Miracle-dz/UniFAR-FTUs huggingface dataset:
Why Do Users of Online Mental Health Communities Get Likes and Reposts: A Combination of Text Mining and Empirical Analysis

An online community is one of the important ways for people with mental disorders to receive assistance and obtain support. This study aims to help users with mental disorders to obtain more support and communication through online communities, and to provide community managers with the possible influence mechanisms based on the information adoption model. We obtained a total of 49,047 posts of an online mental health communities in China, over a 40-day period. Then we used a combination of text mining and empirical analysis. Topic and sentiment analysis were used to derive the key variables—the topic of posts that the users care about most, and the emotion scores contained in posts. We then constructed a theoretical model based on the information adoption model. As core independent variables of information quality, on online mental health communities, the topic of social experience in posts (0.368 ***), the topic of emotional expression (0.353 ***), and the sentiment contained in the text (0.002 *) all had significant positive relationships with the number of likes and reposts. This study found that the users of online mental health communities are more attentive to the topics of social experience and emotional expressions, while they also care about the non-linguistic information. This study highlights the importance of helping community users to post on community-related topics, and gives administrators possible ways to help users gain the communication and support they need.
Prevalence and Clustering of Cardiovascular Risk Factors among Medical Staff in Northeast China

Background: The clustering of cardiovascular disease (CVD) risk factors has become a major public health challenge worldwide. Although many studies have investigated CVD risk factor clusters, little is known about their prevalence and clustering among medical staff in Northeast China. This study aimed to estimate the prevalence and clustering of CVD risk factors and to investigate the association between relevant characteristics and the clustering of CVD risk factors among medical staff in Northeast China. Methods: A cross-sectional survey of 3720 medical staff from 93 public hospitals in Jilin Province was used in this study. Categorical variables were presented as percentages and were compared using the χ2 test. Multiple logistic regression analysis was used to evaluate the association between relevant characteristics and the clustering of CVD risk factors. Results: The prevalence of hypertension, diabetes, dyslipidemia, being overweight, smoking, and drinking were 10.54%, 3.79%, 17.15%, 39.84%, 9.87%, and 21.75%, respectively. Working in a general hospital, male, and age group 18–44 years were more likely to have 1, 2, and ≥3 CVD risk factors, compared with their counterparts. In particular, compared with being a doctor, being a nurse or medical technician was less likely to have 1, 2, and ≥3 CVD risk factors only in general hospitals. Conclusions: The findings suggest that medical staff of general hospitals, males, and older individuals have a high chance associated with CVD risk factor clustering and that more effective interventions should be undertaken to reduce the prevalence and clustering of CVD risk factors, especially among older male doctors who work in general hospitals.
Mammalian Cell-Free System Recapitulates the Early Events of Post-Fertilization Sperm Mitophagy

Propagation of paternal sperm-contributed mitochondrial genes, resulting in heteroplasmy, is seldom observed in mammals due to post-fertilization degradation of sperm mitochondria, referred to as sperm mitophagy. Whole organelle sperm mitochondrion degradation is thought to be mediated by the interplay between the ubiquitin-proteasome system (UPS) and the autophagic pathway (Song et al., Proc. Natl. Acad. Sci. USA, 2016). Both porcine and primate post-fertilization sperm mitophagy rely on the ubiquitin-binding autophagy receptor, sequestosome 1 (SQSTM1), and the proteasome-interacting ubiquitinated protein dislocase, valosin-containing protein (VCP). Consequently, we anticipated that sperm mitophagy could be reconstituted in a cell-free system consisting of permeabilized mammalian spermatozoa co-incubated with porcine oocyte extracts. We found that SQSTM1 was detected in the midpiece/mitochondrial sheath of the sperm tail after, but not before, co-incubation with oocyte extracts. VCP was prominent in the sperm mitochondrial sheath both before and after the extract co-incubation and was also detected in the acrosome and postacrosomal sheath and the subacrosomal layer of the spermatozoa co-incubated with extraction buffer as control. Such patterns are consistent with our previous observation of SQSTM1 and VCP associating with sperm mitochondria inside the porcine zygote. In addition, it was observed that sperm head expansion mimicked the early stages of paternal pronucleus development in a zygote during prolonged sperm-oocyte extract co-incubation. Treatment with anti-SQSTM1 antibody during extract co-incubation prevented ooplasmic SQSTM1 binding to sperm mitochondria. Even in an interspecific cellular environment encompassing bull spermatozoa and porcine oocyte extract, ooplasmic SQSTM1 was recruited to heterospecific sperm mitochondria. Complementary with the binding of SQSTM1 and VCP to sperm mitochondria, two sperm-borne pro-mitophagy proteins, parkin co-regulated gene product (PACRG) and spermatogenesis associated 18 (SPATA18), underwent localization changes after extract coincubation, which were consistent with their degradation observed inside fertilized porcine oocytes. These results demonstrate that the early developmental events of post-fertilization sperm mitophagy observed in porcine zygote can be reconstituted in a cell-free system, which could become a useful tool for identifying additional molecules that regulate mitochondrial inheritance in mammals.
Cudrania tricuspidata Combined with Lacticaseibacillus rhamnosus Modulate Gut Microbiota and Alleviate Obesity-Associated Metabolic Parameters in Obese Mice

