Adult versus adolescent idiopathic scoliosis surgery: a meta-analysis of clinical and radiographic outcomes.

INTRODUCTION: While the natural history of adolescent idiopathic scoliosis (AIS) and adult idiopathic scoliosis (AdIS) is well documented in the literature, the impact of age on postoperative outcomes remains an active area of research. We performed a systematic review and meta-analysis to compare patients undergoing surgery for AIS and AdIS with respect to: (1) postoperative Cobb correction, (2) perioperative variables, and (3) postoperative complications. METHODS: A systematic literature search was performed in accordance with preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. INCLUSION CRITERIA: studies published between 2002 and 2022, retrospective, and comparing AIS vs. AdIS patients undergoing deformity surgery. The primary outcome was postoperative Cobb correction. Secondary outcomes included estimated blood loss (EBL), operative time, total instrumented levels, length of stay (LOS), and postoperative complications. Random-effects models were performed according to the method of DerSimonian and Laird. RESULTS: Of 190 identified articles, 14 fit the inclusion criteria. A total of 1788 patients were included, 1275(71.3%) with AIS, and 513(28.7%) with AdIS. There was a significant age difference between AIS and AdIS (15.3 vs. 36.7 years, mean difference (MD) = 21.3 years, 95%CI = 14.3-28.4,p < 0.001). Mean postoperative Cobb percentage correction was reported in 5 articles and was significantly higher in AIS (68.4%) vs. AdIS (61.4%) (MD = -7.2, 95%CI = -11.6,-2.7,p = 0.001). EBL was not significantly different between AIS and AdIS (695.6 mL vs 817.7 mL,p = 0.204). Furthermore, no difference was found in operative time (MD = 37.9 min,95%CI = -10.7;86.6,p = 0.127), total instrumented level (MD = 0.88,95%CI = -0.7,2.4,p = 0.273), and LOS (MD = 0.5, 95%CI = -0.2;1.2, p = 0.188). Four articles reported postoperative complications in AIS vs AdIS, with no difference in neurological deficit, instrumentation-related complications, and medical complications. CONCLUSION: AIS patients had better radiographic correction compared to AdIS. Though no difference was found in perioperative outcomes and complications, these findings emphasize the importance of counseling patients regarding the optimal timing of surgical correction.

Improving pressure injury risk assessment using real-world data from skilled nursing facilities: A cohort study.

This study aimed to improve the predictive accuracy of the Braden assessment for pressure injury risk in skilled nursing facilities (SNFs) by incorporating real-world data and training a survival model. A comprehensive analysis of 126 384 SNF stays and 62 253 in-house pressure injuries was conducted using a large calibrated wound database. This study employed a time-varying Cox Proportional Hazards model, focusing on variations in Braden scores, demographic data and the history of pressure injuries. Feature selection was executed through a forward-backward process to identify significant predictive factors. The study found that sensory and moisture Braden subscores were minimally contributive and were consequently discarded. The most significant predictors of increased pressure injury risk were identified as a recent (within 21 days) decrease in Braden score, low subscores in nutrition, friction and activity, and a history of pressure injuries. The model demonstrated a 10.4% increase in predictive accuracy compared with traditional Braden scores, indicating a significant improvement. The study suggests that disaggregating Braden scores and incorporating detailed wound histories and demographic data can substantially enhance the accuracy of pressure injury risk assessments in SNFs. This approach aligns with the evolving trend towards more personalized and detailed patient care. These findings propose a new direction in pressure injury risk assessment, potentially leading to more effective and individualized care strategies in SNFs. The study highlights the value of large-scale data in wound care, suggesting its potential to enhance quantitative approaches for pressure injury risk assessment and supporting more accurate, data-driven clinical decision-making.

Hamiltonian Learning from Time Dynamics Using Variational Algorithms.

