Artificial Intelligence and Machine Learning in Neuroregeneration: A Systematic Review.

Artificial intelligence (AI) and machine learning (ML) show promise in various medical domains, including medical imaging, precise diagnoses, and pharmaceutical research. In neuroscience and neurosurgery, AI/ML advancements enhance brain-computer interfaces, neuroprosthetics, and surgical planning. They are poised to revolutionize neuroregeneration by unraveling the nervous system's complexities. However, research on AI/ML in neuroregeneration is fragmented, necessitating a comprehensive review. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations, 19 English-language papers focusing on AI/ML in neuroregeneration were selected from a total of 247. Two researchers independently conducted data extraction and quality assessment using the Mixed Methods Appraisal Tool (MMAT) 2018. Eight studies were deemed high quality, 10 moderate, and four low. Primary goals included diagnosing neurological disorders (35%), robotic rehabilitation (18%), and drug discovery (12% each). Methods ranged from analyzing imaging data (24%) to animal models (24%) and electronic health records (12%). Deep learning accounted for 41% of AI/ML techniques, while standard ML algorithms constituted 29%. The review underscores the growing interest in AI/ML for neuroregenerative medicine, with increasing publications. These technologies aid in diagnosing diseases and facilitating functional recovery through robotics and targeted stimulation. AI-driven drug discovery holds promise for identifying neuroregenerative therapies. Nonetheless, addressing existing limitations remains crucial in this rapidly evolving field.

Bimodal MRI/Fluorescence Nanoparticle Imaging Contrast Agent Targeting Prostate Cancer.

We developed a novel site-specific bimodal MRI/fluorescence nanoparticle contrast agent targeting gastrin-releasing peptide receptors (GRPrs), which are overexpressed in aggressive prostate cancers. Biocompatible ultra-small superparamagnetic iron oxide (USPIO) nanoparticles were synthesized using glucose and casein coatings, followed by conjugation with a Cy7.5-K-8AOC-BBN [7-14] peptide conjugate. The resulting USPIO(Cy7.5)-BBN nanoparticles were purified by 100 kDa membrane dialysis and fully characterized using transmission electron microscopy (TEM), dynamic light scattering (DLS), Fourier transform infrared (FTIR) spectroscopy, and magnetic resonance imaging (MRI) relaxivity, as well as evaluated for in vitro and in vivo binding specificity and imaging efficacy in PC-3 prostate cancer cells and xenografted tumor-bearing mice. The USPIO(Cy7.5)-BBN nanoparticles had a core diameter of 4.93 ± 0.31 nm and a hydrodynamic diameter of 35.56 ± 0.58 nm. The r2 relaxivity was measured to be 70.2 ± 2.5 s-1 mM-1 at 7T MRI. The Cy7.5-K-8AOC-BBN [7-14] peptide-to-nanoparticle ratio was determined to be 21:1. The in vitro GRPr inhibitory binding (IC50) value was 2.5 ± 0.7 nM, indicating a very high binding affinity of USPIO(Cy7.5)-BBN to the GRPr on PC-3 cells. In vivo MRI showed significant tumor-to-muscle contrast enhancement in the uptake group at 4 h (31.1 ± 3.4%) and 24 h (25.7 ± 2.1%) post-injection compared to the blocking group (4 h: 15.3 ± 2.0% and 24 h: -2.8 ± 6.8%; p < 0.005). In vivo and ex vivo near-infrared fluorescence (NIRF) imaging revealed significantly increased fluorescence in tumors in the uptake group compared to the blocking group. These findings demonstrate the high specificity of bimodal USPIO(Cy7.5)-BBN nanoparticles towards GRPr-expressing PC-3 cells, suggesting their potential for targeted imaging in aggressive prostate cancer.

Multi-level structural damage characterization using sparse acoustic sensor networks and knowledge transferred deep learning.

Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material, or geometric complexity. This motivates the exploration of machine-learning-based structural health monitoring methods in complex structures. However, creating sufficient training data sets with various defects is an ongoing challenge for data-driven machine (deep) learning algorithms. The ability to transfer the knowledge of a trained neural network from one component to another or to other sections of the same component would drastically reduce the required training data set. Also, it would facilitate computationally inexpensive machine learning based inspection systems. In this work, a machine-learning-based multi-level damage characterization is demonstrated with the ability to transfer trained knowledge within the sparse sensor network. A novel network spatial assistance and an adaptive convolution technique are proposed for efficient knowledge transfer within the deep learning algorithm. Proposed structural health monitoring method is experimentally evaluated on an aluminum plate with artificially induced defects. It was observed that the method improves the performance of knowledge transferred damage characterization by 50 % during localization and 24 % during severity assessment. Further, experiments using time windows with and without multiple edge reflections are studied. Results reveal that multiply scattered waves contain rich and deterministic defect signatures that can be mined using deep learning neural networks, improving the accuracy of both identification and quantification. In the case of a fixed sensor network, using multiply scattered waves shows 100 % prediction accuracy at all levels of damage characterization.

Photoinduced bidirectional mesophase transition in vesicles containing azobenzene amphiphiles.

The functionality and efficiency of proteins within a biological membrane are highly dependent on both the membrane lipid composition and the physiochemical properties of the solution. Lipid mesophases are directly influenced by changes in temperature, pH, water content or due to individual properties of single lipids such as photoswitchability. In this work, we were able to induce light- and temperature-driven mesophase transitions in a model membrane system containing a mixture of 1,2-dipalmitoyl-phosphatidylcholine phospholipids and azobenzene amphiphiles. We observed reversible and reproducible transitions between the lamellar and Pn3m cubic phase after illuminating the sample for 5 min with light of 365 and 455 nm wavelengths, respectively, to switch between the cis and trans states of the azobenzene N=N double bond. These light-controlled mesophase transitions were found for mixed complexes with up to 20% content of the photosensitive molecule and at temperatures below the gel-to-liquid crystalline phase transition temperature of 33°C. Our results demonstrate the potential to design bespoke model systems to study the response of membrane lipids and proteins upon changes in mesophase without altering the environment and thus provide a possible basis for drug delivery systems.

Molecular-Level Dysregulation of Insulin Pathways and Inflammatory Processes in Peripheral Blood Mononuclear Cells by Circadian Misalignment.

Circadian misalignment due to night work has been associated with an elevated risk for chronic diseases. We investigated the effects of circadian misalignment using shotgun protein profiling of peripheral blood mononuclear cells taken from healthy humans during a constant routine protocol, which was conducted immediately after participants had been subjected to a 3-day simulated night shift schedule or a 3-day simulated day shift schedule. By comparing proteomic profiles between the simulated shift conditions, we identified proteins and pathways that are associated with the effects of circadian misalignment and observed that insulin regulation pathways and inflammation-related proteins displayed markedly different temporal patterns after simulated night shift. Further, by integrating the proteomic profiles with previously assessed metabolomic profiles in a network-based approach, we found key associations between circadian dysregulation of protein-level pathways and metabolites of interest in the context of chronic metabolic diseases. Endogenous circadian rhythms in circulating glucose and insulin differed between the simulated shift conditions. Overall, our results suggest that circadian misalignment is associated with a tug of war between central clock mechanisms controlling insulin secretion and peripheral clock mechanisms regulating insulin sensitivity, which may lead to adverse long-term outcomes such as diabetes and obesity. Our study provides a molecular-level mechanism linking circadian misalignment and adverse long-term health consequences of night work.

Metal oxide-based gas sensor array for VOCs determination in complex mixtures using machine learning.