The aim of the presented study was to investigate the synbiotic effects of L. rhamnosus 4B15 and C. tricuspidata extract administration on the gut microbiota and obesity-associated metabolic parameters in diet-induced obese mice. Thirty-one 6-week-old male C57BL/N6 mice were divided into five diet groups: normal diet (ND, n = 7) group; high-fat diet (HFD, n = 6) group; probiotic (PRO, n = 5) group; prebiotic (PRE, n = 7) group; and synbiotic (SYN, n = 6) group. After 10 weeks, the percent of fat mass, serum triglyceride, and ALT levels were significantly reduced in SYN-fed obese mice, compared with other treatments. SYN treatment also modulated the abundance of Desulfovibrio, Dorea, Adlercreutzia, Allobaculum, Coprococcus, unclassified Clostridiaceae, Lactobacillus, Helicobacter, Flexispira, Odoribacter, Ruminococcus, unclassified Erysipelotrichaceae, and unclassified Desulfovibrionaceae. These taxa showed a strong correlation with obesity-associated indices. Lastly, the SYN-supplemented diet upregulated metabolic pathways known to improve metabolic health. Further investigations are needed to understand the mechanisms driving the synbiotic effect of C. tricuspidata and L. rhamnosus 4B15.
A Rapid Method for Performing a Multivariate Optimization of Phage Production Using the RCCD Approach

Bacteriophages can be used in various applications, from the classical approach as substitutes for antibiotics (phage therapy) to new biotechnological uses, i.e., as a protein delivery vehicle, a diagnostic tool for specific strains of bacteria (phage typing), or environmental bioremediation. The demand for bacteriophage production increases daily, and studies that improve these production processes are necessary. This study evaluated the production of a T4-like bacteriophage vB_EcoM-UFV09 (an E. coli-infecting phage with high potential for reducing environmental biofilms) in seven types of culture media (Luria–Bertani broth and the M9 minimal medium with six different carbon sources) employing four cultivation variables (temperature, incubation time, agitation, and multiplicity of infection). For this purpose, the rotatable central composite design (RCCD) methodology was used, combining and comparing all parameters to determine the ideal conditions for starting to scale up the production process. We used the RCCD to set up the experimental design by combining the cultivation parameters in a specific and systematic way. Despite the high number of conditions evaluated, the results showed that when specific conditions were utilized, viral production was effective even when using a minimal medium, such as M9/glucose, which is less expensive and can significantly reduce costs during large-scale phage production.
Training and Validating a Machine Learning Model for the Sensor-Based Monitoring of Lying Behavior in Dairy Cows on Pasture and in the Barn

Simple Summary There are various systems available for health monitoring and heat detection in dairy cows. By continuously monitoring different behavioral patterns (e.g., lying, ruminating, and feeding), these systems detect behavioral changes linked to health disorders and estrous. Most of the systems were developed for cows kept indoors, and only a few systems are available for pasture-based farms. The systems developed for the barn failed to detect the targeted behavior and thereby its changes on the pasture and vice versa. Therefore, our goal was to train and validate a machine learning model for the automated prediction of lying behavior in dairy cows kept on pastures, as well as indoors. Data collection was conducted on three dairy farms where cows were equipped with the collar-based prototype of the monitoring system and recorded with cameras in parallel. The derived dataset was used to develop the machine learning model. The model performed well in predicting lying behavior in dairy cows both on the pasture and in the barn. Therefore, the building of the model presents a successful first step towards the development of a monitoring system for dairy cows kept on pasture and in the barn. Abstract Monitoring systems assist farmers in monitoring the health of dairy cows by predicting behavioral patterns (e.g., lying) and their changes with machine learning models. However, the available systems were developed either for indoors or for pasture and fail to predict the behavior in other locations. Therefore, the goal of our study was to train and evaluate a model for the prediction of lying on a pasture and in the barn. On three farms, 7–11 dairy cows each were equipped with the prototype of the monitoring system containing an accelerometer, a magnetometer and a gyroscope. Video observations on the pasture and in the barn provided ground truth data. We used 34.5 h of datasets from pasture for training and 480.5 h from both locations for evaluating. In comparison, random forest, an orientation-independent feature set with 5 s windows without overlap, achieved the highest accuracy. Sensitivity, specificity and accuracy were 95.6%, 80.5% and 87.4%, respectively. Accuracy on the pasture (93.2%) exceeded accuracy in the barn (81.4%). Ruminating while standing was the most confused with lying. Out of individual lying bouts, 95.6 and 93.4% were identified on the pasture and in the barn, respectively. Adding a model for standing up events and lying down events could improve the prediction of lying in the barn.
A Recombinant Fusion Construct between Human Serum Albumin and NTPDase CD39 Allows Anti-Inflammatory and Anti-Thrombotic Coating of Medical Devices

