Marker Of Progression Research Articles

Vascular Endothelial Growth Factor-B Blockade with CSL346 in Diabetic Kidney Disease: A Phase 2A Randomized Controlled Trial.

Increased vascular endothelial growth factor B (VEGF-B) expression in patients with diabetic kidney disease (DKD) is associated with increased lipid deposition in glomerular podocytes. Reducing VEGF-B activity in animal models of DKD using an anti-VEGF-B antibody improved histological evidence of glomerular injury and reduced albuminuria; effects attributed to prevention of ectopic lipid deposition in the kidney. CSL346 is a novel humanized monoclonal antibody that binds VEGF-B with high affinity. Targeting VEGF-B in patients with type 2 diabetes mellitus may improve DKD progression markers. An international, randomized, double-blind, placebo-controlled, phase 2a study (NCT04419467) assessed CSL346 (8 mg/kg or 16 mg/kg subcutaneously every 4 weeks for 12 weeks) in participants with type 2 diabetes mellitus and a urinary albumin-to-creatinine ratio (UACR) ≥150 mg/g (17.0 mg/mmol), and eGFR >20 mL/min/1.73m2. Efficacy, safety/tolerability, pharmacokinetics, and pharmacodynamics of CSL346 were evaluated. The primary analysis compared the change from baseline in log-transformed UACR between the two CSL346 dose groups combined versus placebo at Week 16. In total, 114 participants were randomized. CSL346 did not significantly reduce UACR compared with placebo at Week 16 (combined CSL346 group difference from placebo 95% confidence interval]: 4.0% [-14.7, 26.8]). Furthermore, no effect was seen in participant subgroups (degree of kidney impairment or sodium-glucose cotransporter 2 [SGLT2] inhibitor use) or on urinary biomarkers reflecting proximal tubular injury. CSL346 was generally well tolerated; however, diastolic blood pressure was significantly higher with CSL346 16 mg/kg versus placebo from Week 2 onwards, with differences ranging from +3.8 to +5.3 mmHg (P = 0.002 at Week 16). CSL346 did not reduce UACR compared with placebo at 16 weeks in participants with type 2 diabetes mellitus and DKD, and was associated with an increase in diastolic blood pressure.

Thewo Tibetan’s /ta33/ and /nə33/

Abstract This paper has three aims. First, in its form, this paper uses a narrative style to overtly highlight the iterative process used to identify the meaning of the two suffixes in question. Second, this paper seeks to describe the intricate workings of two verbal suffixes in Thewo Tibetan. Finally, this paper makes a defense of why these two suffixes belong to the category of evidentiality. Thewo Tibetan is spoken in China on the Gansu-Sichuan border. The suffixes /ta33/ and /nə33/ appear either directly after action verbs, or directly after the progressive aspect marker. /nə33/ is used to express that the speaker has sensory information of their claim at the time of the speech act. The temporal nature of the evidence, whether it is sensory information of the act itself or of the results of the act, is determined in part by the semantics of the verb (telic vs non-telic). In contrast, /ta33/ is used to express that at the time of the speech act, the speaker no longer has sensory evidence (but formerly did) of the stated action. Based upon these observations, this paper argues that temporal space and verbal semantics are key to understanding the cognition which underlies interpreting /nə33/ and /ta33/. As such, this paper makes the following three contributions: (1) In its form it provides an anecdote of failures and success along the path to identifying the function and meaning of two evidential suffixes; (2) It introduces two suffixes of the hitherto un-described Thewo Tibetan evidential system; and (3) It gives an in-depth analysis of these suffixes and offers evidence to support the claim that they are evidential suffixes.

The Effects of Epigallocatechin-3-Gallate Nutritional Supplementation in the Management of Multiple Sclerosis: A Systematic Review of Clinical Trials.

