The post-COVID-19 condition (PCC) is a highly debilitating and persistent postinfectious syndrome that affects millions of people worldwide and has no effective treatment. Therapeutic plasma exchange (TPE) has the potential to improve the PCC by clearing the peripheral soluble pro-inflammatory immune milieu derived from acute or persistent SARS-CoV-2 infection. In a phase II, double-blind, placebo-controlled, randomized trial, fifty subjects with PCC were randomly assigned (1:1) to receive six sessions of either TPE or a sham plasma exchange and were followed for 90 days (ClinicalTrials.gov registration: NCT05445674). The primary endpoint was safety; secondary endpoints included functional status, symptomology, quality of life, neurocognitive symptoms, and peripheral biochemistry, hematology, coagulation and inflammation parameters. Both study arms had a similarly favorable safety profile. There were no diferences between groups in any of the efficacy parameters evaluated. Whereas TPE is safe, it did not lead to any discernible improvement of the PCC in this clinical trial.

Background: Comprehensive geriatric assessments (CGAs) are necessary to address the needs of people with human immunodeficiency virus infection (PWH) aged ≥ 50 years and ensure that they receive high-quality care. We aimed to identify the most effective tests from an extensive CGA to develop a short CGA. Methods: This observational, cross-sectional, and analytical study was conducted in three phases: (1) describing PWH aged ≥ 50 and matched controls; (2) jointly analyzing data to identify the most effective tests from the original CGA and develop a short version; and (3) applying the short CGA separately to both groups. Results: The most effective tests—the Lawton scale, SPPB, Barber questionnaire, Pittsburgh Sleep Quality Index, and Cognitive Complaints questionnaire—were used to create a short CGA. It identified abnormalities in 77% of PWH flagged by the full CGA, though 65% with the normal short CGA results had at least one abnormal result in the full version. Most false negatives were due to the excluded Hearing-Dependent Activities scale. Conclusions: These findings represent an initial step toward developing a short CGA for an easy and rapid identification of PWH aged ≥ 50, beyond a frailty assessment, who may benefit from early clinical management.

BackgroundThe intestinal microbiota composition has been linked to neurocognitive impairment in people with HIV (PWH). However, the potential interplay of microbial species and related metabolites, particularly in the context of an HIV cure strategy remains underexplored. The BCN02 trial evaluated the impact of romidepsin (RMD), used as a HIV-1 latency reversing agent and with reported beneficial neurological effects, combined with the MVA.HIVconsv vaccine on virus control during 32-weeks of monitored antiretroviral treatment interruption (MAP) in early-treated HIV-infected individuals. Here, we analyzed longitudinal gut microbiome, plasma metabolome and brain functioning data to identify potential associations and novel putative biomarkers of HIV-associated neurocognitive disorders (HAND).MethodsData from fecal shotgun metagenomics, plasma metabolome, cognitive (standardized neuropsychological test score covering 6 cognitive domains, NPZ-6), functional (neuropsychiatric symptoms) and neuroimaging assessments were obtained and evaluated in 18 participants before and after RMD administration, and at the study end (post-MAP follow-up) in the BCN02 trial.ResultsParticipants with neurocognitive impairment (Lower vs. Higher NPZ-6 score group) were enriched in bacterial species, including Desulfovibrio desulfuricans, Sutterella wadsworthensis and Streptococcus thermophilus, and showed higher 1,2-propanediol degradation microbial pathway levels, before RMD administration. A multi-omics profiling showed significant and positive correlations between these microbial features and lipid-related metabolic pathways, previously linked to neurological disorders (i.e., sphingolipid, ether lipid, and glycerophospholipid metabolism), in participants with neurocognitive impairment, before RMD administration. Three indices (microbial-, metabolite-based and combined) obtained from the discriminant features were assessed longitudinally, showing progressive similarities between NPZ-6 score groups over time. Furthermore, the three indices and related discriminant features correlated negatively with functional outcomes, such as quality of life and daily functioning, and positively with depression, stress and CNS-related symptoms before RMD administration, while these associations became less discernible at the subsequent timepoints.ConclusionsWhile the direct effect of the intervention on the observed shifts cannot be conclusively determined in this study settings, these findings strengthen the link between gut bacteria, related metabolites, and neurocognitive function in PWH, and provide an analytical framework for future validation studies aimed at discovering predictive biomarkers for neurocognitive impairment in PWH.

BackgroundThe neurological symptoms of Long COVID (LC) and the impact of neuropsychological manifestations on people’s daily lives have been extensively described. Although a large body of literature describes symptoms, validating this with objective measures is important. This study aims to identify and describe the effects of Long COVID on cognition, balance, and the retinal fundus, and determine whether the duration of symptoms influences cognitive impairment.MethodsThis cross-sectional study involved LC volunteers with cognitive complaint from public health centers in northern Barcelona who participated between January 2022 and March 2023. This study collected sociodemographic characteristics, information on substance use, comorbidities, and clinical data related to COVID-19. We measured five cognitive domains using a battery of neuropsychological tests. Balance was assessed through posturography and retinal vascular involvement by retinography.ResultsA total of 166 people with LC and cognitive complaints participated, 80.72% were women and mean age was 49.28 ± 8.39 years. The most common self-reported symptoms were concentration and memory deficit (98.80%), brain fog (82.53%) and insomnia (71.17%). The 68.67% presented cognitive deficit in at least one domain, with executive functions being the most frequent (43.98%). The 51.52% of the participants exhibited a dysfunctional pattern in balance, and 9.2% showed some alteration in the retina. There were no statistically significant differences between cognitive impairment and symptom duration.ConclusionOur findings contribute to a more comprehensive understanding of the pathology associated with Long COVID. They highlight the diversity of self-reported symptoms, the presence of abnormal balance patterns, and some cognitive impairment. These findings underscore the necessity of addressing the clinical management of this condition in primary care through follow-up and the pursuit of multidisciplinary and comprehensive treatment.

Neurocognitive disorders are frequently reported in people living with HIV (PLWH) even with the introduction of combined antiretroviral treatment (cART). To identify biomarkers and potential therapeutic tools to target HIV infection in peripheral blood and in the central nervous system (CNS), plasma proteomics were applied in untreated chronic HIV-infected individuals with different levels of virus control.

BackgroundWearable sensors-based systems have emerged as a potential tool to continuously monitor Parkinson's Disease (PD) motor features in free-living environments.ObjectivesTo analyse the responsivity of wearable inertial sensor (WIS) measures (On/Off-Time, dyskinesia, freezing of gait (FoG) and gait parameters) after treatment adjustments. We also aim to study the ability of the sensor in the detection of MF, dyskinesia, FoG and the percentage of Off-Time, under ambulatory conditions of use.MethodsWe conducted an observational, open-label study. PD patients wore a validated WIS (STAT-ONTM) for one week (before treatment), and one week, three months after therapeutic changes. The patients were analyzed into two groups according to whether treatment changes had been indicated or not.ResultsThirty-nine PD patients were included in the study (PD duration 8 ± 3.5 years). Treatment changes were made in 29 patients (85%). When comparing the two groups (treatment intervention vs no intervention), the WIS detected significant changes in the mean percentage of Off-Time (p = 0.007), the mean percentage of On-Time (p = 0.002), the number of steps (p = 0.008) and the gait fluidity (p = 0.004). The mean percentage of Off-Time among the patients who decreased their Off-Time (79% of patients) was -7.54 ± 5.26. The mean percentage of On-Time among the patients that increased their On-Time (59% of patients) was 8.9 ± 6.46. The Spearman correlation between the mean fluidity of the stride and the UPDRS-III- Factor I was 0.6 (p = <0.001). The system detected motor fluctuations (MF) in thirty-seven patients (95%), whilst dyskinesia and FoG were detected in fifteen (41%), and nine PD patients (23%), respectively. However, the kappa agreement analysis between the UPDRS-IV/clinical interview and the sensor was 0.089 for MF, 0.318 for dyskinesia and 0.481 for FoG.ConclusionsIt's feasible to use this sensor for monitoring PD treatment under ambulatory conditions. This system could serve as a complementary tool to assess PD motor complications and treatment adjustments, although more studies are required.

Background At least 5-10% of subjects surviving COVID-19 develop the post-COVID-19 condition (PCC) or "Long COVID". The clinical presentation of PCC is heterogeneous, its pathogenesis is being deciphered, and objective, validated biomarkers are lacking. It is unknown if PCC is a single entity or a heterogeneous syndrome with overlapping pathophysiological basis. The large US RECOVER study identified four clusters of subjects with PCC according to their presenting symptoms. However, the long-term clinical implications of PCC remain unknown.Methods We conducted a 2-year prospective cohort study of subjects surviving COVID-19, including individuals fulfilling the WHO PCC definition and subjects with full clinical recovery. We systematically collected postCOVID-19 symptoms using prespecified questionnaires and performed additional diagnostic imaging tests when needed. Factors associated with PCC were identified and modelled using logistic regression. Unsupervised clustering analysis was used to group subjects with PCC according to their presenting symptoms. Factors associated with PCC recovery were modelled using a direct acyclic graph approach.Findings The study included 548 individuals, 341 with PCC, followed for a median of 23 months (IQR 16.5-23.5), and 207 subjects fully recovered. In the model with the best fit, subjects who were male and had tertiary studies were less likely to develop PCC, whereas a history of headache, or presence of tachycardia, fatigue, neurocognitive and neurosensitive complaints and dyspnea at COVID-19 diagnosis predicted the development of PCC. The cluster analysis revealed the presence of three symptom clusters with an additive number of symptoms. Only 26 subjects (7.6%) recovered from PCC during follow-up; almost all of them (n = 24) belonged to the less symptomatic cluster A, dominated mainly by fatigue. Recovery from PCC was more likely in subjects who were male, required ICU admission, or had cardiovascular comorbidities, hyporexia and/or smell/taste alterations during acute COVID-19. Subjects presenting with muscle pain, impaired attention, dyspnea, or tachycardia, conversely, were less likely to recover from PCC.Interpretation Preexisting medical and socioeconomic factors, as well as acute COVID-19 symptoms, are associated with the development of and recovery from the PCC. Recovery is extremely rare during the first 2 years, posing a major challenge to healthcare systems.

Background Biomarkers predicting the outcome of HIV-1 virus control in natural infection and after therapeutic interventions in HIV-1 cure trials remain poorly defined. The BCN02 trial (NCT02616874), combined a T-cell vaccine with romidepsin (RMD), a cancer-drug that was used to promote HIV-1 latency reversal and which has also been shown to have beneficial effects on neurofunction. We conducted longitudinal plasma proteomics analyses in trial participants to define biomarkers associated with virus control during monitored antiretroviral pause (MAP) and to identify novel therapeutic targets that can improve future cure strategies.Methods BCN02 was a phase I, open-label, single-arm clinical trial in early-treated, HIV infected individuals. Longitudinal plasma proteomes were analyzed in 11 BCN02 participants, including 8 participants that showed a rapid HIV-1 plasma rebound during a monitored antiretroviral pause (MAP-NC, 'non-controllers') and 3 that remained off ART with sustained plasma viremia <2000 copies/ml (MAP-C, 'controllers'). Inflammatory and neurological proteomes in plasma were evaluated and integration data analysis (viral and neurocognitive parameters) was performed. Validation studies were conducted in a cohort of untreated HIV-1+ individuals (n = 96) and in vitro viral replication assays using an anti-CD33 antibody were used for functional validation.Findings Inflammatory plasma proteomes in BCN02 participants showed marked longitudinal alterations. Strong proteome differences were also observed between MAP-C and MAP-NC, including in baseline timepoints. CD33/ Siglec-3 was the unique plasma marker with the ability to discriminate between MAPC-C and MAP-NC at all study timepoints and showed positive correlations with viral parameters. Analyses in an untreated cohort of PLWH confirmed the positive correlation between viral parameters and CD33 plasma levels, as well as PBMC gene expression. Finally, adding an anti-CD33 antibody to in vitro virus cultures significantly reduced HIV-1 replication and proviral levels in T cells and macrophages.Interpretation This study indicates that CD33/Siglec-3 may serve as a predictor of HIV-1 control and as potential therapeutic tool to improve future cure strategies.Funding Spanish Science and Innovation Ministry (SAF2017-89726-R and PID2020-119710RB-I00), NIH (P01-AI131568), European Commission (GA101057548) and a Grifols research agreement.Copyright & COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

The diagnosis of the post-COVID condition is usually achieved by excluding other diseases; however, cognitive changes are often found in the post-COVID disorder. Therefore, monitoring and treating the recovery from the post-COVID condition is necessary to establish biomarkers to guide the diagnosis of symptoms, including cognitive impairment. Our study employs a prospected cohort and nested case–control design with mixed methods, including statistical analyses, interviews, and focus groups. Our main aim is to identify biomarkers (functional and structural neural changes, inflammatory and immune status, vascular and vestibular signs and symptoms) easily applied in primary care to detect cognitive changes in post-COVID cases. The results will open up a new line of research to inform diagnostic and therapeutic decisions with special considerations for cognitive impairment in the post-COVID condition.

Integrase strand transfer inhibitors (INSTI) are a main component of the current antiretroviral regimens recommended for treatment of HIV infection. However, little is known about the impact of INSTI on neurocognition and neuroimaging. We developed a prospective observational trial to evaluate the effects of INSTI-based antiretroviral therapy on comprehensive brain outcomes (cognitive, functional, and imaging) according to the time since HIV-1 acquisition. We recruited men living with HIV who initiated antiretroviral therapy with INSTI < 3 months since the estimated date of HIV-1 acquisition (n = 12) and > 6 months since estimated date of HIV-1 acquisition (n = 15). We also recruited a group of matched seronegative individuals (n = 15). Assessments were performed at baseline (before initiation of therapy in HIV arms) and at weeks 4 and 48. Baseline cognitive functioning was comparable between the arms. At week 48, we did not find cognitive differences between starting therapy with INSTI earlier than 3 months or later than 6 months after acquisition of HIV-1 infection. Functional status was poorer in individuals diagnosed earlier. This effect recovered 48 weeks after initiation of therapy. Regarding brain imaging, we found that men living with HIV initiating antiretroviral therapy later experienced a greater decrease in medial orbitofrontal cortex over time, with expected negative repercussions for decision-making tasks.

The NEUrocognitive (NEU) Screen is a practical tool proposed to screen for HIV-associated cognitive impairment in the clinical setting. This is a pencil-and-paper method that can be applied rapidly (≤10 minutes for administration) and has no copyright limitations. In this study, we aimed at investigating its diagnostic accuracy in an older population of persons living with HIV (PLWH), with cutoffs set at 30, 40, 50, and 60 years. Data were collected from a sample of 368 PLWH who underwent a comprehensive neuropsychological tests battery (gold standard). Results of statistical tests showed that accuracy of the NEU Screen increased with age of the participants. The highest degree of precision, with a sensitivity of 91% and specificity of 92%, was obtained for people ages 60 years or older (correct classification: 91%). These optimal results point to the great potential of the NEU Screen as a tool for detecting cognitive disorders in older PLWH.

Among Parkinson’s disease (PD) motor symptoms, freezing of gait (FOG) may be the most incapacitating. FOG episodes may result in falls and reduce patients’ quality of life. Accurate assessment of FOG would provide objective information to neurologists about the patient’s condition and the symptom’s characteristics, while it could enable non-pharmacologic support based on rhythmic cues. This paper is, to the best of our knowledge, the first study to propose a deep learning method for detecting FOG episodes in PD patients. This model is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach was evaluated using data collected by a waist-placed inertial measurement unit from 21 PD patients who manifested FOG episodes. These data were also employed to reproduce the state-of-the-art methodologies, which served to perform a comparative study to our FOG monitoring system. The results of this study demonstrate that our approach successfully outperforms the state-of-the-art methods for automatic FOG detection. Precisely, the deep learning model achieved 90% for the geometric mean between sensitivity and specificity, whereas the state-of-the-art methods were unable to surpass the 83% for the same metric.

Freezing of gait (FoG) is one of the most disturbing and incapacitating symptoms in Parkinson's disease. It is defined as a sudden block in effective stepping, provoking anxiety, stress and falls. FoG is usually evaluated by means of different questionnaires; however, this method has shown to be not reliable, since it is subjective due to its dependence on patients' and caregivers' judgment. Several authors have analyzed the usage of MEMS inertial systems to detect FoG with the aim of objectively evaluating it. So far, specific methods based on accelerometer's frequency response has been employed in many works; nonetheless, since they have been developed and tested in laboratory conditions, their performance is commonly poor when being used at patients' home. Therefore, this work proposes a new set of features that aims to detect FoG in real environments by using accelerometers. This set of features is compared with three previously reported approaches to detect FoG. The different feature sets are trained by means of several machine learning classifiers; furthermore, different window sizes are also evaluated. In addition, a greedy subset selection process is performed to reduce the computational load of the method and to enable a real-time implementation. Results show that the proposed method detects FoG at patients' home with 91.7% and 87.4% of sensitivity and specificity, respectively, enhancing the results of former methods between a 5% and 11% and providing a more balanced rate of true positives and true negatives. (c) 2017 Elsevier B.V. Allrightsreserved.

Among Parkinson's disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient's treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.

ObjectiveTo assess the efficacy and safety of transdermal rivastigmine for the treatment of HIV-associated cognitive impairment.MethodsWe recruited HIV-infected patients with cognitive impairment on stable antiretroviral therapy in a randomized controlled pilot trial with a 48-week follow-up. An additional assessment was held at 12 weeks. Participants received transdermal rivastigmine (9.5 mg daily), lithium (400 mg twice daily, titrated progressively), or remained in a control group (no new medication). The primary efficacy endpoint was change in a global cognitive score (NPZ-7). Secondary endpoints included change in specific cognitive measures, domains, and functional parameters. Safety covered the frequency of adverse events and changes in laboratory results.ResultsSeventy-six subjects were screened, and 29 were finally enrolled. Better cognitive outcomes were observed in all groups, although there were no significant differences between the arms (mean NPZ-7 change [SD]): rivastigmine, 0.35 (0.14); lithium, 0.25 (0.40); control, 0.20 (0.44) (p = 0.78). The rivastigmine group showed the highest positive trend (mean NPZ-7 [SD], baseline vs week 48): rivastigmine, -0.47 (0.22) vs -0.11 (0.29), p = 0.06; lithium, -0.50 (0.40) vs -0.26 (0.21), p = 0.22; control, -0.52 (0.34) vs -0.32 (0.52), p = 0.44. The cognitive domains with the highest positive trends were information processing speed at week 12 and executive function at week 48 (rivastigmine vs control): information processing speed, 0.35 (0.64) vs -0.13 (0.25), p = 0.17, d = 0.96; and executive functioning, 0.73 (0.33) vs 0.03 (0.74), p = 0.09, d = 1.18. No relevant changes were observed regarding functional outcomes. A total of 12 (41%) individuals dropped out of the study: 2 (20%) were due to medication-related effects in the rivastigmine group and 4 (36%) in the lithium group. No severe adverse events were reported.ConclusionsThe results from this small randomized trial indicate that transdermal rivastigmine did not provide significant cognitive benefits in people with HAND on stable antiretroviral therapy, even though positive trends were found in specific cognitive domains. Relevant tolerability issues were not observed.

Freezing of gait (FOG) is one of the most incapacitating symptoms among the motor alterations of Parkinson's disease (PD). Manifesting FOG episodes reduce patients' quality of life and their autonomy to perform daily living activities, while it may provoke falls. Accurate ambulatory FOG assessment would enable non-pharmacologic support based on cues and would provide relevant information to neurologists on the disease evolution. This paper presents a method for FOG detection based on deep learning and signal processing techniques. This is, to the best of our knowledge, the first time that FOG detection is addressed with deep learning. The evaluation of the model has been done based on the data from 15 PD patients who manifested FOG. An inertial measurement unit placed at the left side of the waist recorded tri-axial accelerometer, gyroscope and magnetometer signals. Our approach achieved comparable results to the state-of-the-art, reaching validation performances of 88.6% and 78% for sensitivity and specificity respectively.

The treatment of Parkinson's disease (PD) with levodopa is very effective. However, over time, motor complications (MCs) appear, restricting the patient from leading a normal life. One of the most disabling MCs is ON-OFF fluctuations. Gathering accurate information about the clinical status of the patient is essential for planning treatment and assessing its effect. Systems such as the REMPARK system, capable of accurately and reliably monitoring ON-OFF fluctuations, are of great interest.OBJECTIVE:To analyze the ability of the REMPARK System to detect ON-OFF fluctuations.METHODS:Forty-one patients with moderate to severe idiopathic PD were recruited according to the UK Parkinson's Disease Society Brain Bank criteria. Patients with motor fluctuations, freezing of gait and/or dyskinesia and who were able to walk unassisted in the OFF phase, were included in the study. Patients wore the REMPARK System for 3days and completed a diary of their motor state once every hour.RESULTS:The record obtained by the REMPARK System, compared with patient-completed diaries, demonstrated 97% sensitivity in detecting OFF states and 88% specificity (i.e., accuracy in detecting ON states).CONCLUSION:The REMPARK System detects an accurate evaluation of ON-OFF fluctuations in PD; this technology paves the way for an optimisation of the symptomatic control of PD motor symptoms as well as an accurate assessment of medication efficacy.

Inertial measurement units (IMUs) are devices used, among other fields, in health applications, since they are light, small and effective. More concretely, IMUs have been demonstrated to be useful in the monitoring of motor symptoms of Parkinson's disease (PD). In this sense, most of previous works have attempted to assess PD symptoms in controlled environments or short tests. This paper presents the design of an IMU, called 9 × 3, that aims to assess PD symptoms, enabling the possibility to perform a map of patients' symptoms at their homes during long periods. The device is able to acquire and store raw inertial data for artificial intelligence algorithmic training purposes. Furthermore, the presented IMU enables the real-time execution of the developed and embedded learning models. Results show the great flexibility of the 9 × 3, storing inertial information and algorithm outputs, sending messages to external devices and being able to detect freezing of gait and bradykinetic gait. Results obtained in 12 patients exhibit a sensitivity and specificity over 80%. Additionally, the system enables working 23 days (at waking hours) with a 1200 mAh battery and a sampling rate of 50 Hz, opening up the possibility to be used for other applications like wellbeing and sports.

This chapter describes the methodology developed and used to obtain the elements of the REMPARK system devoted to monitor the related PD motor symptoms, according to the specifications discussed in the previous chapter. The monitoring part basically consists in the waist-worn device and its embedded algorithms for the analysis of the PD patients’ movement and gait. The development of such a device to assess motor symptoms has been divided into several steps that are summarised below: • First, different questionnaires were administered to professionals and cargiveers to identify some system requirements and the most important symptoms to be monitored. This step has been already partially presented and used for the purpose of Chapter 3. The present chapter describes the details of these questionnaires and the obtained results. • A methodology was developed to detect these symptoms, which is based on inertial sensors and machine learning techniques. This methodology is presented in Section 4.3. • A database of signals was collected in order to develop automatic detection methods based on the mentioned methodology. The design of the experiment to collect such data and a summary of the data obtained are also detailed in Section 4.3.