Medical devices directly exposed to blood are commonly used to treat cardiovascular diseases. However, these devices are associated with inflammatory reactions leading to delayed healing, rejection of foreign material or device-associated thrombus formation. We developed a novel recombinant fusion protein as a new biocompatible coating strategy for medical devices with direct blood contact. We genetically fused human serum albumin (HSA) with ectonucleoside triphosphate diphosphohydrolase-1 (CD39), a promising anti-thrombotic and anti-inflammatory drug candidate. The HSA-CD39 fusion protein is highly functional in degrading ATP and ADP, major pro-inflammatory reagents and platelet agonists. Their enzymatic properties result in the generation of AMP, which is further degraded by CD73 to adenosine, an anti-inflammatory and anti-platelet reagent. HSA-CD39 is functional after lyophilisation, coating and storage of coated materials for up to 8 weeks. HSA-CD39 coating shows promising and stable functionality even after sterilisation and does not hinder endothelialisation of primary human endothelial cells. It shows a high level of haemocompatibility and diminished blood cell adhesion when coated on nitinol stents or polyvinylchloride tubes. In conclusion, we developed a new recombinant fusion protein combining HSA and CD39, and demonstrated that it has potential to reduce thrombotic and inflammatory complications often associated with medical devices directly exposed to blood.
TGFβ-Neurotrophin Interactions in Heart, Retina, and Brain

Ischemic insults to the heart and brain, i.e., myocardial and cerebral infarction, respectively, are amongst the leading causes of death worldwide. While there are therapeutic options to allow reperfusion of ischemic myocardial and brain tissue by reopening obstructed vessels, mitigating primary tissue damage, post-infarction inflammation and tissue remodeling can lead to secondary tissue damage. Similarly, ischemia in retinal tissue is the driving force in the progression of neovascular eye diseases such as diabetic retinopathy (DR) and age-related macular degeneration (AMD), which eventually lead to functional blindness, if left untreated. Intriguingly, the easily observable retinal blood vessels can be used as a window to the heart and brain to allow judgement of microvascular damages in diseases such as diabetes or hypertension. The complex neuronal and endocrine interactions between heart, retina and brain have also been appreciated in myocardial infarction, ischemic stroke, and retinal diseases. To describe the intimate relationship between the individual tissues, we use the terms heart-brain and brain-retina axis in this review and focus on the role of transforming growth factor β (TGFβ) and neurotrophins in regulation of these axes under physiologic and pathologic conditions. Moreover, we particularly discuss their roles in inflammation and repair following ischemic/neovascular insults. As there is evidence that TGFβ signaling has the potential to regulate expression of neurotrophins, it is tempting to speculate, and is discussed here, that cross-talk between TGFβ and neurotrophin signaling protects cells from harmful and/or damaging events in the heart, retina, and brain.
Effect of Triweekly Interdental Brushing on Bleeding Reduction in Adults: A Six-Month Retrospective Study

