outcomes of patients undergoing primary cementless ceramic-on-ceramic THA at a minimum follow-up of 20 years. METHODS: A series of 301 consecutive primary THAs in 283 patients were assessed. Clinically, patients were assessed with the modified Harris Hip Score (HHS) and pain questionnaires. Anteroposterior radiographs of the pelvis and lateral radiographs of the hip were used to radiologically assess the implant. Patients were classified as lost to follow-up if they could not be contacted on multiple occasions or did not wish to participate further in this study. RESULTS: At twenty years after operation, 60 patients had died of a cause unrelated to surgery, 16 had experienced complications requiring reoperation, and 100 hips had both clinical assessments and radiographs at a minimum of 20 years of follow-up. The average HHS improved from 56.1 (range: 17-89) before THA to 92.5 (range: 63-100) at the latest follow-up. The classification of the HHS was good or excellent in 96.4% of patients. Only 1.8% of patients still had moderate residual pain at the thigh or groin. Radiographically, all patients demonstrated bony ingrowth but no clinical symptoms of loosening. The overall survival rate of the implants was 94.2% at 20 years with revision for any reason as the end point. CONCLUSION: Long-Term follow-up in our series showed excellent implant survival, excellent functional outcomes, and minimal late complications. There was no significant radiographic evidence of failure at a minimum of 20 years after THA. LEVEL OF EVIDENCE: Therapeutic Level IV.

A targeted simulation-extrapolation method for evaluating biomarkers based on new technologies in precision medicine.

New technologies for novel biomarkers have transformed the field of precision medicine. However, in applications such as liquid biopsy for early tumor detection, the misclassification rates of next generation sequencing and other technologies have become an unavoidable feature of biomarker development. Because initial experiments are usually confined to specific technology choices and application settings, a statistical method that can project the performance metrics of other scenarios with different misclassification rates would be very helpful for planning further biomarker development and future trials. In this article, we describe an approach based on an extended version of simulation extrapolation (SIMEX) to project the performance of biomarkers measured with varying misclassification rates due to different technological or application settings when experimental results are only available from one specific setting. Through simulation studies for logistic regression and proportional hazards models, we show that our proposed method can be used to project the biomarker performance with good precision when switching from one to anther technology or application setting. Similar to the original SIMEX model, the proposed method can be implemented with existing software in a straightforward manner. A data analysis example is also presented using a lung cancer data set and performance metrics for two gene panel based biomarkers. Results demonstrate that it is feasible to infer the potential implications of using a range of technologies or application scenarios for biomarkers with limited human trial data.

Landscape of circRNAs Across 11 Organs and 4 Ages in Fischer 344 Rats.

Circular RNAs (circRNAs) are a class of endogenous noncoding RNAs with a covalently closed loop. Aside from their recognized regulatory functions (e.g., sponging microRNAs to reduce their activity, and altering parental gene transcription by competing with the canonical splicing of pre-mRNA), expression of circRNAs is abundant, diverse, and conservative across species, rendering them as potential biomarker candidates. Consequently, the landscape of circRNAs has been studied for several species. Although the rat is one of the most important animal models for drug safety and toxicological research, few attempts have been made to understand the landscape of rat circRNAs. One noticeable challenge in analyzing circRNAs with next-generation sequencing (NGS) data is to find ways to use rapidly advancing bioinformatics approaches to improve accuracy while also reducing the number of resulting false positives that occur in circRNA identification with these new methods. Here, we applied two of the most advanced circRNA bioinformatics pipelines to provide a landscape of circRNAs in rats by analyzing an RNA-seq data set for 11 organs (adrenal gland, brain, heart, kidney, liver, lung, muscle, spleen, thymus, and testis or uterus) from Fischer 344 rats of both sexes in four age groups (juvenile, adolescence, adult, and aged). The circRNAs displayed organ-specific patterns and sex differences in most organs. Lowest numbers of circRNAs were seen in the liver and muscle, while highest numbers of circRNAs occurred in the brain, which correlated to gene expression patterns seen across those organs. Concordance of circRNAs between males and females was approximately 50% in nonsex organs, implying that some caution needs to be exercised when selecting specific circRNAs as biomarkers for both sexes. The number of common circRNAs between sexes increased with age for most organs except heart, spleen, and thymus. A dramatic drop in the number of circRNAs in kidney, thymus, and testis was observed in aged rats, suggesting that the regulatory function of circRNAs is age dependent. From the 1595 circRNAs identified with high confidence, only 6 appeared in all 9 of the nonsex organs in both sexes and four age groups. Forty-one and 48 circRNAs were identified in more than 5 nonsex organs in males and females, respectively, while close to 280 circRNAs were found in an organ for more than 2 age groups in both sexes. This study offers an overview of rat circRNAs, which contributes to the effort of identifying circRNAs as potential biomarkers for safety and risk assessment.