Multiple sclerosis (MS) is a chronic, debilitating neurological condition for which current treatments often focus on managing symptoms without curing the underlying disease. Recent studies have suggested that dietary supplements could potentially modify disease progression and enhance quality of life. This systematic review aims to evaluate the efficacy and safety of epigallocatechin-3-gallate (EGCG) as a dietary supplement in patients with MS, with a specific focus on its impact on disease progression, symptom management, and overall quality of life. We conducted a comprehensive systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, utilizing an exhaustive search across the databases PubMed, Scopus, and Web of Science up to 23 February 2024. Eligible studies were randomized controlled trials. Nine clinical trials involving 318 participants were analyzed, with dosages ranging from 600 mg to 1200 mg of EGCG daily, although most studies had only a 4-month follow-up period. Results indicated that EGCG supplementation, particularly when combined with coconut oil, led to significant improvements in metabolic health markers and functional abilities such as gait speed and balance. One trial observed significant improvements in the Berg balance scale score from an average of 49 to 52 after four months of treatment with 800 mg of EGCG daily. Additionally, interleukin-6 levels significantly decreased, suggesting anti-inflammatory effects. Measures of quality of life such as the Beck Depression Inventory (BDI) scale showed significant improvements after EGCG supplementation. However, primary outcomes like disease progression measured by the Expanded Disability Status Scale (EDSS) and Magnetic Resonance Imaging (MRI) of lesion activities showed minimal or no significant changes across most studies. EGCG supplementation appears to provide certain symptomatic and functional benefits in MS patients, particularly in terms of metabolic health and physical functionality. However, it does not significantly impact the primary disease progression markers such as EDSS scores and MRI lesions. These findings underscore the potential of EGCG as a supportive treatment in MS management, though its role in altering disease progression remains unclear. Future research should focus on long-term effects and optimal dosing to further elucidate its therapeutic potential.

Prognostic Clinical and Biological Markers for Amyotrophic Lateral Sclerosis Disease Progression: Validation and Implications for Clinical Trial Design and Analysis.

With increasing recognition of the value of incorporating prognostic markers into amyotrophic lateral sclerosis (ALS) trial design and analysis plans, there is a pressing need to understand which among the prevailing clinical and biochemical markers have real value, and how they can be optimally used. A subset of patients with ALS recruited through the multi-center Phenotype-Genotype-Biomarker study (clinicaltrials.gov: NCT02327845) was identified as "trial-like" based on meeting common trial eligibility criteria. Clinical phenotyping was performed by evaluators trained in relevant assessments. Serum neurofilament light (NfL) and phosphorylated neurofilament heavy (pNfH), urinary p75ECD, plasma microRNA-181, and an array of biochemical and clinical measures were evaluated for their prognostic value. Associations with functional progression were estimated by random-slopes mixed models of ALS functional rating scale-revised (ALSFRS-R) score. Associations with survival were estimated by log-rank test and Cox proportional hazards regression. Potential sample size savings from adjusting for given biomarkers in a hypothetical trial were estimated. Baseline serum NfL is a powerful prognostic biomarker, predicting survival and ALSFRS-R rate of decline. Serum NfL <40pg/ml and >100pg/ml correspond to future ALSFRS-R slopes of ~0.5 and 1.5 points/month, respectively. Serum NfL also adds value to the best available clinical predictors, encapsulated by the European Network to Cure ALS (ENCALS) predictor score. In models of functional decline, the addition of NfL yields ~25% sample size saving above those achieved by inclusion of either clinical predictors or ENCALS score alone. The prognostic value of serum pNfH, urinary p75ECD, and plasma miR-181ab is more limited. Among the multitude of biomarkers considered, only blood NfL adds value to the ENCALS prediction model and should be incorporated into analysis plans for all ongoing and future ALS trials. Defined thresholds of NfL might also be used in trial design, for enrichment or stratified randomisation, to improve trial efficiency. NIH (U01-NS107027, U54-NS092091). ALSA (16-TACL-242).

Airway bacterial microbiome signatures correlate with occupational pneumoconiosis progression

Recent evidence has pinpointed a key role of the microbiome in human respiratory health and disease. However, significant knowledge gaps still exist regarding the connection between bacterial communities and adverse effects caused by particulate matters (PMs). Here, we characterized the bacterial microbiome along different airway sites in occupational pneumoconiosis (OP) patients. The sequencing data revealed that OP patients exhibited distinct dysbiosis in the composition and function of the respiratory microbiota. To different extents, there was an overall increase in the colonization of microbiota, such as Streptococcus, implying a possible intrusion pathway provided by exogenous PMs. Compared to those of healthy subjects, unhealthy living habits (i.e., smoking) had a greater impact on microbiome changes in OP patients. Importantly, the associations between the bacterial community and disease indicators indicated that specific bacterial species, including Prevotella, Actinobacillus, and Leptotrichia, might be surrogate markers of OP disease progression. Collectively, our results highlighted the potential participation of the bacterial microbiota in the pathogenesis of respiratory diseases and helped in the discovery of microbiome-based diagnostics for PM-induced disorders.