The Hamiltonian of a quantum system governs the dynamics of the system via the Schrodinger equation. In this paper, the Hamiltonian is reconstructed in the Pauli basis using measurables on random states forming a time series data set. The time propagation is implemented through Trotterization and optimized variationally with gradients computed on the quantum circuit. We validate our output by reproducing the dynamics of unseen observables on a randomly chosen state not used for the optimization. Unlike existing techniques that try and exploit the structure/properties of the Hamiltonian, our scheme is general and provides freedom with regard to what observables or initial states can be used while still remaining efficient with regard to implementation. We extend our protocol to doing quantum state learning where we solve the reverse problem of doing state learning given time series data of observables generated against several Hamiltonian dynamics. We show results on Hamiltonians involving XX, ZZ couplings along with transverse field Ising Hamiltonians and propose an analytical method for the learning of Hamiltonians consisting of generators of the SU(3) group. This paper is likely to pave the way toward using Hamiltonian learning for time series prediction within the context of quantum machine learning algorithms.

Physics-Inspired Quantum Simulation of Resonating Valence Bond States─A Prototypical Template for a Spin-Liquid Ground State.

Spin liquids─an emergent, exotic collective phase of matter─have garnered enormous attention in recent years. While experimentally many prospective candidates have been proposed and realized, theoretically modeling real materials that display such behavior may pose serious challenges due to the inherently high correlation content of such phases. Over the last few decades, the second-quantum revolution has been the harbinger of a novel computational paradigm capable of initiating a foundational evolution in computational physics. In this report, we strive to use the power of the latter to study a prototypical model, a spin-1/2-unit cell of a Kagome antiferromagnet. Extended lattices of such unit cells are known to possess a magnetically disordered spin-liquid ground state. We employ robust classical numerical techniques such as the density-matrix renormalization group (DMRG) to identify the nature of the ground state through a matrix-product state (MPS) formulation. We subsequently use the gained insight to construct an auxiliary Hamiltonian with reduced measurables and also design an ansatz that is modular and gate-efficient. With robust error-mitigation strategies, we are able to demonstrate that the said ansatz is capable of accurately representing the target ground state even on a real IBMQ backend within 1% accuracy in energy. Since the protocol is linearly scaling O(n) in the number of unit cells, gate requirements, and the number of measurements, it is straightforwardly extendable to larger Kagome lattices that can pave the way for efficient construction of spin-liquid ground states on a quantum device.

Metabolite signature in acute ischemic stroke thrombi: a systematic review.

Metabolites are reliable biomarkers for many diseases. However, their role in acute ischemic stroke (AIS) pathogenesis is not well understood. In this systematic review we aim to evaluate the current literature on the presence of metabolites in thrombi retrieved by mechanical thrombectomy from AIS patients. Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 guidelines, we searched OVID Medline, PubMed, OVID Embase, Scopus, and Web of Science until July 13, 2022. Metabolites lists were extracted, and pathway analysis was performed in MetaboAnalyst database. Four articles listing metabolites were included in this systematic review. D-Glucose, diacylglycerol, phytosphingosine, galabiosylceramide, glucosylceramide and 4-hydroxynonenal were reported to be associated with clots. Metabolomics data analysis showed that glycolysis, lactose, and sphingolipid metabolism pathways were enriched. In conclusion, results of the present study show that the thrombi niche has a glycolytic phenotype. Future studies should work to better understand the metabolic properties of AIS thrombi.

Depression Scores following Ventral Intermediate Nucleus Deep Brain Stimulation for Essential Tremor: A Meta-Analysis.

BACKGROUND: Essential tremor (ET) patients present with both motor and non-motor symptoms including depression. Although deep brain stimulation (DBS) of the ventral intermediate nucleus (VIM) is used to treat motor symptoms of ET, there is no consensus as to how VIM DBS influences non-motor symptoms, specifically depression. OBJECTIVE: The objective of this study was to conduct a meta-analysis of available studies investigating change in pre- to postoperative depression scores as measured by Beck Depression Inventory (BDI) in ET patients receiving VIM DBS. METHODS: Inclusion criteria were randomized control trials or observational studies of patients undergoing unilateral/bilateral VIM DBS. Non-ET patients, case reports, patients <18 years old, only non-VIM electrode placement, non-English articles, and abstracts were excluded. The primary outcome was change in BDI score from the preoperative time point to the last available follow-up. Pooled estimates of overall effect for BDI standardized mean difference were calculated using random effects models with the inverse variance method. RESULTS: Seven studies divided into eight cohorts for a total of 281 ET patients met inclusion criteria. Pooled preoperative BDI score was 12.44 (95% CI [6.63-18.25]). A statistically significant decrease in depression scores was observed postoperatively (SMD = -0.29, 95% CI [-0.46 to -0.13], p = 0.0006). Pooled postoperative BDI score was 9.18 (95% CI [4.98-13.38]). A supplementary analysis which included an additional study with an estimated standard deviation at last follow-up was conducted. There was also a statistically significant decrease in depression postoperatively (9 cohorts, n = 352, SMD = -0.31, 95% CI [-0.46 to -0.16], p < 0.0001). CONCLUSIONS: Both quantitative and qualitative analyses of the existing literature suggest that VIM DBS improves depression postoperatively among ET patients. These results may guide surgical risk-benefit analysis and counseling for ET patients undergoing VIM DBS.