Impact of Sequencing Depth and Library Preparation on Toxicological Interpretation of RNA-Seq Data in a "Three-Sample" Scenario.

While RNA-sequencing (RNA-seq) has emerged as a standard approach in toxicogenomics, its full potential in gaining underlying toxicological mechanisms is still not clear when only three biological replicates are used. This "three-sample" study design is common in toxicological research, particularly in animal studies during preclinical drug development. Sequencing depth (the total number of reads in an experiment) and library preparation are critical to the resolution and integrity of RNA-seq data and biological interpretation. We used aflatoxin b1 (AFB1), a model toxicant, to investigate the effect of sequencing depth and library preparation in RNA-seq on toxicological interpretation in the "three-sample" scenario. We also compared different gene profiling platforms (RNA-seq, TempO-seq, microarray, and qPCR) using identical liver samples. Well-established mechanisms of AFB1 toxicity served as ground truth for our comparative analyses. We found that a minimum of 20 million reads was sufficient to elicit key toxicity functions and pathways underlying AFB1-induced liver toxicity using three replicates and that identification of differentially expressed genes was positively associated with sequencing depth to a certain extent. Further, our results showed that RNA-seq revealed toxicological insights from pathway enrichment with overall higher statistical power and overlap ratio, compared with TempO-seq and microarray. Moreover, library preparation using the same methods was important to reproducing the toxicological interpretation.

Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets.

Selecting a model in predictive toxicology often involves a trade-off between prediction performance and explainability: should we sacrifice the model performance to gain explainability or vice versa. Here we present a comprehensive study to assess algorithm and feature influences on model performance in chemical toxicity research. We conducted over 5000 models for a Tox21 bioassay data set of 65 assays and ∼7600 compounds. Seven molecular representations as features and 12 modeling approaches varying in complexity and explainability were employed to systematically investigate the impact of various factors on model performance and explainability. We demonstrated that end points dictated a model's performance, regardless of the chosen modeling approach including deep learning and chemical features. Overall, more complex models such as (LS-)SVM and Random Forest performed marginally better than simpler models such as linear regression and KNN in the presented Tox21 data analysis. Since a simpler model with acceptable performance often also is easy to interpret for the Tox21 data set, it clearly was the preferred choice due to its better explainability. Given that each data set had its own error structure both for dependent and independent variables, we strongly recommend that it is important to conduct a systematic study with a broad range of model complexity and feature explainability to identify model balancing its predictivity and explainability.

DLI-IT: a deep learning approach to drug label identification through image and text embedding.

BACKGROUND: Drug label, or packaging insert play a significant role in all the operations from production through drug distribution channels to the end consumer. Image of the label also called Display Panel or label could be used to identify illegal, illicit, unapproved and potentially dangerous drugs. Due to the time-consuming process and high labor cost of investigation, an artificial intelligence-based deep learning model is necessary for fast and accurate identification of the drugs. METHODS: In addition to image-based identification technology, we take advantages of rich text information on the pharmaceutical package insert of drug label images. In this study, we developed the Drug Label Identification through Image and Text embedding model (DLI-IT) to model text-based patterns of historical data for detection of suspicious drugs. In DLI-IT, we first trained a Connectionist Text Proposal Network (CTPN) to crop the raw image into sub-images based on the text. The texts from the cropped sub-images are recognized independently through the Tesseract OCR Engine and combined as one document for each raw image. Finally, we applied universal sentence embedding to transform these documents into vectors and find the most similar reference images to the test image through the cosine similarity. RESULTS: We trained the DLI-IT model on 1749 opioid and 2365 non-opioid drug label images. The model was then tested on 300 external opioid drug label images, the result demonstrated our model achieves up-to 88% of the precision in drug label identification, which outperforms previous image-based or text-based identification method by up-to 35% improvement. CONCLUSION: To conclude, by combining Image and Text embedding analysis under deep learning framework, our DLI-IT approach achieved a competitive performance in advancing drug label identification.

The Association Between Anxiety, Depression, and Locus of Control With Patient Outcomes Following Total Knee Arthroplasty.

BACKGROUND: We aimed to determine how preoperative anxiety, depression, and locus of control (LoC) might predict patient outcomes following total knee arthroplasty (TKA). METHODS: Patients undergoing TKA were prospectively recruited over an 18-month period. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was used to assess TKA outcomes. The Short Form-12, Hospital Anxiety and Depression Score, and LoC surveys were completed by the patients to assess their psychosocial state. These scores were collected preoperatively and at 6 weeks, 18 weeks, and 1 year postoperation. RESULTS: The final cohort consisted of 136 patients. Greater preoperative depression (P = .004) and anxiety (P = .001) scores were correlated with worse total WOMAC score at 6 weeks and 18 weeks postoperatively, respectively. A poorer preoperative Short Form-12 mental score was also correlated with a worse total WOMAC score at 6 weeks postoperatively (P = .007). Greater tendency toward an internal LoC preoperatively was correlated with better WOMAC pain (P < .001) and function (P = .003) scores at 18 weeks postoperatively. However, there was no correlation between preoperative external LoC and postoperative WOMAC score. There was also no correlation between any of the preoperative psychosocial measures and WOMAC score at 1 year postoperatively. CONCLUSION: We identified a group of patients whose psychosocial markers predicted them to have worse outcomes in the short to medium term even though they normalized to satisfactory outcomes at 1 year postoperatively. Identifying this group could allow for targeted intervention with an adjustment of expectations and thus more effective recovery.

Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA.

BACKGROUND: Adverse Drug Reactions (ADRs) are of great public health concern. FDA-approved drug labeling summarizes ADRs of a drug product mainly in three sections, i.e., Boxed Warning (BW), Warnings and Precautions (WP), and Adverse Reactions (AR), where the severity of ADRs are intended to decrease in the order of BW > WP > AR. Several reported studies have extracted ADRs from labeling documents, but most, if not all, did not discriminate the severity of the ADRs by the different labeling sections. Such a practice could overstate or underestimate the impact of certain ADRs to the public health. In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to drug labeling and systematically analyzed and compared the ADRs from the three labeling sections with a specific emphasis on analyzing serious ADRs presented in BW, which is of most drug safety concern. RESULTS: This study investigated New Drug Application (NDA) labeling documents for 1164 single-ingredient drugs using Oracle Text search to extract MedDRA terms. We found that only a small portion of MedDRA Preferred Terms (PTs), 3819 out of 21,920 or 17.42%, were observed in a whole set of documents. In detail, 466/3819 (12.0%) PTs were in BW, 2023/3819 (53.0%) were in WP, and 2961/3819 (77.5%) were in AR sections. We also found a higher overlap of top 20 occurring BW PTs with WP sections compared to AR sections. Within the MedDRA System Organ Class levels, serious ADRs (sADRs) from BW were prevalent in Nervous System disorders and Vascular disorders. A Hierarchical Cluster Analysis (HCA) revealed that drugs within the same therapeutic category shared the same ADR patterns in BW (e.g., nervous system drug class is highly associated with drug abuse terms such as dependence, substance abuse, and respiratory depression). CONCLUSIONS: This study demonstrated that combining MedDRA standard terminologies with data mining techniques facilitated computer-aided ADR analysis of drug labeling. We also highlighted the importance of labeling sections that differ in seriousness and application in drug safety. Using sADRs primarily related to BW sections, we illustrated a prototype approach for computer-aided ADR monitoring and studies which can be applied to other public health documents.

HetEnc: a deep learning predictive model for multi-type biological dataset.

BACKGROUND: Researchers today are generating unprecedented amounts of biological data. One trend in current biological research is integrated analysis with multi-platform data. Effective integration of multi-platform data into the solution of a single or multi-task classification problem; however, is critical and challenging. In this study, we proposed HetEnc, a novel deep learning-based approach, for information domain separation. RESULTS: HetEnc includes both an unsupervised feature representation module and a supervised neural network module to handle multi-platform gene expression datasets. It first constructs three different encoding networks to represent the original gene expression data using high-level abstracted features. A six-layer fully-connected feed-forward neural network is then trained using these abstracted features for each targeted endpoint. We applied HetEnc to the SEQC neuroblastoma dataset to demonstrate that it outperforms other machine learning approaches. Although we used multi-platform data in feature abstraction and model training, HetEnc does not need multi-platform data for prediction, enabling a broader application of the trained model by reducing the cost of gene expression profiling for new samples to a single platform. Thus, HetEnc provides a new solution to integrated gene expression analysis, accelerating modern biological research.

Global transcriptomic analyses of Salmonella enterica in Iron-depleted and Iron-rich growth conditions.

BACKGROUND: Salmonella enterica possess several iron acquisition systems, encoded on the chromosome and plasmids. Recently, we demonstrated that incompatibility group (Inc) FIB plasmid-encoded iron acquisition systems (Sit and aerobactin) likely play an important role in persistence of Salmonella in human intestinal epithelial cells (Caco-2). In this study, we sought to determine global transcriptome analyses of S. enterica in iron-rich (IR) and iron-depleted (ID) growth conditions. RESULTS: The number of differentially-expressed genes were substantially higher for recipient (SE819) (n = 966) and transconjugant (TC) (n = 945) compared to the wild type (WT) (SE163A) (n = 110) strain in ID as compared to IR growth conditions. Several virulence-associated factors including T3SS, flagellin, cold-shock protein (cspE), and regulatory genes were upregulated in TC in ID compared to IR conditions. Whereas, IS1 and acrR/tetR transposases located on the IncFIB plasmid, ferritin and several regulatory genes were downregulated in TC in ID conditions. Enterobactin transporter (entS), iron ABC transporter (fepCD), colicin transporter, IncFIB-encoded enolase, cyclic di-GMP regulator (cdgR) and other regulatory genes of the WT strain were upregulated in ID compared to IR conditions. Conversely, ferritin, ferrous iron transport protein A (feoA), IncFIB-encoded IS1 and acrR/tetR transposases and ArtA toxin of WT were downregulated in ID conditions. SDS-PAGE coupled with LC-MS/MS analyses revealed that siderophore receptor proteins such as chromosomally-encoded IroN and, IncFIB-encoded IutA were upregulated in WT and TC in ID growth conditions. Both chromosome and IncFIB plasmid-encoded SitA was overexpressed in WT, but not in TC or recipient in ID conditions. Increased expression of flagellin was detected in recipient and TC, but not in WT in ID conditions. CONCLUSION: Iron concentrations in growth media influenced differential gene expressions both at transcriptional and translational levels, including genes encoded on the IncFIB plasmid. Limited iron availability within the host may promote pathogenic Salmonella to differentially express subsets of genes encoded by chromosome and/or plasmids, facilitating establishment of successful infection.