Browse Articles

Association between asymptomatic submicroscopic and microscopic malaria infections and anemia: A study in southern Benin

Introduction Recently, efforts to eliminate malaria have shifted focus from symptomatic cases alone to include asymptomatic carriers, who are now recognized as significant contributors to the disease’s transmission and control. This study examines the relationship between asymptomatic malaria infection and hemoglobin levels in Benin. Methods A cohort in Benin was enrolled and categorized into three age groups (under 5 years, 5–15 years, and over 15 years) for follow-up from August to November 2021. Participants were monitored over two months and assessed for malaria infection through microscopy and polymerase chain reaction (PCR) during their three visits. A questionnaire was employed to gather general and clinical characteristics. Multivariate models were utilized to analyze the associations between asymptomatic infection, anemia, and hemoglobin levels. Results Among 393 participants, 58.2% were diagnosed at enrolment with malaria via PCR, and 30.5% through microscopy. Anemia, defined as hemoglobin levels < 11 g/dL, was observed in 40.5% of the cohort. The risk of anemia declined with increasing age (OR for ages 5–15: 0.64, 95% CI [0.37; 1.09]; OR for over 15 years: 0.34, 95% CI [0.20; 0.59]) compared to children under 5. Individuals with both microscopic and submicroscopic infections exhibited higher odds of anemia (respectively OR = 4.15, 95% CI [2.41; 7.13] and OR = 2.09 [1.22; 3.57]) relative to those uninfected. Hemoglobin levels were consistently lower in participants with microscopic malaria across all age groups (β = -2.73, 95% CI [-3.41; -2.05] for those under 5, β = -1.35, 95% CI [-1.89; -0.82] for ages 5–15, and β = -0.72, 95% CI [-1.34; -0.07]) compared to non-infected individuals. Discussion Our findings suggest that asymptomatic malaria infections, including submicroscopic cases, are associated with anemia and decreased hemoglobin levels. This underscores the importance of employing ultrasensitive diagnostic methods for such infections and acknowledging their potential health implications.

Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine)

Modelling sensory attenuation as Bayesian causal inference across two datasets

Introduction To interact with the environment, it is crucial to distinguish between sensory information that is externally generated and inputs that are self-generated. The sensory consequences of one’s own movements tend to induce attenuated behavioral- and neural responses compared to externally generated inputs. We propose a computational model of sensory attenuation (SA) based on Bayesian Causal Inference, where SA occurs when an internal cause for sensory information is inferred. Methods Experiment 1investigates sensory attenuation during a stroking movement. Tactile stimuli on the stroking finger were suppressed, especially when they were predictable. Experiment 2 showed impaired delay detection between an arm movement and a video of the movement when participants were moving vs. when their arm was moved passively. We reconsider these results from the perspective of Bayesian Causal Inference (BCI). Using a hierarchical Markov Model (HMM) and variational message passing, we first qualitatively capture patterns of task behavior and sensory attenuation in simulations. Next, we identify participant-specific model parameters for both experiments using optimization. Results A sequential BCI model is well equipped to capture empirical patterns of SA across both datasets. Using participant-specific optimized model parameters, we find a good agreement between data and model predictions, with the model capturing both tactile detections in Experiment 1 and delay detections in Experiment 2. Discussion BCI is an appropriate framework to model sensory attenuation in humans. Computational models of sensory attenuation may help to bridge the gap across different sensory modalities and experimental paradigms and may contribute towards an improved description and understanding of deficits in specific patient groups (e.g. schizophrenia).

Elevated D-dimer on admission may predict poor prognosis in childhood influenza associated encephalopathy

Assessment of temporospatial and kinematic gait parameters using human pose estimation in patients with Parkinson’s disease: A comparison between near-frontal and lateral views

Gait disturbance is one of the most common symptoms in patients with Parkinson’s disease (PD) that is closely associated with poor clinical outcomes. Recently, video-based human pose estimation (HPE) technology has attracted attention as a cheaper and simpler method for performing gait analysis than marker-based 3D motion capture systems. However, it remains unclear whether video-based HPE is a feasible method for measuring temporospatial and kinematic gait parameters in patients with PD and how this function varies with camera position. In this study, treadmill and overground walking in 24 patients with early PD was measured using a motion capture system and two smartphone cameras placed on the near-frontal and lateral sides of the subjects. We compared the differences in temporospatial gait parameters and kinematic characteristics between joint position data obtained from the 3D motion capture system and the markerless HPE. Our results confirm the feasibility of analyzing gait in patients with PD using HPE. Although the near-frontal view, where the heel and toe are clearly visible, is effective for estimating temporal gait parameters, the lateral view is particularly well-suited for assessing spatial gait parameters and joint angles. However, in clinical settings where lateral recordings are not feasible, near-frontal view recordings can still serve as a practical alternative to motion capture systems.

Tool wear prediction based on XGBoost feature selection combined with PSO-BP network

Virtual reality perimetry compared to standard automated perimetry in adults with glaucoma: A systematic review

Purpose The purpose of this systematic review was to consolidate and summarize available data comparing virtual reality perimetry (VRP) with standard automated perimetry (SAP) in adults with glaucoma. Understanding the utility and diagnostic performance of emerging VRP technology may expand access to visual field testing but requires evidence-based validation. Methods A systematic literature search was conducted in 3 databases (PubMed Central, Embase, and Cochrane Central Register of Controlled Trials) from the date of inception to 10/09/2024. Eligibility criteria included randomized controlled trials or prospective or retrospective cohort studies that compared different modalities of VRP to SAP in adults >18 years of age with glaucoma. Studies were excluded if they were review articles, letters, case reports, abstract-only papers, unavailable full text, or non-English language. Identified studies were formally evaluated for risk of bias using the Newcastle-Ottawa tool. The study protocol was prospectively registered with PROSPERO in May 2023 (registration number: CRD42023429071). Results The literature search yielded 1657 results. After deduplication, abstract and title screening, 14 studies met inclusion criteria and were included in the final systematic review. Compared to Humphrey Field Analyzer or Octopus 900, 10 different VRP devices were included in our study: Oculus Quest, Smartphone-based Campimetry, Toronto Portable Perimeter, VirtualEye, Advance Vision Analyzer, VisuALL, Vivid Vision Perimeter, C3 fields visual field analyzer, Radius, and Virtual Field. Overall, published studies of VRP are promising; however, more work is required to better evaluate these devices, namely test-retest repeatability. Conclusions VRP holds strong potential to evaluate visual fields in adults with glaucoma, though further data is needed to validate emerging technologies and testing protocols. Eye providers may consider using these devices to monitor certain adults with glaucoma.

A method of coating life prediction based on high temperature thermal shock life test and three-dimensional heat transfer analysis

Correction: Achieving the 95 95 95 targets for all: A pathway to ending AIDS

Dose-dependent enhancement of in vitro osteogenic activity on strontium-decorated polyetheretherketone

Psychometric validation of the Cystic Fibrosis Impact Questionnaire (CF-IQ): A patient-reported outcome assessing impacts of cystic fibrosis

The Cystic Fibrosis (CF) Impact Questionnaire (CF-IQ) was qualitatively developed to assess the impact of CF in the context of treatment advancements and increased longevity. This study reports the CF-IQ validation. In this noninterventional validation study, people with CF completed the 40-item CF-IQ and validating patient-reported outcome measures (PROMs) via electronic diaries at enrollment (baseline) and at the 4-week follow-up. Validation consisted of modern methods and focus groups to finalize structural validity, and classical methods to assess internal consistency [1–3], test-retest reliability [4,5], concurrent validity [5], and known-groups validity [5] of the CF-IQ. At baseline, 214 adults completed the survey; 193 completed the follow-up survey. Unidimensional item response theory (IRT) models were separately fit to 5 prespecified domains (Control and Burden of CF Treatment Impacts, Physical Activity Impacts, Social Activity Impacts, Emotional Impacts, and Work/School Limitation Impacts). IRT local dependence (LD) statistics identified 17 redundant items. Two independent CF-patient focus groups (14 total patients) confirmed these findings, and the 17 items were dropped. Each domain defined on the final 23 items achieved the criterion of exact model fit as measured by the root mean squared error of approximation (RMSEA, values = 0), Internal consistency (Cronbach’s α) values ranged from 0.81 to 0.89, 4 of 5 domains achieved acceptable test-retest reliability, with intraclass correlation coefficient (ICC) values ≥ 0.7, acceptable concurrent validity was achieved for all domains, and known-groups validity was established. The novel CF-IQ is a psychometrically robust PROM capturing patient-centric impacts of CF in the context of the current standard of care.

Pharmacovigilance imbalance analysis of VEGFR-TKI-related taste and smell disorders

NKp46 enhances type 1 innate lymphoid cell proliferation and function and anti-acute myeloid leukemia activity

Abstract NKp46 is a critical regulator of natural killer (NK) cell immunity, but its function in non-NK innate immune cells remains unclear. Here, we show that NKp46 is indispensable for expressing IL-2 receptor-α (IL-2Rα) by non-NK liver-resident type-1 innate lymphoid cells (ILC1s). Deletion of NKp46 reduces IL-2Rα on ILC1s by downregulating NF-κB signaling, thus impairing ILC1 proliferation and cytotoxicity in vitro and in vivo. The binding of anti-NKp46 antibody to NKp46 triggers the activation of NF-κB, the expression of IL-2Rα, interferon-γ (IFN-γ), tumor necrosis factor (TNF), proliferation, and cytotoxicity. Functionally, NKp46 expressed on mouse ILC1s interacts with tumor cells through cell–cell contact, increasing ILC1 production of IFN-γ and TNF, and enhancing cytotoxicity. In a mouse model of acute myeloid leukemia, deletion of NKp46 impairs the ability of ILC1s to control tumor growth and reduces survival. This can be reversed by injecting NKp46+ ILC1s into NKp46 knock-out mice. Human NKp46+ ILC1s exhibit stronger cytokine production and cytotoxicity than their NKp46− counterparts, suggesting that NKp46 plays a similar role in humans. These findings identify an NKp46–NF-κB–IL-2Rα axis and suggest that activating NKp46 with an anti-NKp46 antibody may provide a potential strategy for anti-tumor innate immunity.

Low-resource MobileBERT for emotion recognition in imbalanced text datasets mitigating challenges with limited resources

Modern dialogue systems rely on emotion recognition in conversation (ERC) as a core element enabling empathetic and human-like interactions. However, the weak correlation between emotions and semantics poses significant challenges to emotion recognition in dialogue. Semantically similar utterances can express different types of emotions, depending on the context or speaker. In order to tackle this challenge, our paper proposes a novel loss called Focal Weighted Loss (FWL) with adversarial training and the compact language model MobileBERT. Our proposed loss function handles the problem of imbalanced emotion classification through Focal Weighted Loss and adversarial training and does not require large batch sizes or more computational resources. Our approach has been employed on four text emotion recognition benchmark datasets, MELD, EmoryNLP, DailyDialog and IEMOCAP demonstrating competitive performance. Extensive experiments on these benchmark datasets validate the effectiveness of our proposed FWL with adversarial training. This enables more human-like interactions on digital platforms. Our approach shows its potential to deliver competitive performance under limited resource constraints, comparable to large language models.

Characteristics and analysis of PM2.5 particles in a light-polluted atmosphere in winter

Bamboo-inspired ultra-strong nanofiber-reinforced composite hydrogels

Transcranial Doppler sonography follow-up study in mild vascular cognitive impairment

Background To date, few data to transcranial Doppler sonography (TCD) are available in patients with mild vascular cognitive impairment (VCI) at risk for vascular or mixed dementia. In a previous study in patients with mild VCI and cerebral small vessels disease, a hemodynamic pattern of cerebral hypoperfusion and enhanced vascular resistance were observed; however, longitudinal data are currently lacking. Here, we perform a clinical, psychopathological, and neurosonological follow-up of patients with VCI in order to monitor any progression and to identify TCD measures to detect it. Methods From the original cohort of 161 patients, 127 with VCI (mean age 73.6 ± 7.1; 67 males) were re-evaluated after 5.0 ± 1.8 years. Namely, the Montreal Cognitive Assessment (MoCA), the 17-items Hamilton Depression Rating Scale (HDRS), and the Stroop Color-Word Interference Test (StroopT) were administered to screen for global cognitive status, to quantify depressive symptoms, and to explore executive functions, respectively. Mean blood flow velocity (MBFV), peak systolic blood flow velocity (PSV), end-diastolic blood flow velocity (EDV), pulsatility index (PI), and resistivity index (RI) were recorded from the middle cerebral artery, bilaterally. Results At follow up, patients exhibited a significant worsening of both MoCA (21.7 ± 2.1 vs. 20.7 ± 2.0) and StroopT scores (57.4 ± 19.4 vs. 59.7 ± 18.6), whereas HDRS showed an improvement, although the mean raw score remained above the cut-off value for depression (10.3 ± 6.6 vs. 9.8 ± 6.3). MBFV, PSV, and EDV showed a significant increase in PSV and PI and a reduction in EDV. When focused to younger patients (<65 years), we confirmed the significant worsening of both MoCA and StroopT but not HDRS, as well as the significant changes in PI and RI. Finally, considering the differences (D) between baseline and follow-up, the following significant correlations emerged, although with a small-to-medium effect size for all of them: positive correlation between MBFV-D and MoCA-D and between RI-D and STROOP-D, and a negative significant correlation between RI-D and MoCA-D. Conclusions Notwithstanding some limitations, such as the lack of a control group and neuroimaging data at follow-up, TCD may contribute to the early detection, monitoring, and management of VCI patients at risk for dementia. Together with compatible clinical and cognitive features, the exploration of early TCD markers that possibly indicate a higher risk of progression might represent an intriguing research direction and a significant clinical perspective.

Attention-based interactive multi-level feature fusion for named entity recognition

A scalable solar-driven photocatalytic system for separated H2 and O2 production from water

Clustering analysis of Yue opera character tone trends based on quantum particle swarm optimization for fuzzy C-means

This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. The second-order difference method extracts tonal trend features. We introduce a fuzzy C-means clustering method enhanced by quantum particle swarm optimization (QPSO) to manage data uncertainties, improving classification accuracy and convergence speed. Additionally, we employ a cross-correlation function to eliminate uncertainties from tonal transition redundancies. We designed a detection algorithm using trend data to validate our clustering method, thereby enhancing the accuracy of the analysis of tonal ranges and potential models. This method detects whether Yue Opera adheres to traditional rhythmic norms and models the regularity of musical tones and vocal patterns. Simulation results reveal that our approach achieves a 91.4% accuracy in classifying vocal styles, surpassing traditional methods and demonstrating its potential for identifying various styles. This research offers technical support for Yue Opera music education and interdisciplinary research. The findings enhance the quality of artistic creation and performance in Yue Opera, ensuring its preservation and development.