The Long-term Effects of Percutaneous Laser Disc Decompression (PLDD) Treatment on Lumbar Disc Protrusion: A 2-Year Follow-up

Introduction: Nowadays many physicians have focused their attention on using low invasive methods for the treatment of disc protrusion. Thus, the current study was carried out to evaluate the effect and therapeutic outcomes of clinical percutaneous laser disc decompression (PLDD) in the treatment of chronic low back pain caused by disc protrusion during a two-year follow-up.Methods: This historical cohort study was conducted on 40 patients, who were suffering from chronic low back pain caused by disc protrusion diagnosed, and referred to the pain clinic of Akhtar Hospital from March to August 2016 were treated with PLDD and were followed up for at least two years after performing PLDD (from 2018 to 2019). All the information has been extracted using medical records and patient interviews. The severity of pain was measured by the Numeric Rating Scale (NRS), and the Oswestry disability index (ODI) was measured before and two years after the treatment.Results: The most common sites for two-level PLDD were L4-S1 and L3-L5, and the most common sites for one-level PLDD were L5-S1 and L4-L5. Overall, the levels of pain and functional disability two years after PLDD showed significant improvements (P = 0.0001). The results revealed no statistically significant differences in NRS and ODI scores between the two groups of men and women two years after PLDD (P > 0.05). Furthermore, they indicated no statistically significant differences in NRS and ODI scores between the different disc protrusion levels two years after PLDD (P > 0.05).Conclusion: It seems that the PLDD is a low-invasive, safe, and effective method that can be used in patients with chronic low back pain caused by a disc protrusion. Therefore, it can be considered as a suitable choice in treating patients with chronic low back pain caused by a disc protrusio

Targeting the Pseudomonas aeruginosa Virulence Factor Phospholipase C With Engineered Liposomes.

Engineered liposomes composed of the naturally occurring lipids sphingomyelin (Sm) and cholesterol (Ch) have been demonstrated to efficiently neutralize toxins secreted by Gram-positive bacteria such as Streptococcus pneumoniae and Staphylococcus aureus. Here, we hypothesized that liposomes are capable of neutralizing cytolytic virulence factors secreted by the Gram-negative pathogen Pseudomonas aeruginosa. We used the highly virulent cystic fibrosis P. aeruginosa Liverpool Epidemic Strain LESB58 and showed that sphingomyelin (Sm) and a combination of sphingomyelin with cholesterol (Ch:Sm; 66 mol/% Ch and 34 mol/% Sm) liposomes reduced lysis of human bronchial and red blood cells upon challenge with the Pseudomonas secretome. Mass spectrometry of liposome-sequestered Pseudomonas proteins identified the virulence-promoting hemolytic phospholipase C (PlcH) as having been neutralized. Pseudomonas aeruginosa supernatants incubated with liposomes demonstrated reduced PlcH activity as assessed by the p-nitrophenylphosphorylcholine (NPPC) assay. Testing the in vivo efficacy of the liposomes in a murine cutaneous abscess model revealed that Sm and Ch:Sm, as single dose treatments, attenuated abscesses by >30%, demonstrating a similar effect to that of a mutant lacking plcH in this infection model. Thus, sphingomyelin-containing liposome therapy offers an interesting approach to treat and reduce virulence of complex infections caused by P. aeruginosa and potentially other Gram-negative pathogens expressing PlcH

Severe COVID-19 and non-COVID-19 severe sepsis converge transcriptionally after a week in the intensive care unit, indicating common disease mechanisms

IntroductionSevere COVID-19 and non-COVID-19 pulmonary sepsis share pathophysiological, immunological, and clinical features. To what extent they share mechanistically-based gene expression trajectories throughout hospitalization was unknown. Our objective was to compare gene expression trajectories between severe COVID-19 patients and contemporaneous non-COVID-19 severe sepsis patients in the intensive care unit (ICU).MethodsIn this prospective single-center observational cohort study, whole blood was drawn from 20 COVID-19 patients and 22 non-COVID-19 adult sepsis patients at two timepoints: ICU admission and approximately a week later. RNA-Seq was performed on whole blood to identify differentially expressed genes and significantly enriched pathways.ResultsAt ICU admission, despite COVID-19 patients being almost clinically indistinguishable from non-COVID-19 sepsis patients, COVID-19 patients had 1,215 differentially expressed genes compared to non-COVID-19 sepsis patients. After one week in the ICU, the number of differentially expressed genes dropped to just 9 genes. This drop coincided with decreased expression of antiviral genes and relatively increased expression of heme metabolism genes over time in COVID-19 patients, eventually reaching expression levels seen in non-COVID-19 sepsis patients. Both groups also had similar underlying immune dysfunction, with upregulation of immune processes such as “Interleukin-1 signaling” and “Interleukin-6/JAK/STAT3 signaling” throughout disease compared to healthy controls.DiscussionEarly on, COVID-19 patients had elevated antiviral responses and suppressed heme metabolism processes compared to non-COVID-19 severe sepsis patients, although both had similar underlying immune dysfunction. However, after one week in the ICU, these diseases became indistinguishable on a gene expression level. These findings highlight the importance of early antiviral treatment for COVID-19, the potential for heme-related therapeutics, and consideration of immunomodulatory therapies for both diseases to treat shared immune dysfunction