Detection of volatile organic compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array of 3 component metal oxides that give maximal cross-sensitivity and can successfully use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array comprises NiO-Au (ohmic), CuO-Au (Schottky), and ZnO-Au (Schottky) sensors made by the DC reactive sputtering method and having a film thickness of 80-100 nm. The NiO and CuO films have ultrafine particle sizes of < 50 nm and rough surface texture, while ZnO films consist of nanoscale platelets. This array was subjected to various VOC concentrations, including ethanol, acetone, toluene, and chloroform, one by one and in a pair/mix of gases. Thus, the response values show severe interference and departure from commonly observed power law behavior. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with an R2 value of more than 0.99 and LOD of 0.012 ppm, 0.015 ppm, 0.014 ppm, and 0.025 ppm for predicting the concentrations of acetone, toluene, ethanol, and chloroform, respectively, in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring.

Recent Research Trends in Neuroinflammatory and Neurodegenerative Disorders.

Neuroinflammatory and neurodegenerative disorders including Alzheimer's disease (AD), Parkinson's disease (PD), traumatic brain injury (TBI) and Amyotrophic lateral sclerosis (ALS) are chronic major health disorders. The exact mechanism of the neuroimmune dysfunctions of these disease pathogeneses is currently not clearly understood. These disorders show dysregulated neuroimmune and inflammatory responses, including activation of neurons, glial cells, and neurovascular unit damage associated with excessive release of proinflammatory cytokines, chemokines, neurotoxic mediators, and infiltration of peripheral immune cells into the brain, as well as entry of inflammatory mediators through damaged neurovascular endothelial cells, blood-brain barrier and tight junction proteins. Activation of glial cells and immune cells leads to the release of many inflammatory and neurotoxic molecules that cause neuroinflammation and neurodegeneration. Gulf War Illness (GWI) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are chronic disorders that are also associated with neuroimmune dysfunctions. Currently, there are no effective disease-modifying therapeutic options available for these diseases. Human induced pluripotent stem cell (iPSC)-derived neurons, astrocytes, microglia, endothelial cells and pericytes are currently used for many disease models for drug discovery. This review highlights certain recent trends in neuroinflammatory responses and iPSC-derived brain cell applications in neuroinflammatory disorders.

Ganglioside GM1 Drives Hemin and Protoporphyrin Adsorption in Phospholipid Membranes: A Structural Study.

Monosialoganglioside (GM1), a ubiquitous component of lipid rafts, and hemin, an integral part of heme proteins such as hemoglobin, are essential to the cell membranes of brain neurons and erythrocyte red blood cells for regulating cellular communication and oxygen transport. Protoporphyrin IX (PPIX) and its derivative hemin, on the contrary, show significant cytotoxic effects when in excess causing hematological diseases, such as thalassemia, anemia, malaria, and neurodegeneration. However, the in-depth molecular etiology of their interactions with the cell membrane has so far been poorly understood. Herein, the structure of the polymer cushion-supported lipid bilayer (SLB) of the binary mixture of phospholipid and GM1 in the presence of PPIX and its derivative hemin has been investigated to predict the molecular interactions in model phospholipid membranes. A high-resolution synchrotron-based X-ray scattering technique has been employed to explore the out-of-plane structure of the assembly at different compositions and concentrations. The structural changes have been complemented with the isobaric changes in the mean molecular area obtained from the Langmuir monolayer isotherm to predict the additive-induced membrane condensation and fluidization. PPIX-induced fluidization of phospholipid SLB without GM1 was witnessed, which was reversed to condensation with 2-fold higher structural changes in the presence of GM1. A hemin concentration-dependent linear condensing effect was observed in the pristine SLB. The effect was significantly reduced, and the linearity was observed to be lost in the mixed SLB containing GM1. Our study shows that GM1 alters the interaction of hemin and PPIX with the membrane, which could be explained with the aid of hydrophobic and electrostatic interactions. Our study indicates favorable and unfavorable interactions of GM1 with PPIX and hemin, respectively, in the membrane. The observed structural changes in both SLB and the underlying polymer cushion layer lead to the proposal of a molecule-specific interaction model that can benefit the pharmaceutical industries specialized for drug designing. Our study potentially enriches our fundamental biophysical understanding of neurodegenerative diseases and drug-membrane interactions.

Void-Engineered Metamaterial Delay Line with Built-In Impedance Matching for Ultrasonic Applications.

Metamaterials exhibit unique ultrasonic properties that are not always achievable with traditional materials. However, the structures and geometries needed to achieve such properties are often complex and difficult to obtain using common fabrication techniques. In the present research work, we report a novel metamaterial acoustic delay line with built-in impedance matching that is fabricated using a common 3D printer. Delay lines are commonly used in ultrasonic inspection when signals need to be separated in time for improved sensitivity. However, if the impedance of the delay line is not perfectly matched with those of both the sensor and the target medium, a strong standing wave develops in the delay line, leading to a lower energy transmission. The presented metamaterial delay line was designed to match the acoustic impedance at both the sensor and target medium interfaces. This was achieved by introducing graded engineered voids with different densities at both ends of the delay line. The measured impedances of the designed metamaterial samples show a good match with the theoretical predictions. The experimental test results with concrete samples show that the acoustic energy transmission is increased by 120% and the standing wave in the delay line is reduced by over a factor of 2 compared to a commercial delay line.

Development of Polyclonal Antibodies-Based Serological Method for the Detection of Calanthe Mild Mosaic Virus and Application in Virus Certification Programme.

Calanthe mild mosaic virus (CalMMV) infecting orchids is an important potyvirus which is known to cause mild leaf mosaic and flower colour-breaking symptoms in Calanthe and other orchid plants. The present study reports the production of polyclonal antibodies against CalMMV using bacterially expressed recombinant coat protein as immunogen, which in turn would be useful in routine indexing and screening of orchid germplasm. The coat protein (CP) gene (~ 807 bp) of CalMMV isolated from infected orchid sample was cloned in expression vector, pET-28a ( +) that yielded ~ 31 kDa fusion protein with Histidine tag (His6BP). The expression of fusion CP was confirmed through SDS-PAGE and Western blotting. The His6BP-CalMMV-CP obtained in soluble state after purification was used to immunize New Zealand white rabbit for the production of polyclonal antibodies (PAb). The PAb produced against the purified fusion protein successfully detected CAlMMV in the orchid samples at a dilution of 1:2000 in direct antigen-coated enzyme-linked immunosorbent assay (DAC-ELISA). This study presents the first report of Histidine tag (His6BP) fusion CalMMV-CP-based antibody production and its successful application in the identification of the virus in orchid plants. Outcome of this study will be helpful in routine certification programmes, screening of orchid germplasm and production of CalMMV-free planting materials of orchids.

Unravelling HDAC Selectivity for Erasing Acetyl Mark on Lys-5 of Histone H2B.

The reversible acetylation of specific Lysine residues of histones plays crucial role in the epigenetic regulation of chromatin activity. Importantly, perturbations of acetylation-deacetylation dynamics have important implications for cancer and neurological disorders. There are 18 human HDACs including sirtuins. The site-selective acetyl eraser specificity of HDACs is poorly defined. Deciphering the site specificity preference of HDACs from a gamut of lysine in histones may be critical for targeted inhibitor development and delineation of regulatory mechanisms associated with chromatin. Here, we have interrogated the propensity of HDACs to erase acetyl mark at Lys-5 of H2B namely, H2BK5Ac engineered by a peptide ligation reaction catalyzed by transpeptidase sortase. HDACs and Sirtuins were individually over-expressed in HEK293 cells and the deacetylation propensity of respective cell lysates was evaluated against H2BK5Ac for initial screening of potential acetyl erasers. This screen indicated HDAC1 as the prime eraser of acetyl mark in H2BK5Ac. The propensity of HDAC1 to erase acetyl mark of H2BK5Ac was further probed using semisynthetic designer nucleosomes with whole cell lysates, recombinant enzyme, and specific inhibitors. Consistent with the above data, siRNA knockdown of HDAC1 and closely related HDAC3 in HEK293 cells prevented the loss of H2BK5 acetylation.

Tannerella forsythia scavenges Fusobacterium nucleatum secreted NOD2 stimulatory molecules to dampen oral epithelial cell inflammatory response.

The oral organism Tannerella forsythia is auxotrophic for peptidoglycan amino sugar N-acetylmuramic acid (MurNAc). It survives in the oral cavity by scavenging MurNAc- and MurNAc-linked peptidoglycan fragments (muropeptides) secreted by co-habiting bacteria such as Fusobacterium nucleatum with which it forms synergistic biofilms. Muropeptides, MurNAc-l-Ala-d-isoGln (MDP, muramyl dipeptide) and d-γ-glutamyl-meso-DAP (iE-DAP dipeptide), are strong immunostimulatory molecules that activate nucleotide oligomerization domain (NOD)-like innate immune receptors and induce the expression of inflammatory cytokines and antimicrobial peptides. In this study, we utilized an in vitro T. forsythia-F. nucleatum co-culture model to determine if T. forsythia can selectively scavenge NOD ligands from the environment and impact NOD-mediated inflammation. The results showed that NOD-stimulatory molecules were secreted by F. nucleatum in the spent culture broth, which subsequently induced cytokine and antimicrobial peptide expression in oral epithelial cells. In the spent broth from T. forsythia-F. nucleatum co-cultures, the NOD-stimulatory activity was significantly reduced. These data indicated that F. nucleatum releases NOD2-stimulatory muropeptides in the environment, and T. forsythia can effectively scavenge the muropeptides released by co-habiting bacteria to dampen NOD-mediated host responses. This proof-of-principle study demonstrated that peptidoglycan scavenging by T. forsythia can impact the innate immunity of oral epithelium by dampening NOD activation.

Spectral characteristics of the flavones and anthocyanins present in passionflower (Passiflora incarnata).

Rich in antioxidants with a variety of flavones and anthocyanins, passionflower/fruit has been extensively used in food, beverage, medicinal, and natural dyes industries. The individual components present in passionflower are identified by extracting them in methanol, partitioning them between ethyl acetate and aqueous layers, and recording their ESI mass spectrometric data. The steady-state absorption and fluorescence spectra of the extract in methanol and dimethyl sulfoxide are recorded and the lifetime of the fluorescing species is reported. The pH dependence of the absorption spectrum confirms the presence of the anthocyanins.

Contemporary National Trends of Mechanical Circulatory Support Among Myocarditis Hospitalizations Before the Pandemic: A National In-patient Database (2005-2019) Study.

According to the Global Burden of Disease Project, the morbidity and mortality of myocarditis continue to be a significant worldwide burden. On October 1, 2015, hospital administrative data started using the International Classification of Diseases (ICD)-10 codes instead of the ICD-9. To our knowledge, nationwide trends of myocarditis have not been studied after this update. The NIS database from 2005-2019 was analyzed using ICD-9 and 10 codes. Our search yielded 141,369 hospitalizations due to myocarditis, with 40.9% females. There were 6627 (4.68%) patients who required mechanical circulatory support (MCS) using left ventricular assisted devices (LVAD), intra-aortic balloon pump (IABP), or extracorporeal membrane oxygenation (ECMO). The use of LVAD and ECMO increased significantly during the study period (p-trend 0.003 and <0.001, respectively), whereas the use of IABP decreased during the same period (p-trend 0.025). Our study demonstrated an overall increase in the use of MCS overall in myocarditis, with increasing utilization of more advanced MCS in the forms of LVAD and ECMO.

Ionic Liquid-Induced Assembly of DNA at Air-Water Interface.

DNA nanotechnology is the future of many products in the pharmaceutical and cosmetic industries. Self-assembly of this negatively charged biopolymer at surfaces and interfaces is an essential step to elaborate its field of applications. In this study, the ionic liquid (IL) monolayer-assisted self-assembly of DNA macromolecules at the air-water interface has been closely monitored by employing various quantitative techniques, namely, surface pressure-area (π-A) isotherms, surface potential, interfacial rheology, and X-ray reflectivity (XRR). The π-A isotherms reveal that the IL 1,3-didecyl 3-methyl imidazolium chloride induces DNA self-assembly at the interface, leading to a thick viscoelastic film. The interfacial rheology exhibits a notable rise in the viscoelastic modulus as the surface pressure increases. The values of storage and loss moduli measured as a function of strain frequency suggest a relaxation frequency that depends on the length of the macromolecule. The XRR measurements indicate a considerable increase in DNA layer thickness at the elevated surface pressures depending on the number of base pairs of the DNA. The results are considered in terms of the electrostatic and hydrophobic interactions, allowing a quantitative conclusion about the arrangement of DNA strands underneath the monolayer of the ILs at the air-water interface.

Use of 3D Printing Techniques to Fabricate Implantable Microelectrodes for Electrochemical Detection of Biomarkers in the Early Diagnosis of Cardiovascular and Neurodegenerative Diseases.

This Review provides a comprehensive overview of 3D printing techniques to fabricate implantable microelectrodes for the electrochemical detection of biomarkers in the early diagnosis of cardiovascular and neurodegenerative diseases. Early diagnosis of these diseases is crucial to improving patient outcomes and reducing healthcare systems' burden. Biomarkers serve as measurable indicators of these diseases, and implantable microelectrodes offer a promising tool for their electrochemical detection. Here, we discuss various 3D printing techniques, including stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), and two-photon polymerization (2PP), highlighting their advantages and limitations in microelectrode fabrication. We also explore the materials used in constructing implantable microelectrodes, emphasizing their biocompatibility and biodegradation properties. The principles of electrochemical detection and the types of sensors utilized are examined, with a focus on their applications in detecting biomarkers for cardiovascular and neurodegenerative diseases. Finally, we address the current challenges and future perspectives in the field of 3D-printed implantable microelectrodes, emphasizing their potential for improving early diagnosis and personalized treatment strategies.

Experience of "One Stop TB Diagnostic Solution" Model in Engaging a Private Laboratory for End-to-End Diagnostic Services in the National TB Elimination Program in Hisar, India.

The complete diagnostic evaluation of tuberculosis based on its drug-resistance profile is critical for appropriate treatment decisions. The TB diagnostic landscape in India has been transformed with the scaling-up of WHO-recommended diagnostics, but challenges remain with specimen transportation, completing diagnostic assessment, turnaround time (TAT), and maintaining laboratories. Private laboratories have demonstrated efficiencies for specimen collection, transportation, and the timely testing and issue of results. A one-stop TB diagnostic model was designed to assess the feasibility of providing end-to-end diagnostic services in the Hisar district of Haryana state, India. A NTEP-certified private laboratory was engaged to provide the services, complementing the existing public sector diagnostic services. A total of 10,164 specimens were collected between May 2022 and January 2023 and these were followed for the complete diagnostic assessment of Drug-Susceptible TB (DS-TB) and Drug-Resistant TB (DR-TB) and the time taken for issuing results. A total of 2152 (21%) patients were detected with TB, 1996 (93%) Rifampicin-Sensitive and 134 (6%) with Rifampicin-Resistant TB. Nearly 99% of the patients completed the evaluation of DS-TB and DR-TB within the recommended TAT. The One-Stop TB/DR-TB Diagnostic Solution model has demonstrated that diagnostic efficiencies could be enhanced through the strategic purchase of private laboratory services.

COVID-19 and Long COVID: Disruption of the Neurovascular Unit, Blood-Brain Barrier, and Tight Junctions.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), could affect brain structure and function. SARS-CoV-2 can enter the brain through different routes, including the olfactory, trigeminal, and vagus nerves, and through blood and immunocytes. SARS-CoV-2 may also enter the brain from the peripheral blood through a disrupted blood-brain barrier (BBB). The neurovascular unit in the brain, composed of neurons, astrocytes, endothelial cells, and pericytes, protects brain parenchyma by regulating the entry of substances from the blood. The endothelial cells, pericytes, and astrocytes highly express angiotensin converting enzyme 2 (ACE2), indicating that the BBB can be disturbed by SARS-CoV-2 and lead to derangements of tight junction and adherens junction proteins. This leads to increased BBB permeability, leakage of blood components, and movement of immune cells into the brain parenchyma. SARS-CoV-2 may also cross microvascular endothelial cells through an ACE2 receptor-associated pathway. The exact mechanism of BBB dysregulation in COVID-19/neuro-COVID is not clearly known, nor is the development of long COVID. Various blood biomarkers could indicate disease severity and neurologic complications in COVID-19 and help objectively diagnose those developing long COVID. This review highlights the importance of neurovascular and BBB disruption, as well as some potentially useful biomarkers in COVID-19, and long COVID/neuro-COVID.

Ticagrelor Versus Prasugrel in Patients With Acute Coronary Syndrome: A Systematic Review and Meta-Analysis.

Limited data comparing prasugrel and ticagrelor in acute coronary syndrome are available. Online databases, including MEDLINE and Cochrane Central, were queried to compare these drugs. The primary outcomes of this meta-analysis are myocardial infarction (MI), all-cause mortality, cardiovascular mortality, noncardiovascular mortality, stent thrombosis, and stroke. The secondary outcome is major bleeding. A total of 9 studies, including 94,590 patients (prasugrel group = 32,759; ticagrelor group = 61,831), were included in this meta-analysis. The overall mean age was 62.73 years, whereas the mean age for the ticagrelor and prasugrel groups was 63.80 and 61.65 years, respectively. Prasugrel is equally effective as compared with ticagrelor in preventing MI. There was no difference between the 2 groups regarding all-cause mortality, stent thrombosis, stroke, or major bleeding. In patients with acute coronary syndrome, prasugrel is equally effective when compared with ticagrelor in preventing MI.

Superior Vena Cava Syndrome: An Umbrella Review.

Superior vena cava syndrome (SVCS) is a medical emergency that encompasses an array of signs and symptoms due to obstruction of blood flow through the superior vena cava (SVC). It poses a significant healthcare burden due to its associated morbidity and mortality. Its impact on the healthcare system continues to grow due to the increasing incidence of the condition. This incidence trend has been attributed to the growing use of catheters, pacemakers, and defibrillators, although it is a rare complication of these devices. The most common cause of SVCS remains malignancies accounting for up to 60% of the cases. Understanding the pathophysiology of SVCS requires understanding the anatomy, the SVC drains blood from the right and left brachiocephalic veins, which drain the head and the upper extremities accounting for about one-third of the venous blood to the heart. The most common presenting symptoms of SVCS are swelling of the face and hand, chest pain, respiratory symptoms (dyspnea, stridor, cough, hoarseness, and dysphagia), and neurologic manifestations (headaches, confusion, or visual/auditory disturbances). Symptoms generally worsen in a supine position. Diagnosis typically requires imaging, and SVCS can be graded based on classification schemas depending on the severity of symptoms and the location, understanding, and degree of obstruction. Over the past decades, the management modalities of SVCS have evolved to meet the increasing burden of the condition. Here, we present an umbrella review providing an overall assessment of the available information on SVCS, including the various management options, their indications, and a comparison of the advantages and disadvantages of these modalities.