Prediction of Future Parkinson Disease Using Plasma Proteins Combined With Clinical-Demographic Measures.

Identification of individuals at high risk of developing Parkinson disease (PD) several years before diagnosis is crucial for developing treatments to prevent or delay neurodegeneration. This study aimed to develop predictive models for PD risk that combine plasma proteins and easily accessible clinical-demographic variables. Using data from the UK Biobank (UKB), which recruited participants across the United Kingdom, we conducted a longitudinal study to identify predictors for incident PD. Participants with baseline plasma proteins and no PD were included. Through machine learning, we narrowed down predictors from a pool of 1,463 plasma proteins and 93 clinical-demographic. These predictors were then externally validated using the Parkinson's Progression Marker Initiative (PPMI) cohort. To further investigate the temporal trends of predictors, a nested case-control study was conducted within the UKB. A total of 52,503 participants without PD (median age 58, 54% female) were included. Over a median follow-up duration of 14.0 years, 751 individuals were diagnosed with PD (median age 65, 37% female). Using a forward selection approach, we selected a panel of 22 plasma proteins for optimal prediction. Using an ensemble tree-based Light Gradient Boosting Machine (LightGBM) algorithm, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.800 (95% CI 0.785-0.815). The LightGBM prediction model integrating both plasma proteins and clinical-demographic variables demonstrated enhanced predictive accuracy, with an AUC of 0.832 (95% CI 0.815-0.849). Key predictors identified included age, years of education, history of traumatic brain injury, and serum creatinine. The incorporation of 11 plasma proteins (neurofilament light, integrin subunit alpha V, hematopoietic PGD synthase, histamine N-methyltransferase, tubulin polymerization promoting protein family member 3, ectodysplasin A2 receptor, Latexin, interleukin-13 receptor subunit alpha-1, BAG family molecular chaperone regulator 3, tryptophanyl-TRNA synthetase, and secretogranin-2) augmented the model's predictive accuracy. External validation in the PPMI cohort confirmed the model's reliability, producing an AUC of 0.810 (95% CI 0.740-0.873). Notably, alterations in these predictors were detectable several years before the diagnosis of PD. Our findings support the potential utility of a machine learning-based model integrating clinical-demographic variables with plasma proteins to identify individuals at high risk for PD within the general population. Although these predictors have been validated by PPMI, additional validation in a more diverse population reflective of the general community is essential.

Exposure to household pesticides and Parkinson's disease in the Parkinson's Progression Markers Initiative cohort.

In the last decades, the association of household pesticide usage with Parkinson's disease (PD) has been poorly explored, with discordant results. Based on the Parkinson's Progression Markers Initiative (PPMI) cohort study, we analyzed (1) the association of household pesticide exposure with the development of PD and (2) the effect of household pesticides on progression of PD. Data from participants of the "FOllow Up persons with Neurologic Disease" (FOUND study) included in the PPMI cohort database were analyzed. The PPMI FOUND study applied the Parkinson's Disease Risk Factor Questionnaire to collect information regarding the use of pesticides in non-work settings during periods of life, and the lifetime pesticide exposure for each participant was estimated. We defined a high use of pesticides if the exposure estimate had a z-score higher than one standard deviation from the mean. Also, we evaluated longitudinal data of people with PD to analyze the effect of high use of household pesticides on disease progression according to motor impairment, cognitive dysfunction, depressive symptoms, and modification of motor clinical phenotype. We analyzed data from 206 people with PD and 64 healthy controls, almost all from the USA. High use of household pesticides was not associated with the odds of developing PD. Regarding PD progression, only cognitive dysfunction was associated with the high use of household fungicides (HR 5.64 per standard deviation increase in exposure estimate, 95% CI 1.41-22.6). Chronic exposure to household pesticides may impact the clinical progression of PD, especially cognitive symptoms.

Protocol and rationale for a randomized controlled SGLT2 inhibitor trial in pediatric and young adult populations with chronic kidney disease: DOUBLE PRO-TECT Alport.

Clinical trials have demonstrated positive cardiovascular and kidney outcomes of sodium-glucose-co-transporter-2 (SGLT2) inhibitors in adult patients with diabetic and other chronic kidney diseases (CKD). Whether benefits extend to children, teenagers, and young adults with early-stage CKD is unknown. For this reason, the DOUBLE PRO-TECT Alport trial (NCT05944016) will study the progression of albuminuria in young patients with Alport syndrome (AS), the most common hereditary CKD, to assess the safety and efficacy of the SGLT2-inhibitor dapagliflozin. Patients living with AS and chronically elevated albuminuria have a high risk of kidney failure before the age of 50 years. DOUBLE PRO-TECT Alport is a multicenter, randomized, double-blind, placebo-controlled trial (RCT). Participants (aged 10 to 39 years) must have a diagnosis of AS by genetic testing or kidney biopsy, be on a stable (> 3 months) maximum tolerated dose of a renin-angiotensin-system-inhibitor (RASi) and must have a Urinary Albumin to Creatinine Ratio (UACR) of >300 mg/g (pediatric) or >500 mg/g (adult).Eligible participants will be randomly assigned at a 2:1 ratio to 48 weeks of treatment with dapaglifozin 10 mg/day -to- matched placebo. Most participants are expected to be children with a normal glomerular filtration rate (eGFR). In addition to safety, the primary (change in UACR from baseline to Week 48) and key secondary (eGFR change from baseline to Week 52) efficacy outcomes will be analyzed with a mixed model repeated measures approach. Efficacy analyses will be performed primarily in the full analysis set according to the intention-to-treat principle. A sensitivity analysis will be performed using reference-based multiple imputation. DOUBLE PRO-TECT Alport will assess whether SGLT2-inhibitors can safely reduce change from baseline in UACR as a marker for progression of CKD in young patients living with AS.

Cognitive Performance Between Latino and White Non-Latino Individuals With Parkinson's Disease.

Cognitive impairment is a common nonmotor symptom in Parkinson's disease (PD). Individuals of Latino background are traditionally underrepresented in research on PD. Despite the fact that Latinos comprise 18% of the U.S. population, they commonly make up less than 5% of samples in studies of PD. Emerging evidence suggests that Latino individuals with PD may experience disparities relative to White non-Latinos in terms of having more severe motor symptoms, more severe depressive symptoms, and worse health-related quality of life. The purpose of the present study was to investigate differences in cognitive performance between Latino and White non-Latino individuals with PD and examine correlates of cognitive performance. Data were obtained from the Parkinson's Progression Markers Initiative. Participants included 60 Latino individuals with PD and 1,009 White non-Latino individuals with PD, all of whom were followed annually for up to 5 years. Participants completed neuropsychological tests of attention and working memory, processing speed, visuospatial functioning, verbal fluency, and immediate and delayed memory and recall. Relative to White non-Latino individuals with PD, Latino individuals with PD had significantly lower scores on the global measure of cognitive functioning, a test of processing speed, and tests of working memory and attention. Years of education was the strongest correlate of performance in these three cognitive domains among individuals in the Latino group. These findings provide initial evidence of disparities in cognitive functioning among Latino individuals with PD. Educational disadvantages may be one potential driver of these disparities.

Predicting Cerebrospinal Fluid Alpha-Synuclein Seed Amplification Assay Status from Demographics and Clinical Data.

To develop and externally validate models to predict probabilities of alpha-synuclein (a-syn) positive or negative status in vivo in a mixture of people with and without Parkinson's disease (PD) using easily accessible clinical predictors. Uni- and multi-variable logistic regression models were developed in a cohort of participants from the Parkinson Progression Marker Initiative (PPMI) study to predict cerebrospinal fluid (CSF) a-syn status as measured by seeding amplification assay (SAA). Models were externally validated in a cohort of participants from the Systemic Synuclein Sampling Study (S4) that had also measured CSF a-syn status using SAA. The PPMI model training/testing cohort consisted of 1260 participants, of which 76% had manifest PD with a mean (± standard deviation) disease duration of 1.2 (±1.6) years. Overall, 68.7% of the overall PPMI cohort (and 88.0% with PD of those with manifest PD) had positive CSF a-syn SAA status results. Variables from the full multivariable model to predict CSF a-syn SAA status included age- and sex-specific University of Pennsylvania Smell Identification Test (UPSIT) percentile values, sex, self-reported presence of constipation problems, leucine-rich repeat kinase 2 (LRRK2) genetic status and pathogenic variant, and GBA status. Internal performance of the model on PPMI data to predict CSF a-syn SAA status had an area under the receiver operating characteristic curve (AUROC) of 0.920, and sensitivity/specificity of 0.881/0.845. When this model was applied to the external S4 cohort, which included 71 participants (70.4% with manifest PD for a mean 5.1 (±4.8) years), it performed well, achieving an AUROC of 0.976, and sensitivity/specificity of 0.958/0.870. Models using only UPSIT percentile performed similarly well upon internal and external testing. Data-driven models using non-invasive clinical features can accurately predict CSF a-syn SAA positive and negative status in cohorts enriched for people living with PD. Scores from the UPSIT were highly significant in predicting a-syn SAA status.

A standardized clinical database for research in Chagas disease: The NHEPACHA network.

The NHEPACHA Iberoamerican Network, founded on the initiative of a group of researchers from Latin American countries and Spain, aims to establish a research framework for Chagas disease that encompasses diagnosis and treatment. For this purpose, the network has created a questionnaire to gather relevant data on epidemiological, clinical, diagnostic, and therapeutic aspects of the disease. This questionnaire was developed based on a consensus of expert members of the network, with the intention of collecting high-quality standardized data, which can be used interchangeably by the different research centers that make up the NHEPACHA network. Furthermore, the network intends to offer a clinical protocol that can be embraced by other researchers, facilitating comparability among published studies, as well as the development of therapeutic response and progression markers.

Motor progression trajectories and risk of mild cognitive impairment in Parkinson's disease: A latent class trajectory model from PPMI cohort.

Rare studies have investigated the association between heterogeneity of motor progression and risk of early cognitive impairment in Parkinson's disease (PD). In this study, we aim to identify distinct trajectories of motor progression longitudinally and investigate their impact on predicting mild cognitive impairment (MCI). A 5-year cohort including 415 PD patients at baseline was collected from the Parkinson's Progression Markers Initiative. The severity of motor symptoms was evaluated using the Movement Disorder Society Unified Parkinson's Disease Rating Scale part III. The latent class trajectory model and nonlinear mixed-effects model were used to analyze and delineate the longitudinal changes in motor symptoms. Propensity score matching (PSM) was used to minimize the impact of potential confounders. Cox proportional hazard models were applied to calculate hazard ratios for MCI, and a Kaplan-Meier curve was generated using the occurrence of MCI during the follow-up as the time-to-event. Two latent trajectories were identified: a mild and remitting motor symptoms class (Class 1, 33.01%) and a severe and progressive motor symptom class (Class 2, 66.99%). Patients in Class 2 initially exhibited severe motor symptoms that worsened progressively despite receiving anti-PD medications. In comparison, patients in Class 1 exhibited milder symptoms that improved following drug therapy and a slower progression. During a 5-year follow-up, patients in Class 2 showed a higher risk of developing MCI compared to those in Class 1 before PSM (Log-Rank 28.58, p < 0.001) and after PSM (Log-Rank 8.20, p = 0.004). PD patients with severe and progressive motor symptoms are more likely to develop MCI than those with mild and stable motor symptoms.