Persistence is key: unresolved immune dysfunction is lethal in both COVID-19 and non-COVID-19 sepsis

IntroductionSevere COVID-19 and non-COVID-19 pulmonary sepsis share pathophysiological, immunological, and clinical features, suggesting that severe COVID-19 is a form of viral sepsis. Our objective was to identify shared gene expression trajectories strongly associated with eventual mortality between severe COVID-19 patients and contemporaneous non-COVID-19 sepsis patients in the intensive care unit (ICU) for potential therapeutic implications.MethodsWhole blood was drawn from 20 COVID-19 patients and 22 non-COVID-19 adult sepsis patients at two timepoints: ICU admission and approximately a week later. RNA-Seq was performed on whole blood to identify differentially expressed genes and significantly enriched pathways. Using systems biology methods, drug candidates targeting key genes in the pathophysiology of COVID-19 and sepsis were identified.ResultsWhen compared to survivors, non-survivors (irrespective of COVID-19 status) had 3.6-fold more “persistent” genes (genes that stayed up/downregulated at both timepoints) (4,289 vs. 1,186 genes); these included persistently downregulated genes in T-cell signaling and persistently upregulated genes in select innate immune and metabolic pathways, indicating unresolved immune dysfunction in non-survivors, while resolution of these processes occurred in survivors. These findings of persistence were further confirmed using two publicly available datasets of COVID-19 and sepsis patients. Systems biology methods identified multiple immunomodulatory drug candidates that could target this persistent immune dysfunction, which could be repurposed for possible therapeutic use in both COVID-19 and sepsis.DiscussionTranscriptional evidence of persistent immune dysfunction was associated with 28-day mortality in both COVID-19 and non-COVID-19 septic patients. These findings highlight the opportunity for mitigating common mechanisms of immune dysfunction with immunomodulatory therapies for both diseases

Predicting sepsis severity at first clinical presentation:The role of endotypes and mechanistic signatures

BACKGROUND: Inter-individual variability during sepsis limits appropriate triage of patients. Identifying, at first clinical presentation, gene expression signatures that predict subsequent severity will allow clinicians to identify the most at-risk groups of patients and enable appropriate antibiotic use. METHODS: Blood RNA-Seq and clinical data were collected from 348 patients in four emergency rooms (ER) and one intensive-care-unit (ICU), and 44 healthy controls. Gene expression profiles were analyzed using machine learning and data mining to identify clinically relevant gene signatures reflecting disease severity, organ dysfunction, mortality, and specific endotypes/mechanisms. FINDINGS: Gene expression signatures were obtained that predicted severity/organ dysfunction and mortality in both ER and ICU patients with accuracy/AUC of 77–80%. Network analysis revealed these signatures formed a coherent biological program, with specific but overlapping mechanisms/pathways. Given the heterogeneity of sepsis, we asked if patients could be assorted into discrete groups with distinct mechanisms (endotypes) and varying severity. Patients with early sepsis could be stratified into five distinct and novel mechanistic endotypes, named Neutrophilic-Suppressive/NPS, Inflammatory/INF, Innate-Host-Defense/IHD, Interferon/IFN, and Adaptive/ADA, each based on ∼200 unique gene expression differences, and distinct pathways/mechanisms (e.g., IL6/STAT3 in NPS). Endotypes had varying overall severity with two severe (NPS/INF) and one relatively benign (ADA) groupings, consistent with reanalysis of previous endotype studies. A 40 gene-classification tool (accuracy=96%) and several gene-pairs (accuracy=89–97%) accurately predicted endotype status in both ER and ICU validation cohorts. INTERPRETATION: The severity and endotype signatures indicate that distinct immune signatures precede the onset of severe sepsis and lethality, providing a method to triage early sepsis patients

Preparing for Life: Plasma Proteome Changes and Immune System Development During the First Week of Human Life.

Neonates have heightened susceptibility to infections. The biological mechanisms are incompletely understood but thought to be related to age-specific adaptations in immunity due to resource constraints during immune system development and growth. We present here an extended analysis of our proteomics study of peripheral blood-plasma from a study of healthy full-term newborns delivered vaginally, collected at the day of birth and on day of life (DOL) 1, 3, or 7, to cover the first week of life. The plasma proteome was characterized by LC-MS using our established 96-well plate format plasma proteomics platform. We found increasing acute phase proteins and a reduction of respective inhibitors on DOL1. Focusing on the complement system, we found increased plasma concentrations of all major components of the classical complement pathway and the membrane attack complex (MAC) from birth onward, except C7 which seems to have near adult levels at birth. In contrast, components of the lectin and alternative complement pathways mainly decreased. A comparison to whole blood messenger RNA (mRNA) levels enabled characterization of mRNA and protein levels in parallel, and for 23 of the 30 monitored complement proteins, the whole blood transcript information by itself was not reflective of the plasma protein levels or dynamics during the first week of life. Analysis of immunoglobulin (Ig) mRNA and protein levels revealed that IgM levels and synthesis increased, while the plasma concentrations of maternally transferred IgG1-4 decreased in accordance with their in vivo half-lives. The neonatal plasma ratio of IgG1 to IgG2-4 was increased compared to adult values, demonstrating a highly efficient IgG1 transplacental transfer process. Partial compensation for maternal IgG degradation was achieved by endogenous synthesis of the IgG1 subtype which increased with DOL. The findings were validated in a geographically distinct cohort, demonstrating a consistent developmental trajectory of the newborn's immune system over the first week of human life across continents. Our findings indicate that the classical complement pathway is central for newborn immunity and our approach to characterize the plasma proteome in parallel with the transcriptome will provide crucial insight in immune ontogeny and inform new approaches to prevent and treat diseases

Non-suicidal Self-injury in delinquent adolescents and adolescents with history of childhood maltreatment: motivation and suicide probability

The aims of this study were to (1) investigate methods and functions of Non suicidal self-injury (NNSI); (2) examine association between NSSI and suicide probability in delinquent adolescents and adolescent with history of childhood maltreatment. Participants included 238 adolescents selected based on convenient sampling. The participants completed Non suicidal self injury behaviors checklist, Inventory of Non suicidal self- injury-functions and suicide probability scale. Results showed that 51% (31 from delinquent and 83 from another group) has at least 1 type of خودجرحی بدون NSSI behaviors which included cutting, burning, self-hitting, head-banging and rubbing. Suicide probability was significantly higher in adolescents with NSSI behaviors. Confirmatory factor analysis indicated group acceptance, marking distress, anti-suicide, self-affirmation, interpersonal boundaries, affect regulation and revenge as motivations extracted from Inventory of Statements about Self-Injury. Affect regulation, marking distress and group acceptance were motivations that predicted suicide probability

Predicting Bitcoin price changes using sentiment analysis in social media and celebrities along with data mining

Most of the research has been done on predicting the price changes of cryptocurrencies, and the researchers used machine learning algorithms in this field. The purpose of this article is to present an approach to predicting Bitcoin price changes using machine learning algorithms along with data from people's sentiment analysis towards famous people such as Elon Musk on Twitter, Reddit, and Telegram social networks. The data collected for this research is from 2021 and 2022. The distinguishing feature of this research is the use of technical, economic indicators and sentiment analysis simultaneously to predict the price trend. The recursive feature removal method was used to select the feature and in the next step, the results were compared by testing 8 classification algorithms. In this research, the xgboost algorithm showed excellent performance with a record accuracy of 88% in predicting the trend of Bitcoin price changes

Project portfolio risk identification and analysis, considering project risk interactions and using bayesian networks

An organization’s strategic objectives are accomplished through portfolios. However, the materialization of portfolio risks may affect a portfolio’s sustainable success and the achievement of those objectives. Moreover, project interdependencies and cause–effect relationships between risks create complexity for portfolio risk analysis. This paper presents a model using Bayesian network (BN) methodology for modeling and analyzing portfolio risks. To develop this model, first, portfolio-level risks and risks caused by project interdependencies are identified. Then, based on their cause–effect relationships all portfolio risks are organized in a BN. Conditional probability distributions for this network are specified and the Bayesian networks method is used to estimate the probability of portfolio risk. This model was applied to a portfolio of a construction company located in Iran and proved effective in analyzing portfolio risk probability. Furthermore, the model provided valuable information for selecting a portfolio’s projects and making strategic decisions

IDR-1002 and LL-37 reduced LPS-induced cytokines and chemokine MCP-1 in RAW cells.

<p>The listed concentrations of IDR-1002 or LL-37 were added with or without <i>P</i>. <i>aeruginosa</i> LPS (10 ng/ml), then supernatants were collected after 24 h. Without LPS, IDR-1002 and LL-37 did not induce any of the cytokines or chemokine. IDR-1002 decreased LPS-induced IL-6 (A), TNF-α (B), and MCP-1 (C). LL-37 also decreased LPS-induced IL-6 (D), TNF-α (E), and MCP-1 (F). Data represent mean ± SEM from four independent experiments expressed as fold-change relative to LPS and were analyzed using two-way ANOVA and Dunnett’s multiple comparisons test. Only the significance for samples given LPS is displayed. *: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.001, ****: p ≤ 0.0001 compared to LPS.</p