Quantum machine learning for chemistry and physics.

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

Sustainability of cover cropping practice with changing climate in Illinois.

Climate change could adversely impact the best management practices (BMPs) designed to build a sustainable agro-ecological environment. Cover cropping is a conservation practice capable of reducing nitrate-nitrogen (NO3-N) loadings by consuming water and nitrate from the soil. The objective of this study was to investigate how climate change would impact the proven water quality benefits of cereal rye as a winter cover crop (CC) over the climate divisions of Illinois using the DSSAT model. Moreover, this study explores the sustainability of the CC with the changing climate conditions by using five regional climate models (RCMs) projections of two warming scenarios-rcp45 (a medium emission scenario - radiative forcing of 4.5 W/m2) and rcp85 (a high emission scenario - radiative forcing of 8.5 W/m2)). The CC impact simulated in the warming scenarios for the near-term (2021-2040) and the far-term future (2041-2060) were compared with the baseline scenario (2001-2020). Our results conclude that the climate change may negatively impact [average of CC and no CC (NCC)] maize yield (-6.6%) while positively affecting soybean yield (17.6%) and CC biomass (73.0%) by the mid-century. Increased mineralization caused by rising temperature could increase the nitrate loss via tile flow (NLoss) and nitrate leached (NLeached) up to 26.3% and 7.6% on average by the mid-century in Illinois. Increasing CC biomass could reduce the NLoss more considerably in all the scenarios compared to the baselines. Nevertheless, the NLoss level in the CC treatment can increase from the near-term to far-term future and could get closer to the baseline levels in the NCC treatment. These results suggest that CC alone may not address nitrate loss goals via subsurface drainage (caused by increasing N mineralization) in future. Therefore, more robust and cost-effective BMPs are needed to aid the CC benefits in preventing nutrient loss from the agricultural fields.

Matched case-control analysis of outcomes following surgical resection of incidental meningioma.

PURPOSE: The management of incidentally discovered meningioma remains controversial. We sought to compare outcomes following surgical resection of incidental meningioma to a matched cohort of symptomatic meningiomas. METHODS: A retrospective single-center case-control study was conducted for patients undergoing resection of incidental meningioma from 2000 to 2019. A 1:1 case-control matching for incidental and symptomatic meningioma was performed using the following variables: age at initial visit, gender, tumor location/size, and presence of peritumoral edema. Primary outcomes included (1) WHO grading/histopathological subtype/MIB-1 index, (2) extent of resection (gross total resection or subtotal resection), and (3) recurrence. Outcomes were compared between groups using descriptive/bivariate analyses. RESULTS: A total of 91 incidental meningiomas were analyzed. Trauma was the most common reason (n = 19, 21%) to obtain imaging, and tumor size the leading reason to operate (n = 37, 41%). Median time-to-surgery from initial clinical encounter was 5-months (Q1:3, Q3:16.5). More incidental meningioma patients (n = 47, 52%) were privately insured compared to their matched symptomatic cohort (n = 30, 33%) (P = 0.006). Patients with incidental meningioma had significantly higher mean Karnofsky Performance Scale at time-of-surgery (93.2, SD:11.1 vs. 81.4, SD:12.7) (P < 0.001). There were no significant differences in primary/secondary outcomes between the groups. Incidental meningioma was not associated with recurrence on Cox proportional hazards analysis (HR: 0.795, 95%CI: 0.3-2.1, P = 0.637). CONCLUSION: Matched case-control analysis demonstrated no significant differences in clinical, histopathological, and functional outcomes following resection of incidental and symptomatic meningioma. While non-operative management with close follow-up and serial imaging is preferred for incidental meningiomas, those undergoing resection when indicated can anticipate similar safety and efficacy as symptomatic meningiomas.

Variational approach to quantum state tomography based on maximal entropy formalism.

Quantum state tomography is an integral part of quantum computation and offers the starting point for the validation of various quantum devices. One of the central tasks in the field of state tomography is to reconstruct, with high fidelity, the quantum states of a quantum system. From an experiment on a real quantum device, one can obtain the mean measurement values of different operators. With such data as input, in this report we employ the maximal entropy formalism to construct the least biased mixed quantum state that is consistent with the given set of expectation values. Even though, in principle, the reported formalism is quite general and should work for an arbitrary set of observables, in practice we shall demonstrate the efficacy of the algorithm on an informationally complete (IC) set of Hermitian operators. Such a set possesses the advantage of uniquely specifying a single quantum state from which the experimental measurements have been sampled and hence renders the rare opportunity not only to construct a least-biased quantum state but even replicate the exact state prepared experimentally within a preset tolerance. The primary workhorse of the algorithm is reconstructing an energy function which we designate as the effective Hamiltonian of the system, and parameterizing it with Lagrange multipliers, according to the formalism of maximal entropy. These parameters are thereafter optimized variationally so that the reconstructed quantum state of the system converges to the true quantum state within an error threshold. To this end, we employ a parameterized quantum circuit and a hybrid quantum-classical variational algorithm to obtain such a target state, making our recipe easily implementable on a near-term quantum device.

ΔNO and the complexities of electron correlation in simple hydrogen clusters.

The Δ natural orbital (ΔNO) two-electron density matrix (2-RDM) and energy expression are derived from a multideterminantal wave function. The approximate ΔNO 2-RDM is combined with an on-top density functional and a double-counting correction to capture electron correlation. A trust-region Newton's method optimization algorithm for the simultaneous optimization of ΔNO orbitals and occupancies is introduced and compared to the previous iterative diagonalization algorithm. The combination of ΔNO and two different on-top density functionals, Colle-Salvetti (CS) and Opposite-spin exponential cusp and Fermi-hole correction (OF), is assessed on small hydrogen clusters and compared to density functional, single-reference coupled-cluster, and multireference perturbation theory (MRMP2) methods. The ΔNO-CS and ΔNO-OF methods outperform the single-reference methods and are comparable to MRMP2. However, there is a distinct qualitative error in the ΔNO potential energy surface for H4 compared to the exact. This discrepancy is explained through analysis of the ΔNO orbitals, occupancies, and the two-electron density.

Construction of Entropically Favored Supramolecular Metal-Ligand Trimeric Assemblies Supported by Flexible Pyridylaminophosphorus(V) Scaffolds.

The self-assembly reactions of tetratopic metal acceptors with the flexible bidentate ligands are known to yield self-assembled molecular squares of the type [M4L8], triangles of composition [M3L6], or a mixture of these two. In this work, we demonstrate the preferential formation of a trimeric cage assembly of the formula [Pd3(L1)6·(BF4)6] (1a) over the tetrameric cage [Pd4(L1)8·(BF4)8] (1b) by employing a flexible dipodal phosphoramide ligand, [PhPO(NH(3-Py))2] (L1; 3-Py = 3-aminopyridine), in a reaction with [Pd(CH3CN)4·(BF4)2]. The entropically favored trimeric self-assembly of 1a is the predominant species in the solution [dimethyl sulfoxide (DMSO)-d6] at room temperature. In fact, at higher temperatures, 1a was found to be the only product, as observed from the disappearance of the peak due to 1b in the 31P NMR spectrum. However, in a 1:1 mixture of acetonitrile (MeCN)-d3 and DMSO-d6, the tetrameric species 1b is the preferred species, as revealed by the 31P NMR and electrospray ionization mass spectral analyses. The structure of the molecular trimer 1a has been established in the solid state by using single-crystal X-ray diffraction analysis. Interestingly, treatment of an another flexible ligand, [MePO(NH(3-Py))2] (L2), with the same Pd(II) acceptor resulted in exclusive formation of the trimeric cage [Pd3(L2)6·(BF4)6] (2).

Convergence of a Reconstructed Density Matrix to a Pure State Using the Maximal Entropy Approach.

Impressive progress has been made in the past decade in the study of technological applications of varied types of quantum systems. With industry giants like IBM laying down their roadmap for scalable quantum devices with more than 1000-qubits by the end of 2023, efficient validation techniques are also being developed for testing quantum processing on these devices. The characterization of a quantum state is done by experimental measurements through the process called quantum state tomography (QST) which scales exponentially with the size of the system. However, QST performed using incomplete measurements is aptly suited for characterizing these quantum technologies especially with the current nature of noisy intermediate-scale quantum (NISQ) devices where not all mean measurements are available with high fidelity. We, hereby, propose an alternative approach to QST for the complete reconstruction of the density matrix of a quantum system in a pure state for any number of qubits by applying the maximal entropy formalism on the pairwise combinations of the known mean measurements. This approach provides the best estimate of the target state when we know the complete set of observables, which is the case of convergence of the reconstructed density matrix to a pure state. Our goal is to provide a practical inference of a quantum system in a pure state that can find its applications in the field of quantum error mitigation on a real quantum computer that we intend to investigate further.

Noble gas hydrides in the triplet state: HNgCCO+ (Ng = He, Ne, Ar, Kr, and Xe).

Motivated by the very recent investigations of neutral noble gas compounds in the open-shell configuration, we explored a new series of noble gas hydrides in the triplet state. The possible existence of noble gas-inserted ketenyl cations, HNgCCO+ (Ng = He, Ne, Ar, Kr, and Xe), in their triplet electronic state has been predicted by various ab initio quantum chemical techniques. Density functional theory (DFT), second-order Møller-Plesset perturbation theory (MP2), and coupled-cluster theory (CCSD(T)) based methods have been employed to investigate the structures, energetics, harmonic vibrational frequencies, and charge distribution analysis of these ions. The aforementioned ions have been found to be thermodynamically stable with respect to all plausible 2-body and 3-body dissociation channels, except the 2-body dissociation pathway leading to the formation of global minima products (Ng + HCCO+). Nevertheless, each of the predicted HNgCCO+ ions is connected to the global minima products through a transition state with a finite barrier height on the potential energy surface, which confirms the kinetic stability of the metastable species. Detailed analysis of the optimized structural parameters, energetics, and harmonic vibrational frequencies of the predicted species clearly indicated that a strong covalent bond exists between H and Ng atoms, while a comparatively weak interaction is found between Ng and C atoms. Moreover, charge distribution and atoms-in-molecules (AIM) analysis strongly concurred with the above inferences and also suggested that the predicted metastable ions should exist essentially in the form of [HNg]+[CCO] complex. These results ultimately indicate that these predicted species may be prepared and characterized by suitable experimental technique(s) under a cryogenic environment.

Study Model-based Evaluation of Built-in Tip, Torque, and In-out Characteristics of a Third-generation Preadjusted Edgewise Appliance.

AIM: The aim of the study was to evaluate the clinical effectiveness of MBT™ preadjusted edgewise appliance (PEA) in terms of achieving the optimal expression of its built-in characteristics of tip, torque, and in-out. MATERIALS AND METHODS: Pretreatment and posttreatment study models of 20 subjects who received full fixed appliance treatment involving four first premolar extractions using the MBT™ appliance were measured for tip, torque, and in-out using the method described by Andrews. Treatment changes were analyzed statistically, and the posttreatment measurements were compared with the MBT™ specifications as well as Andrews' values for the above-mentioned parameters. RESULTS: Except for the maxillary canines and second premolars, the built-in tip of MBT™ appliance was nearly fully expressed, though there was some lack of correlation with Andrews' values. Despite the fact that the full amount of torque built into the MBT™ appliance was not expressed, torque measurements for all teeth except the maxillary second premolars and the first molars showed either no statistically significant difference or were significantly higher than Andrews' values for these teeth. In-out readings were lower than both MBT™ and Andrews' values, but the relative order of crown prominences was similar. Conclusion and clinical significance: The MBT™ appliance is thus effective in ensuring a successful treatment result, though individual adjustments may be necessary for optimal tooth positioning at the end of the treatment, as with any preadjusted appliance.

Aggregation-Induced Emission-Based Chemodosimeter Approach for Selective Sensing and Imaging of Hg(II) and Methylmercury Species.

Methylmercury (CH3Hg+) is the common form of organic mercury and is more toxic than its inorganic or elemental forms. Mercury is emanated in the course of various natural events and human activities and converts to methylmercury by anaerobic organisms. CH3Hg+ are ingested by fish and subsequently bioaccumulated in their tissue and, eventually, enter the human diet, causing serious health issues. Therefore, selective and sensitive detection of bioaccumulated CH3Hg+ in fish samples is essential. Herein, the development of a simple, highly sensitive and selective aggregation-induced emission (AIE)-based turn-on probe for both inorganic mercury ions and organicmercury species is reported. The probe's function is based on mercury ion-promoted transmetalation reaction of aryl boronic acid. The probe, a tetraphenylethylene (TPE)-monoboronic acid (1), was successfully utilized for AIE-based fluorescence imaging study on methylmercury-contaminated live cells and zebrafish for the first time. Both Hg(II) and CH3Hg+ ensued a fast transmetalation of TPE-boronic acid causing drastic reduction in the solubility of the resulting product (TPE-HgCl/TPE-HgMe) in the working solvent system. At the dispersed phase, the aggregated form of TPE-mercury ions recovers planarity because of restricted rotational freedom promoting aggregation-induced emission. Simple design, cost-effective synthesis, high selectivity, inexpensive instrumentation, fast signal transduction, and low limit of detection (0.12 ppm) are some of the key merits of this analytical tool.

Quality Assessment of Radiomics Studies on Functional Outcomes After Acute Ischemic Stroke-A Systematic Review.

OBJECTIVE: Radiomics is a machine-learning method that extracts features from medical images. The objective of the present systematic review was to assess the quality of existing studies that use radiomics methods to predict functional outcomes in patients after acute ischemic stroke. METHODS: Studies using radiomics-extracted features to predict functional outcomes among patients with acute ischemic stroke using the modified Rankin Scale were included. PubMed, Scopus, Web of Science, and Embase were screened using the terms "radiomics" and "texture" in combination with "stroke." Quality scores were calculated based on Radiomics Quality Score, the IBSI (Image Biomarkers Standardization Initiative), and the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2). RESULTS: Fourteen studies were included. The median total Radiomics Quality Score was 14.5 (13-16) out of 36. Domains 1, 5, and 6 on protocol quality and stability of imaging and segmentation, level of evidence, and use of open science and data, respectively, were poor. Median IBSI score was 2.5 (1-5) out of 6. Few studies included bias-field correction algorithms, isovoxel resampling, skull stripping, or gray-level discretization. Of 14 studies, none received +6 points, 1 received +5 points, 5 received +4 points, 1 study received +3 points, 5 received +2 points, 2 received +1 points, and none received 0 points. As per the QUADAS-2, 6/14 (42.9%) studies had a risk of bias concern and 0/14 (0%) had applicability concern. CONCLUSIONS: The quality of the included studies was low to moderate. With increasing use of radiomics, future studies should attempt to adhere to and report established radiomics quality guidelines.

Effect of Treatment Choice on Short-Term and Long-Term Outcomes for Carotid Near-Occlusion: A Meta-Analysis.

OBJECTIVE: Current guidelines recommend best medical treatment (BMT) over revascularization for carotid near-occlusion (CNO); however, it remains unclear whether BMT, carotid artery stenting (CAS), or carotid endarterectomy (CEA) is the optimal treatment strategy. The present meta-analysis aimed to compare outcomes among patients with CNO receiving BMT, CAS, or CEA. METHODS: PubMed, Web of Science, Scopus, and Embase were searched. English studies with ≥1 month follow-up, that used established CNO diagnostic guidelines, that provided outcomes by treatment, and in which 95% confidence intervals (CIs) were calculable were included. Studies on acute ischemic stroke (AIS) requiring emergent reperfusion therapy, nonatherosclerotic lesions, nonprimary research articles, non-English, and nonhuman studies were excluded. Outcomes were mortality, AIS, transient ischemic attack, myocardial infarction within and beyond 30 days, and restenosis. A generalized linear mixed model, subgroup analysis, and meta-regression were used to compare outcomes. RESULTS: Thirty-eight studies were included. Pooled rates for AIS beyond 30 days were 9.90% (95% CI, 4.31%-21.16%), 0.79% (95% CI, 0.24%-2.53%), and 0.80% (95% CI, 0.15%-4.07%) for BMT, CAS, and CEA, respectively. Subgroup analysis was statistically significant (P < 0.001). Meta-regression showed lower incidence favoring procedural intervention (CAS vs. BMT, P = 0.001; CEA vs. BMT, P = 0.003). Subgroup analysis for mortality beyond 30 days was also significant (P = 0.016) but meta-regression did not favor one treatment over another. Other outcomes were not statistically significant. CONCLUSIONS: Revascularization for CNO may decrease long-term stroke rates. Given that current guidelines are based on randomized controlled trials from the 1990s, updated randomized trials are warranted to determine the optimal treatment for CNO.

Structural neuroimaging markers of normal pressure hydrocephalus versus Alzheimer's dementia and Parkinson's disease, and hydrocephalus versus atrophy in chronic TBI-a narrative review.

Introduction: Normal Pressure Hydrocephalus (NPH) is a prominent type of reversible dementia that may be treated with shunt surgery, and it is crucial to differentiate it from irreversible degeneration caused by its symptomatic mimics like Alzheimer's Dementia (AD) and Parkinson's Disease (PD). Similarly, it is important to distinguish between (normal pressure) hydrocephalus and irreversible atrophy/degeneration which are among the chronic effects of Traumatic Brain Injury (cTBI), as the former may be reversed through shunt placement. The purpose of this review is to elucidate the structural imaging markers which may be foundational to the development of accurate, noninvasive, and accessible solutions to this problem. Methods: By searching the PubMed database for keywords related to NPH, AD, PD, and cTBI, we reviewed studies that examined the (1) distinct neuroanatomical markers of degeneration in NPH versus AD and PD, and atrophy versus hydrocephalus in cTBI and (2) computational methods for their (semi-) automatic assessment on Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans. Results: Structural markers of NPH and those that can distinguish it from AD have been well studied, but only a few studies have explored its structural distinction between PD. The structural implications of cTBI over time have been studied. But neuroanatomical markers that can predict shunt response in patients with either symptomatic idiopathic NPH or post-traumatic hydrocephalus have not been reliably established. MRI-based markers dominate this field of investigation as compared to CT, which is also reflected in the disproportionate number of MRI-based computational methods for their automatic assessment. Conclusion: Along with an up-to-date literature review on the structural neurodegeneration due to NPH versus AD/PD, and hydrocephalus versus atrophy in cTBI, this article sheds light on the potential of structural imaging markers as (differential) diagnostic aids for the timely recognition of patients with reversible (normal pressure) hydrocephalus, and opportunities to develop computational tools for their objective assessment.

Towards an AI-Based Objective Prognostic Model for Quantifying Wound Healing.

Chronic wounds affect millions of people worldwide every year. An adequate assessment of a wound's prognosis is critical to wound care, guiding clinical decision making by helping clinicians understand wound healing status, severity, triaging and determining the efficacy of a treatment regimen. The current standard of care involves using wound assessment tools, such as Pressure Ulcer Scale for Healing (PUSH) and Bates-Jensen Wound Assessment Tool (BWAT), to determine wound prognosis. However, these tools involve manual assessment of a multitude of wound characteristics and skilled consideration of a variety of factors, thus, making wound prognosis a slow process which is prone to misinterpretation and high degree of variability. Therefore, in this work we have explored the viability of replacing subjective clinical information with deep learning-based objective features derived from wound images, pertaining to wound area and tissue amounts. These objective features were used to train prognostic models that quantified the risk of delayed wound healing, using a dataset consisting of 2.1 million wound evaluations derived from more than 200,000 wounds. The objective model, which was trained exclusively using image-based objective features, achieved at minimum a 5% and 9% improvement over PUSH and BWAT, respectively. Our best performing model, that used both subjective and objective features, achieved at minimum an 8% and 13% improvement over PUSH and BWAT, respectively. Moreover, the reported models consistently outperformed the standard tools across various clinical settings, wound etiologies, sexes, age groups and wound ages, thus establishing the generalizability of the models.