This study assessed the effect of triweekly interdental brushing for a period of 6 months using the bleeding on full-mouth interdental brushing (BOFIB) index. All participants answered questionnaires and were instructed to clean all interdental areas using an interdental brush at least every second day. A dentist assessed the BOFIB index of 28 participants at baseline and at 3- and 6-month follow-up visits. We stratified the participants into three groups: IB-NN, those who used the interdental brush less than three times per week both before and after this program; IB-NY, those who used the interdental brush less than three times per week before the program but at least three times per week after the program; and IB-YY, those who used it at least three times per week both before and after the program. Owing to the weekly number of interdental brushings, the IB-YY and the other two groups showed a significant difference at baseline. At the 6-month follow-up, the weekly number of interdental brushings led to a significant difference between the IB-NN and the other two groups. The BOFIB index was lower among the compliant participants than among the noncompliant participants after 3 and 6 months of triweekly interdental brushing.
Multiplex Screening for Interacting Compounds in Paediatric Acute Myeloid Leukaemia

Paediatric acute myeloid leukaemia (AML) is a heterogeneous disease characterised by the malignant transformation of myeloid precursor cells with impaired differentiation. Standard therapy for paediatric AML has remained largely unchanged for over four decades and, combined with inadequate understanding of the biology of paediatric AML, has limited the progress of targeted therapies in this cohort. In recent years, the search for novel targets for the treatment of paediatric AML has accelerated in parallel with advanced genomic technologies which explore the mutational and transcriptional landscape of this disease. Exploiting the large combinatorial space of existing drugs provides an untapped resource for the identification of potential combination therapies for the treatment of paediatric AML. We have previously designed a multiplex screening strategy known as Multiplex Screening for Interacting Compounds in AML (MuSICAL); using an algorithm designed in-house, we screened all pairings of 384 FDA-approved compounds in less than 4000 wells by pooling drugs into 10 compounds per well. This approach maximised the probability of identifying new compound combinations with therapeutic potential while minimising cost, replication and redundancy. This screening strategy identified the triple combination of glimepiride, a sulfonylurea; pancuronium dibromide, a neuromuscular blocking agent; and vinblastine sulfate, a vinca alkaloid, as a potential therapy for paediatric AML. We envision that this approach can be used for a variety of disease-relevant screens allowing the efficient repurposing of drugs that can be rapidly moved into the clinic.
Immune Checkpoints in Cancers: From Signaling to the Clinic

Simple Summary Immune checkpoint therapies are treatments used to fight cancers by reactivating a patient’s own immune system. Melanoma was the first cancer to benefit from these treatments. Despite a clear benefit for patients and the existence of long responders, most patients fail to respond or develop resistance to these treatments. In this review, we discuss immune checkpoint signaling in the different immune cells with their biological consequences and summarize new immune checkpoint therapies that are under investigation in clinical trials or in development to bypass resistances and to improve the outcome of these therapies. Abstract The immune system is known to help fight cancers. Ten years ago, the first immune checkpoint inhibitor targeting CTLA4 was approved by the FDA to treat patients with metastatic melanoma. Since then, immune checkpoint therapies have revolutionized the field of oncology and the treatment of cancer patients. Numerous immune checkpoint inhibitors have been developed and tested, alone or in combination with other treatments, in melanoma and other cancers, with overall clear benefits to patient outcomes. However, many patients fail to respond or develop resistance to these treatments. It is therefore essential to decipher the mechanisms of action of immune checkpoints and to understand how immune cells are affected by signaling to be able to understand and overcome resistance. In this review, we discuss the signaling and effects of each immune checkpoint on different immune cells and their biological and clinical relevance. Restoring the functionality of T cells and their coordination with other immune cells is necessary to overcome resistance and help design new clinical immunotherapy strategies. In this respect, NK cells have recently been implicated in the resistance to anti-PD1 evoked by a protein secreted by melanoma, ITGBL1. The complexity of this network will have to be considered to improve the efficiency of future immunotherapies and may lead to the discovery of new immune checkpoints.
Glioblastoma Surgery Imaging–Reporting and Data System: Validation and Performance of the Automated Segmentation Task

Simple Summary Neurosurgical decisions for patients with glioblastoma depend on visual inspection of a preoperative MR scan to determine the tumor characteristics. To avoid subjective estimates and manual tumor delineation, automatic methods and standard reporting are necessary. We compared and extensively assessed the performances of two deep learning architectures on the task of automatic tumor segmentation. A total of 1887 patients from 14 institutions, manually delineated by a human rater, were compared to automated segmentations generated by neural networks. The automated segmentations were in excellent agreement with the manual segmentations, and external validity, as well as generalizability were demonstrated. Together with automatic tumor feature computation and standardized reporting, our Glioblastoma Surgery Imaging Reporting And Data System (GSI-RADS) exhibited the potential for more accurate data-driven clinical decisions. The trained models and software are open-source and open-access, enabling comparisons among surgical cohorts, multicenter trials, and patient registries. Abstract For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime.