Miracle-dz/UniFAR-FTUs huggingface dataset:
Two–weight Criteria For Potentials With Product Kernels On The Cone Of Non-increasing Functions

Necessary and sucient conditions governing one and two weight inequalities for one-sided strong fractional maximal operators, one-sided and Riesz potentials with product kernels are established on the cone of non-increasing functions.From the two{ weight results it follows criteria for the trace inequality Lpdec(Rn+)! Lq(v;Rn+) boundedness for these operators, where v, in general, is not product of one-dimensional weights.Various type of two-weight necessary and sucient conditions for the discrete Riemann{Liouville transform with product kernels are also established. The most of the derived two-weight results (continuous and discrete) are new even for potentials with single kernels.
Iron Rim Lesions as a Specific and Prognostic Biomarker of Multiple Sclerosis: 3T-Based Susceptibility-Weighted Imaging

This study aimed to identify the clinical significance of iron rim lesions (IRLs) in distinguishing multiple sclerosis (MS) from other central nervous system (CNS) demyelinating diseases, determine the relationship between IRLs and disease severity, and understand the long-term dynamic changes in IRLs in MS. We retrospectively evaluated 76 patients with CNS demyelinating diseases. CNS demyelinating diseases were classified into three groups: MS (n = 30), neuromyelitis optica spectrum disorder (n = 23), and other CNS demyelinating diseases (n = 23). MRI images were obtained using conventional 3T MRI including susceptibility-weighted imaging. Sixteen of 76 patients (21.1%) had IRLs. Of the 16 patients with IRLs, 14 were in the MS group (87.5%), indicating that IRLs were significantly specific for MS. In the MS group, patients with IRLs had a significantly higher number of total WMLs, experienced more frequent recurrence, and were treated more with second-line immunosuppressive agents than were patients without IRLs. In addition to IRLs, T1-blackhole lesions were observed more frequently in the MS group than in the other groups. IRLs are specific for MS and could represent a reliable imaging biomarker to improve the diagnosis of MS. Additionally, the presence of IRLs seems to reflect more severe disease progression in MS.
Natural Bacterial and Fungal Peptides as a Promising Treatment to Defeat Lung Cancer Cells

Despite the increasing availability of modern treatments, including personalized therapies, there is a strong need to search for new drugs that will be effective in the fight against cancer. The chemotherapeutics currently available to oncologists do not always yield satisfactory outcomes when used in systemic treatments, and patients experience burdensome side effects during their application. In the era of personalized therapies, doctors caring for non-small cell lung cancer (NSCLC) patients have been given a powerful weapon, namely molecularly targeted therapies and immunotherapies. They can be used when genetic variants of the disease qualifying for therapy are diagnosed. These therapies have contributed to the extension of the overall survival time in patients. Nevertheless, effective treatment may be hindered in the case of clonal selection of tumor cells with acquired resistance mutations. The state-of-the-art therapy currently used in NSCLC patients is immunotherapy targeting the immune checkpoints. Although it is effective, some patients have been observed to develop resistance to immunotherapy, but its cause is still unknown. Personalized therapies extend the lifespan and time to cancer progression in patients, but only those with a confirmed marker qualifying for the treatment (gene mutations/rearrangements or PD-L1 expression on tumor cells) can benefit from these therapies. They also cause less burdensome side effects than chemotherapy. The article is focused on compounds that can be used in oncology and produce as few side effects as possible. The search for compounds of natural origin, e.g., plants, bacteria, or fungi, exhibiting anticancer properties seems to be a good solution. This article is a literature review of research on compounds of natural origin that can potentially be used as part of NSCLC therapies.
Effects of Different Types of Recombinant SARS-CoV-2 Spike Protein on Circulating Monocytes’ Structure

This study investigated the biological effects on circulating monocytes after challenge with SARS-CoV-2 recombinant spike protein. Whole blood collected from seven ostensibly healthy healthcare workers was incubated for 15 min with 2 and 20 ng/mL final concentration of recombinant spike protein of Ancestral, Alpha, Delta, and Omicron variants. Samples were analyzed with Sysmex XN and DI-60 analyzers. Cellular complexity (i.e., the presence of granules, vacuoles and other cytoplasmic inclusions) increased in all samples challenged with the recombinant spike protein of the Ancestral, Alpha, and Delta variants, but not in those containing Omicron. The cellular content of nucleic acids was constantly decreased in most samples, achieving statistical significance in those containing 20 ng/mL of Alpha and Delta recombinant spike proteins. The heterogeneity of monocyte volumes significantly increased in all samples, achieving statistical significance in those containing 20 ng/mL of recombinant spike protein of the Ancestral, Alpha and Delta variants. The monocyte morphological abnormalities after spike protein challenge included dysmorphia, granulation, intense vacuolization, platelet phagocytosis, development of aberrant nuclei, and cytoplasmic extrusions. The SARS-CoV-2 spike protein triggers important monocyte morphological abnormalities, more evident in cells challenged with recombinant spike protein of the more clinically severe Alpha and Delta variants.
Lactobacillus casei and Its Supplement Alleviate Stress-Induced Depression and Anxiety in Mice by the Regulation of BDNF Expression and NF-κB Activation

Stress-induced depression and anxiety (DA) are closely connected to gastrointestinal inflammation and dysbiosis, which can suppress brain-derived neurotrophic factor (BDNF) in the brain. Herein, we isolated the BDNF expression-inducing probiotics Lactobacillus casei HY2782 and Bifidobacterium lactis HY8002 in lipopolysaccharide-stimulated SH-SY5Y cells. Then, we investigated the effects of HY2782, HY8002, anti-inflammatory L-theanine, and their supplement (PfS, probiotics-fermented L-theanine-containing supplement) on DA in mice exposed to restraint stress (RS) or the fecal microbiota of patients with inflammatory bowel disease and depression (FMd). Oral administration of HY2782, HY8002, or L-theanine alleviated RS-induced DA-like behaviors. They also decreased RS-induced hippocampal interleukin (IL)-1β and IL-6 levels, as well as NF-κB-positive cell numbers, blood corticosterone level, and colonic IL-1β and IL-6 levels and NF-κB-positive cell numbers. L-theanine more potently suppressed DA-like behaviors and inflammation-related marker levels than probiotics. However, these probiotics more potently increased RS-suppressed hippocampal BDNF level and BDNF+NeuN+ cell numbers than L-theanine. Furthermore, HY2782 and HY8002 suppressed RS-increased Proteobacteria and Verrucomicrobia populations in gut microbiota. In particular, they increased Lachnospiraceae and Lactobacillacease populations, which are closely positively associated with hippocampal BDNF expression, and suppressed Sutterellaceae, Helicobacteriaceae, Akkermansiaceae, and Enterobacteriaceae populations, which are closely positively associated with hippocampal IL-1β expression. HY2782 and HY8002 potently alleviated FMd-induced DA-like behaviors and increased FMd-suppressed BDNF, serotonin levels, and BDNF-positive neuronal cell numbers in the brain. They alleviated blood corticosterone level and colonic IL-1β α and IL-6 levels. However, L-theanine weakly, but not significantly, alleviated FMd-induced DA-like behaviors and gut inflammation. BDNF expression-inducing probiotic (HY2782, HY8002, Streptococcus thermophilus, and Lactobacillus acidophilus)-fermented and anti-inflammatory L-theanine-containing supplement PfS alleviated DA-like behaviors, inflammation-related biomarker levels, and gut dysbiosis more than probiotics or L-theanine. Based on these findings, a combination of BDNF expression-inducing probiotics with anti-inflammatory L-theanine may additively or synergistically alleviate DA and gut dysbiosis by regulating gut microbiota-mediated inflammation and BDNF expression, thereby being beneficial for DA.
Humans can detect axillary odor cues of an acute respiratory infection in others

Abstract Background and objectives Body odor conveys information about health status to conspecifics and influences approach-avoidance behaviors in animals. Experiments that induce sickness in otherwise healthy individuals suggest that humans too can detect sensory cues to infection in others. Here, we investigated whether individuals could detect through smell a naturally occurring acute respiratory infection in others and whether sickness severity, measured via body temperature and sickness symptoms, was associated with the accuracy of detection. Methodology Body odor samples were collected from 20 donors, once while healthy and once while sick with an acute respiratory infection. Using a double-blind, two-alternative forced-choice method, 80 raters were instructed to identify the sick body odor from paired sick and healthy samples (i.e. 20 pairs). Results Sickness detection was significantly above chance, although the magnitude of the effect was low (56.7%). Raters’ sex and disgust sensitivity were not associated with the accuracy of sickness detection. However, we find some indication that greater change in donor body temperature, but not sickness symptoms, between sick and healthy conditions improved sickness detection accuracy. Conclusion and implications Our findings suggest that humans can detect individuals with an acute respiratory infection through smell, albeit only slightly better than chance. Humans, similar to other animals, are likely able to use sickness odor cues to guide adaptive behaviors that decrease the risk of contagion, such as social avoidance. Further studies should determine how well humans can detect specific infections through body odor, such as Covid-19, and how multisensory cues to infection are used simultaneously.
The Work of Nurses in Primary Health Care: Crossings of the New Public Management

The literature in the field of health management mentions a concept called new public management (NPM), introduced in Brazil and France at the end of the 20th century. The objective of the study was to analyze the repercussions of the work of nurses in primary health care in Brazil and France under the influence of NPM. This is an excerpt of a double-titled thesis, which is a research intervention with nurses from two Brazilian states and five French departments. Data were produced between February 2019 and July 2021. The public policy Health on the Hour acted as an institutional transducer, provoking a reduction in access and producing effects on professional practices. In both countries, NPM amplified the predominance of technical and quantifiable acts, the focus on individual care, and the loss of autonomy. Nurses reported insurmountable situations, using the metaphor “Sophie’s choice”. The results showed that making dilemmatic decisions has been the daily routine of nurses, which has not resulted in debureaucratization and higher quality of care.
Job Seekers’ Burnout and Engagement: A Qualitative Study of Long-Term Unemployment in Italy

Long-term unemployment has major consequences from an economic, physical and psychosocial perspective. Several authors have pointed out that the search for employment is in itself work, which can generate feelings of exhaustion of psychophysical energies, cynicism and disinvestment, as well as a sense of ineffectiveness to the point of complete disillusion. The construct of burnout can be used to describe this psychological process. This study evaluated the burnout and engagement dimensions in individuals searching for work for a long time, from a qualitative perspective. Fifty-six semi-structured interviews were conducted with a sample of long-term unemployed job seekers (Sardinia, Italy), based on Maslach’s model of burnout (exhaustion, cynicism, effectiveness in job search). The answers to the semi-structured interviews were processed through T-Lab, a semi-automatic textual analysis software. Four thematic cores emerged: exhaustion vs. engagement, cynicism vs. trust, inefficacy vs. efficacy in job search and disillusion vs. hope. This result is consistent with the four-dimensional theoretical model of burnout, originally proposed by Edelwich and Brodsky, recently taken up by Santinello, and framed as the opposite of engagement, as shown in the JD-R model. This study highlights that burnout can describe the psychosocial experiences of long-term unemployed job seekers.
Analysis of Functional Neuroplastic Changes in the Cortical Language System in Relation to Different Growth Patterns of Glioblastoma

The interpretation of fMRI data in glioblastoma (GB) is challenging as these tumors exhibit specific hemodynamic processes which, together with malignancy, tumor volume and proximity to eloquent cortex areas, may lead to misinterpretations of fMRI signals. The aim of this study was to investigate if different radiologically defined GB tumor growth patterns may also influence the fMRI signal, activation pattern and functional connectivity differently. Sixty-four patients with left-hemispheric glioblastoma were included and stratified according to their radiologically defined tumor growth pattern into groups with a uniform (U-TGP) or diffuse tumor growth pattern (D-TGP). Task-based fMRI data were analyzed using SPM12 with the marsbar, LI and CONN toolboxes. The percent signal change and the laterality index were analyzed, as well as functional connectivity between 23 selected ROIs. Comparisons of both patient groups showed only minor non-significant differences, indicating that the tumor growth pattern is not a relevant influencing factor for fMRI signal. In addition to these results, signal reductions were found in areas that were not affected by the tumor underlining that a GB is not a localized but rather a systemic disease affecting the entire brain.
Physics Learning Using Guided Inquiry Models Based on Virtual Laboratories and Real Laboratories to Improve Learning

The lack of teacher creativity in testing various learning models and methods makes a significant contribution to the stigma of boredom and boredom in studying physics. The right learning model is needed so that it can help students improve learning outcomes, especially learning physics. The purpose of this study was to analyze the influence the experimental method combined with the virtual and real experiment-based guided inquiry learning model had on the physics learning outcomes. The research samples were class XI IPA 1 (32 people) for the experimental group and class XI IPA 2 (32 people) for the control group. In this study, the cluster sampling technique used was random sampling. Data collection techniques in research are in the form of learning outcomes test instruments at the beginning (pretest) and at the end of learning (posttest). The data analysis technique used is the normality test and the independent sample t-test. To test the research hypothesis, two similarity tests were used on average using the t test, namely the independent sample t-test or one-way analysis of variance (ANOVA) using the SPSS version 21 program. The result show that the guided inquiry learning model uses virtual laboratories and real laboratories to influence student learning outcomes (0.000 < 0.05). Based on the research results, it can be concluded that virtual and real experiments can improve students' physics learning outcomes, as evidenced by the difference in the average score of students' learning outcomes in the experimental class (87.47) compared to the control class (69.72).
The Analytical and Clinical Validity of the pfSTEP Digital Biomarker of the Susceptibility/Risk of Declining Physical Function in Community-Dwelling Older Adults

Measures of stepping volume and rate are common outputs from wearable devices, such as accelerometers. It has been proposed that biomedical technologies, including accelerometers and their algorithms, should undergo rigorous verification as well as analytical and clinical validation to demonstrate that they are fit for purpose. The aim of this study was to use the V3 framework to assess the analytical and clinical validity of a wrist-worn measurement system of stepping volume and rate, formed by the GENEActiv accelerometer and GENEAcount step counting algorithm. The analytical validity was assessed by measuring the level of agreement between the wrist-worn system and a thigh-worn system (activPAL), the reference measure. The clinical validity was assessed by establishing the prospective association between the changes in stepping volume and rate with changes in physical function (SPPB score). The agreement of the thigh-worn reference system and the wrist-worn system was excellent for total daily steps (CCC = 0.88, 95% CI 0.83–0.91) and moderate for walking steps and faster-paced walking steps (CCC = 0.61, 95% CI 0.53–0.68 and 0.55, 95% CI 0.46–0.64, respectively). A higher number of total steps and faster paced-walking steps was consistently associated with better physical function. After 24 months, an increase of 1000 daily faster-paced walking steps was associated with a clinically meaningful increase in physical function (0.53 SPPB score, 95% CI 0.32–0.74). We have validated a digital susceptibility/risk biomarker—pfSTEP—that identifies an associated risk of low physical function in community-dwelling older adults using a wrist-worn accelerometer and its accompanying open-source step counting algorithm.
Autotaxin Inhibition with IOA-289 Decreases Breast Tumor Growth in Mice Whereas Knockout of Autotaxin in Adipocytes Does Not

Simple Summary Breast cancer cells produce negligible quantities of autotaxin. We hypothesized that the autotaxin produced by inflamed breast adipocytes adjacent to breast tumors provides a major source of autotaxin secretion that drives breast tumor progression and the loss of treatment efficacy. We tested this hypothesis by using mice with an adipocyte-specific knock out of autotaxin. This knockout did not decrease the growth of E0771 tumors in syngeneic C57BL/6 mice or the growth and lung metastasis of spontaneous breast tumors in MMTV-PyMT mice. Despite this, the inhibition of autotaxin with IOA-289 decreased the growth of E0771 tumors in the mice. This demonstrates that cells other than adipocytes are responsible for promoting breast tumor growth. Abstract Breast cancer cells produce negligible quantities of autotaxin. Instead, previous work indicated that adipocytes in the inflamed adipose tissue adjacent to breast tumors are a major source of autotaxin secretion that drives breast tumor growth, metastasis, and the loss of efficacy for chemotherapy and radiotherapy. To test this hypothesis, we used mice with an adipocyte-specific knock out of autotaxin. The lack of autotaxin secretion from adipocytes failed to decrease the growth of orthotopic E0771 breast tumors in syngeneic C57BL/6 mice and the growth and lung metastasis of spontaneous breast tumors in MMTV-PyMT mice. However, the inhibition of autotaxin with IOA-289 decreased the growth of E0771 tumors, indicating that another source of autotaxin is responsible for tumor growth. Tumor-associated fibroblasts and leukocytes produce the majority of autotoxin transcripts in the E0771 breast tumors, and we hypothesize that they are the main sources of ATX that drive breast tumor growth. Autotaxin inhibition with IOA-289 increased the numbers of CD8α+-T-cells in the tumors. This was accompanied by decreases in the concentrations of CXCL10, CCL2, and CXCL9 in the plasma and LIF, TGFβ1, TGFβ2, and prolactin in the tumors. Bioinformatics analysis of human breast tumor databases showed that autotaxin (ENPP2) is expressed mainly in endothelial cells and fibroblasts. Autotaxin expression correlated significantly with increases in IL-6 cytokine receptor ligand interactions, signaling by LIF, TGFβ, and prolactin. This confirms the relevance of results from autotaxin inhibition in the mouse model. We propose that inhibiting autotaxin activity that is derived from cells presenting breast tumors such as fibroblasts, leukocytes, or endothelial cells changes the tumor micro-environment in such a way as to inhibit tumor growth.
Bioinformatics Analysis of Gene Expression Profiles for Diagnosing Sepsis and Risk Prediction in Patients with Sepsis

Although early recognition of sepsis is essential for timely treatment and can improve sepsis outcomes, no marker has demonstrated sufficient discriminatory power to diagnose sepsis. This study aimed to compare gene expression profiles between patients with sepsis and healthy volunteers to determine the accuracy of these profiles in diagnosing sepsis and to predict sepsis outcomes by combining bioinformatics data with molecular experiments and clinical information. We identified 422 differentially expressed genes (DEGs) between the sepsis and control groups, of which 93 immune-related DEGs were considered for further studies due to immune-related pathways being the most highly enriched. Key genes upregulated during sepsis, including S100A8, S100A9, and CR1, are responsible for cell cycle regulation and immune responses. Key downregulated genes, including CD79A, HLA-DQB2, PLD4, and CCR7, are responsible for immune responses. Furthermore, the key upregulated genes showed excellent to fair accuracy in diagnosing sepsis (area under the curve 0.747–0.931) and predicting in-hospital mortality (0.863–0.966) of patients with sepsis. In contrast, the key downregulated genes showed excellent accuracy in predicting mortality of patients with sepsis (0.918–0.961) but failed to effectively diagnosis sepsis. In conclusion, bioinformatics analysis identified key genes that may serve as biomarkers for diagnosing sepsis and predicting outcomes among patients with sepsis.
TSML: A New Pig Behavior Recognition Method Based on Two-Stream Mutual Learning Network

Changes in pig behavior are crucial information in the livestock breeding process, and automatic pig behavior recognition is a vital method for improving pig welfare. However, most methods for pig behavior recognition rely on human observation and deep learning. Human observation is often time-consuming and labor-intensive, while deep learning models with a large number of parameters can result in slow training times and low efficiency. To address these issues, this paper proposes a novel deep mutual learning enhanced two-stream pig behavior recognition approach. The proposed model consists of two mutual learning networks, which include the red–green–blue color model (RGB) and flow streams. Additionally, each branch contains two student networks that learn collaboratively to effectively achieve robust and rich appearance or motion features, ultimately leading to improved recognition performance of pig behaviors. Finally, the results of RGB and flow branches are weighted and fused to further improve the performance of pig behavior recognition. Experimental results demonstrate the effectiveness of the proposed model, which achieves state-of-the-art recognition performance with an accuracy of 96.52%, surpassing other models by 2.71%.