Miracle-dz/UniFAR-FTUs huggingface dataset:
Acute and Past Common Lacrimal Canalicular Lacerations: A Report of Two Cases

Lacrimal canalicular lacerations can be caused by trauma to the ocular adnexa, such as a penetrating or blunt injury. Only a few cases involving the common canaliculus or lacrimal sac have been reported, and only a few reports have described the detailed course of treatment. Here, we report an acute case of common lacrimal canalicular laceration and a case of a previous common canalicular laceration. The acute case was treated with a bicanalicular stent directly inserted into the nasolacrimal duct. The case with a previous common canalicular laceration was treated with external dacryocystorhinostomy combined with monocanalicular stent insertion. These treatments may be appropriate for the initial surgery and can be selected before performing conjunctivodacryocystorhinostomy.
Automatic detection of Opisthorchis viverrini egg in stool examination using convolutional-based neural networks

Background Human opisthorchiasis is a dangerous infectious chronic disease distributed in many Asian areas in the water-basins of large rivers, Siberia, and Europe. The gold standard for human opisthorchiasis laboratory diagnosis is the routine examination of Opisthorchis spp. eggs under a microscope. Manual detection is laborious, time-consuming, and dependent on the microscopist’s abilities and expertise. Automatic screening of Opisthorchis spp. eggs with deep learning techniques is a useful diagnostic aid. Methods Herein, we propose a convolutional neural network (CNN) for classifying and automatically detecting O. viverrini eggs from digitized images. The image data acquisition was acquired from infected human feces and was processed using the gold standard formalin ethyl acetate concentration technique, and then captured under the microscope digital camera at 400x. Microscopic images containing artifacts and O.viverrini egg were augmented using image rotation, filtering, noising, and sharpening techniques. This augmentation increased the image dataset from 1 time to 36 times in preparation for the training and validation step. Furthermore, the overall dataset was subdivided into a training-validation and test set at an 80:20 ratio, trained with a five-fold cross-validation to test model stability. For model training, we customized a CNN for image classification. An object detection method was proposed using a patch search algorithm to detect eggs and their locations. A performance matrix was used to evaluate model efficiency after training and IoU analysis for object detection. Results The proposed model, initially trained on non-augmented data of artifacts (class 0) and O. viverrini eggs (class 1), showed limited performance with 50.0% accuracy, 25.0% precision, 50.0% recall, and a 33.0% F1-score. After implementing data augmentation, the model significantly improved, reaching 100% accuracy, precision, recall, and F1-score. Stability assessments using 5-fold cross-validation indicated better stability with augmented data, evidenced by an ROC-AUC metric improvement from 0.5 to 1.00. Compared to other models such as ResNet50, InceptionV3, VGG16, DenseNet121, and Xception, the proposed model, with a smaller file size of 2.7 MB, showed comparable perfect performance. In object detection, the augmented data-trained model achieved an IoU score over 0.5 in 139 out of 148 images, with an average IoU of 0.6947. Conclusion This study demonstrated the successful application of CNN in classifying and automating the detection of O. viverrini eggs in human stool samples. Our CNN model’s performance metrics and true positive detection rates were outstanding. This innovative application of deep learning can automate and improve diagnostic precision, speed, and efficiency, particularly in regions where O. viverrini infections are prevalent, thereby possibly improving infection sustainable control and treatment program.
Clinical pharmacist intervention to ensure safe stimulant prescribing practices at a Veterans Affairs facility

Introduction The Psychotropic Drug Safety Initiative (PDSI) is a national Veterans Affairs program that recommends obtaining cardiovascular vital signs semiannually and urine toxicology screening annually for veterans prescribed stimulants. The PDSI also recommends a risk review of concurrent central nervous system (CNS) depressants to ensure the benefits of coadministration with stimulants outweigh the risks. This project’s purpose was to evaluate the occurrence of coprescriptions for CNS depressants and stimulants and encourage compliance with the PDSI recommendations to increase safe and appropriate management of veterans prescribed the combination. This study aimed to evaluate the occurrence of coprescriptions for CNS depressants and stimulants, evaluate compliance with stimulant monitoring recommendations, and measure the proportion of pharmacist recommendations implemented by the prescriber. Methods This quality improvement project identified veterans with an outpatient prescription for a stimulant and any coprescription(s) for benzodiazepines, sedative-hypnotics, and/or opioids. A pharmacy intervention note was generated to request a risk review, provide recommendations for de-escalation, and notify the stimulant prescriber of overdue monitoring parameters. Impact was measured 60 days after intervention. Descriptive statistics and a McNemar test were used to compare preintervention and postintervention data. Results From the 61 patients included, there were 67 unique prescriptions for benzodiazepines (49.3%), sedative-hypnotics (34.3%), and opioids (16.4%) in combination with a stimulant. Pharmacist intervention resulted in de-escalation of coprescribing for 9 patients (16.1%) and was associated with statistically significant improvement in compliance to stimulant monitoring recommendations. Discussion Clinical pharmacists can assist in ensuring safe and appropriate monitoring and management of veterans prescribed stimulants.
An Update on Anti-TNF Biosimilar Switching—Real-World Clinical Effectiveness and Safety

Abstract Background Biological medications for inflammatory bowel disease (IBD) account for a significant burden on provincial budgets. In an effort to curb these rising costs, nationwide switching to biosimilars is expected to be complete in Canada before the end of 2023. Biosimilar products do not require the same rigor for licensing as the originator and therefore there has been appropriate scepticism as to how biosimilars will perform in real-world practice. Methods We have performed a systematic review including real-world observational studies of adult patients with IBD. The primary outcome was clinical effectiveness and/or safety in patients who had switched from originator to biosimilar anti-TNF. Secondary outcomes included loss of response (LOR), treatment persistence or cessation and immunogenicity. Results We included 43 studies (7,462 patients [70 percent Crohn’s disease: 30 percent ulcerative colitis]; 32 infliximab studies, and 11 adalimumab studies). For infliximab, 75 percent patients were in clinical remission at the time of switch and 75 percent maintained clinical remission beyond 12 months, compared to 78 percent of patients who continued originator. For adalimumab, 86 percent patients were in remission at the time of switch with 82 percent maintaining remission at 6 months follow-up. Injection site pain was higher in patients who switched to a citrate containing adalimumab biosimilar, compared with those who continued originator. All other outcomes (LOR, treatment cessation or persistence and serious adverse events) were similar to patients who continued originator (in comparator cohorts or the available literature). Conclusion Whilst ongoing vigilance is required, these data are reassuring to both patients and clinicians and will significantly help to reduce health-care costs across Canada.
Molecular signature of stem-like glioma cells (SLGCs) from human glioblastoma and gliosarcoma

Glioblastoma multiforme (GBM) and the GBM variant gliosarcoma (GS) are among the tumors with the highest morbidity and mortality, providing only palliation. Stem-like glioma cells (SLGCs) are involved in tumor initiation, progression, therapy resistance, and relapse. The identification of general features of SLGCs could contribute to the development of more efficient therapies. Commercially available protein arrays were used to determine the cell surface signature of eight SLGC lines from GBMs, one SLGC line obtained from a xenotransplanted GBM-derived SLGC line, and three SLGC lines from GSs. By means of non-negative matrix factorization expression metaprofiles were calculated. Using the cophenetic correlation coefficient (CCC) five metaprofiles (MPs) were identified, which are characterized by specific combinations of 7–12 factors. Furthermore, the expression of several factors, that are associated with GBM prognosis, GBM subtypes, SLGC differentiation stages, or neural identity was evaluated. The investigation encompassed 24 distinct SLGC lines, four of which were derived from xenotransplanted SLGCs, and included the SLGC lines characterized by the metaprofiles. It turned out that all SLGC lines expressed the epidermal growth factor EGFR and EGFR ligands, often in the presence of additional receptor tyrosine kinases. Moreover, all SLGC lines displayed a neural signature and the IDH1 wildtype, but differed in their p53 and PTEN status. Pearson Correlation analysis identified a positive association between the pluripotency factor Sox2 and the expression of FABP7, Musashi, CD133, GFAP, but not with MGMT or Hif1α. Spherical growth, however, was positively correlated with high levels of Hif1α, CDK4, PTEN, and PDGFRβ, whereas correlations with stemness factors or MGMT (MGMT expression and promoter methylation) were low or missing. Factors highly expressed by all SLGC lines, irrespective of their degree of stemness and growth behavior, are Cathepsin-D, CD99, EMMPRIN/CD147, Intβ1, the Galectins 3 and 3b, and N-Cadherin.
Clinical Use of Bedside Portable Low-field Brain Magnetic Resonance Imaging in Patients on ECMO: The Results from Multicenter SAFE MRI ECMO Study

Purpose: Early detection of acute brain injury (ABI) is critical for improving survival for patients with extracorporeal membrane oxygenation (ECMO) support. We aimed to evaluate the safety of ultra-low-field portable MRI (ULF-pMRI) and the frequency and types of ABI observed during ECMO support. Methods: We conducted a multicenter prospective observational study (NCT05469139) at two academic tertiary centers (August 2022-November 2023). Primary outcomes were safety and validation of ULF-pMRI in ECMO, defined as exam completion without adverse events (AEs); secondary outcomes were ABI frequency and type. Results: ULF-pMRI was performed in 50 patients with 34 (68%) on venoarterial (VA)-ECMO (11 central; 23 peripheral) and 16 (32%) with venovenous (VV)-ECMO (9 single lumen; 7 double lumen). All patients were imaged successfully with ULF-pMRI, demonstrating discernible intracranial pathologies with good quality. AEs occurred in 3 (6%) patients (2 minor; 1 serious) without causing significant clinical issues. ABI was observed in ULF-pMRI scans for 22 patients (44%): ischemic stroke (36%), intracranial hemorrhage (6%), and hypoxic-ischemic brain injury (4%). Of 18 patients with both ULF-pMRI and head CT (HCT) within 24 hours, ABI was observed in 9 patients with 10 events: 8 ischemic (8 observed on ULF-oMRI, 4 on HCT) and 2 hemorrhagic (1 observed on ULF-pMRI, 2 on HCT). Conclusions: ULF-pMRI was shown to be safe and valid in ECMO patients across different ECMO cannulation strategies. The incidence of ABI was high, and ULF-pMRI may more sensitive to ischemic ABI than HCT. ULF-pMRI may benefit both clinical care and future studies of ECMO-associated ABI.
The effectiveness of diabetes self-management education intervention on glycaemic control and cardiometabolic risk in adults with type 2 diabetes in low- and middle-income countries: A systematic review and meta-analysis

Diabetes mellitus (DM) poses a significant challenge to public health. Effective diabetes self-management education (DSME) interventions may play a pivotal role in the care of people with type 2 diabetes mellitus (T2DM) in low- and middle-income countries (LMICs). A specific up-to-date systematic review is needed to assess the effect of DSME interventions on glycaemic control, cardiometabolic risk, self-management behaviours, and psychosocial well-being among T2DM across LMICs. The MEDLINE, Embase, CINAHL, Global Health, and Cochrane databases were searched on 02 August 2022 and then updated on 10 November 2023 for published randomised controlled trials (RCTs) and quasi-experimental studies. The quality of the studies was assessed, and a random-effect model was used to estimate the pooled effect of diabetes DSME intervention. Heterogeneity (I2) was tested, and subgroup analyses were performed. Egger’s regression test and funnel plots were used to examine publication bias. The risk of bias of the included studies was assessed using the Cochrane risk-of-bias tool for randomized trial (RoB 2). The overall assessment of the evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach. A total of 5893 articles were retrieved, and 44 studies (n = 11838) from 21 LMICs met the inclusion criteria. Compared with standard care, pooled analysis showed that DSME effectively reduced the HbA1c level by 0.64% (95% CI: 0.45% to 0.83%) and 1.27% (95% CI: -0.63% to 3.17%) for RCTs and quasi-experimental design studies, respectively. Further, the findings showed an improvement in cardiometabolic risk reduction, diabetes self-management behaviours, and psychosocial well-being. This review suggests that ongoing support alongside individualised face-to-face intervention delivery is favourable for improving overall T2DM management in LMICs, with a special emphasis on countries in the lowest income group.
Analysis of winter diet in Guizhou golden monkey (Rhinopithecus brelichi) using DNA metabarcoding data

Abstract The Guizhou golden monkey (Rhinopithecus brelichi) is a critically endangered wildlife species, and understanding its diet composition may be useful for assessing its feeding strategies. DNA metabarcoding was used to determine the dietary diversity of R. brelichi. DNA was extracted from 31 faecal samples and amplified chloroplast rbcL and mitochondrial COI DNA was sequenced using the Illumina NovaSeq platform. A comparative analysis of the sequences revealed that the five most abundant plant genera were Magnolia, Morinda, Viburnum, Tetradium and Eurya. In winter, R. brelichi mostly consumed shrubs, herbs and shrubs/trees according to the habit of plant genera with higher abundances comparatively. The five most abundant families in animal diet were Psychodidae, Trichinellidae, Staphylinidae, Scarabaeidae and Trichoceridae. This study is the first to show the composition of the winter animal diets of R. brelichi based on DNA metabarcoding. These results provide an important basis for understanding the diet of wild R. brelichi, which inhabits only the Fanjingshan National Nature Reserve, China.
Keeping distance or getting closer: How others’ emotions shape approach-avoidance postural behaviors and preferred interpersonal distance

Understanding the influence of emotions on social interactions is important for a global understanding of the dynamics of human behavior. In this study, we investigated the interplay between emotions, spontaneous approach or avoidance tendencies, and the regulation of interpersonal distance. Fifty-seven healthy adults participated in a three-part experiment involving exposure to approaching or withdrawing emotional faces (neutral, happy, sad, fearful, disgusted, angry). The sequence began with an initial computerized stop-distance task, followed by a postural task in which participants’ approach or avoidance tendencies were quantified via center of pressure (CoP-Y) displacements on a force platform, and concluded with a final computerized stop-distance task. Our findings revealed a gradient in postural responses, with the most forward CoP-Y displacements for neutral and happy faces, indicative of approach tendencies. These were followed by lesser forward displacements for sad and fearful faces, and most pronounced backward displacements for disgusted and angry faces, indicating avoidance. Furthermore, we observed modulations in participants’ preferred interpersonal distance based on emotional cues, with neutral and happy faces associated with shorter distances, and disgusted and angry faces linked to larger distances. Despite these similar results, no direct correlation was found between CoP-Y and preferred interpersonal distance, underscoring a dissociation between spontaneous and voluntary social behaviors. These results contribute to a better understanding of how emotional expressions shape social interactions and underscore the importance of considering emotional cues, postural action tendencies, and interpersonal distance in facilitating successful social interactions.
A computational model to characterize the time-course of response to rapid antidepressant therapies

Our objective is to propose a method capable of disentangling the magnitude, the speed, and the duration or decay rate of the time course of response to rapid antidepressant therapies. To this end, we introduce a computational model of the time course of response to a single treatment with a rapid antidepressant. Numerical simulation is used to evaluate whether model parameters can be accurately estimated from observed data. Finally, we compare our computational modelling-based approach with linear mixed effects modelling in terms of their ability to detect changes in the magnitude and time-course of response to rapid antidepressant therapies in simulated randomized trials. Simulation experiments show that the parameters of our computational model can be accurately recovered using nonlinear least squares. Parameter estimation accuracy is stable over noise levels reaching as high as 25% of the true antidepressant effect magnitude. Comparison of our approach to mixed effects modelling using simulated randomized controlled trial data demonstrates an inability of linear mixed models to disentangle effect magnitude and time course, while our computational model accurately separates these response components. Our modelling approach may accurately identify the (A) magnitude, (B) speed, and (C) durability or decay rate of response to rapid antidepressant therapies. Future studies should fit this model to data from real clinical trials, and use resulting parameter estimates to uncover predictors and causes of different elements of the temporal course of antidepressant response.
Endometrial receptivity defects MUC-1 and COX-2 polymorphisms in endometriosis

The endometrium produces MUCIN-1 (MUC-1) and cyclooxygenase-2 (COX-2), which are essential for implantation. MUC-1 is required for adhesion, while COX-2 is necessary for decidualization. Variations or polymorphisms in MUC-1 and COX-2 can lead to changes in endometrial receptivity. This study investigated the relationship between MUC-1 and COX-2 polymorphisms and endometrial receptivity in endometriosis patients. Blood DNA samples were collected from 35 patients with endometriosis and 32 healthy patients between days 19 to 24 of their menstrual cycle (secretory phase). MUC-1 polymorphism was determined using the Amplification Refractory Mutation System (ARMS), and COX-2 gene polymorphism was assessed using Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP). The frequency distribution of gene polymorphisms between the two groups was compared using bivariate analysis. There were seven genotypic combinations of MUC-1 and COX-2: AAGC; AAGG; GACC; GAGC; GAGG; GGGC; GGGG. The AAGC genotype combination test was significant, with an OR=6.43 (95% CI:1.09–7.62) and p=0.01. In conclusion, combining MUC-1 and COX-2 (AAGC) genotypes results in endometrial receptivity defects in endometriosis.
Biomarkers That Predict Crohn’s Disease Outcomes

Abstract Crohn’s disease (CD), a chronic inflammatory condition of the digestive tract, poses significant challenges in terms of disease prognosis and treatment selection. Biomarkers have the potential to predict CD outcomes and guide clinical decision-making. This review aims to summarize the current literature on promising biomarkers associated with CD outcomes and their potential clinical implications. The identification of reliable biomarkers for CD outcomes is of paramount importance in tailoring treatment strategies, monitoring disease activity, and predicting the risk of complications. Clinical prognostic factors traditionally used to assess disease severity, and the likelihood of complications have limitations in accuracy and predictive value. Thus, there is a need for more precise biomarkers, particularly in newly diagnosed and treatment-naive patients. Pharmacogenomic markers, such as TPMT and NUDT15 polymorphisms, have been utilized to identify patients at risk of adverse events with thiopurine therapy. Several biomarkers, including HLA haplotypes, oncostatin M expression, and transcriptomic profiles, have shown associations with response to anti-TNF therapy. Confocal laser endomicroscopy and single-cell analyses hold promise in predicting treatment response to specific therapies. The identification of biomarkers associated with post-operative recurrence in CD is crucial, as it could lead to changes in management algorithms. Several promising microbiome signatures and proteomic profiles have been identified. In conclusion, biomarkers have the potential to revolutionize the management of CD by providing valuable prognostic information and guiding treatment decisions. However, further research and validation are necessary to establish their clinical utility and integration into routine practice.