Genetic influence on microstructure integrity and motor progression in Parkinson's disease

BackgroundUp to 10 % of Parkinson's disease (PD) populations carry a genetic risk variant, which may not only increase one's chance of developing PD but also affect disease presentation and progression. We hypothesize motor impairment in genetic carriers of PD correlate to different patterns of microstructural changes over time. Design/methodsData were accessed from the Parkinson's Progression Markers Initiative (PPMI) project. Connectometry analyses were performed for GBA1+ PD, LRRK2+ PD, and sporadic PD correlating white matter structural changes, as measured by quantitative anisotropy (QA), with motor impairment, as measured by MDS-UPDRS III. ResultsThere was a negative correlation between QA and MDS-UPDRS III in all 3 cohorts at 48 months. In GBA1+ PD (n = 12), the white matter tracts identified were cortical and subcortical, while in the LRRK2+ PD (n = 18) and sporadic PD (n = 45) cohorts white tracts identified were primarily subcortical and within the brainstem. ConclusionsOur findings highlight the association between motor symptom progrerssion and structural connectivity in individuals with GBA1+ PD, LRRK2+ PD, and sporadic PD. Due to the small sample size, larger studies are needed in the future to confirm the findings.

Protection from oxygen-glucose deprivation by neurosteroid treatment in primary neurons and oligodendrocytes.

Preterm birth results in an increased risk of neonatal brain injury and neurobehavioural disorders. Despite the seriousness of these adverse outcomes, there are currently no effective therapies to protect the vulnerable developing brain. We propose that neurosteroid replacement therapy may be a novel approach in reducing detrimental neurological outcomes following preterm birth. The use of guinea pig primary neuronal and oligodendrocyte cultures with relevance to late gestation allows insight into the mechanisms behind the effectiveness of these treatments. Primary neuronal and oligodendrocyte cultures were derived from fetal guinea pig frontal cortex brain tissue at gestational age 62 (GA62). Cell cultures were pre-treated with either etifoxine (5µM) or zuranolone (1µm) for 24h prior to insult. Cells were then exposed to either oxygen-glucose deprivation (OGD; 0% O2 and no glucose DMEM; preterm birth insult) or sham (standard cell culture conditions; 25mM DMEM) for 2h. Lactate dehydrogenase assay (LDH) was performed following OGD as a measure of cytotoxicity. Relative mRNA expression of key neuronal and oligodendrocyte markers, as well as neuronal receptors and transporters, were quantified using high throughput (Fluidigm) RT-PCR. OGD significantly increased cellular cytotoxicity in both neurons and oligodendrocytes. Additionally, key neuronal marker mRNA expression was reduced following OGD, and oligodendrocytes displayed arrested mRNA expression of key markers of lineage progression. Treatment with etifoxine restored a number of parameters back to control levels, whereas treatment with zuranolone provided a robust improvement in all parameters examined. This study has demonstrated the neuroprotective potential of neurosteroid replacement therapy in a model of hypoxia related to preterm birth. Neuroprotection appears to be mediated through glutamate reduction and increased brain derived neurotrophic factor (BDNF). Future work is warranted in examining these treatments in vivo, with the overall aim to suppress preterm associated brain damage and reduce long term outcomes for affected offspring.

Pentraxin 3 as a marker of development and severity of stable coronary artery disease

PurposeInflammation plays a crucial role in the development of atherosclerotic plaques. Pentraxin 3 (PTX3) is produced at the site of inflammation and has been identified as a specific marker of atherosclerosis, vascular inflammation, and progression of the coronary artery disease (CAD).The aim of the study was to establish if PTX3 has potential relations with classical markers of cardiovascular risk, and if PTX3 may act as an independent risk factor of CAD occurrence and advancement. Materials and methodsThe study included 98 patients with stable CAD confirmed in coronary angiography (CAD group) (median age 65 interquartile range [IQR] 61–72 years; 72 ​% men). The control group consisted of 40 patients without CAD. ResultsThe CAD group had significantly higher PTX3 concentration compared to the control group. There was a correlation with age, male gender, lipid profile and intima-media thickness. There was no correlation between PTX3 concentration and the number of coronary vessels with significant atherosclerotic lesions and the advancement of atherosclerotic lesions on the Gensini scoring scale. The cut-off point was determined for 0.89 ​ng/ml for the exclusion of angiographically significant atherosclerotic lesions. ConclusionsPatients with CAD have significantly higher concentration of PTX3. There was no correlation between PTX3 and the advancement of angiographically significant atherosclerotic lesions in coronary arteries. Low PTX3 concentration may serve as an indicator for the absence of atherosclerosis.

Leveraging Hypotension Prediction Index to Forecast LPS-Induced Acute Lung Injury and Inflammation in a Porcine Model: Exploring the Role of Hypoxia-Inducible Factor in Circulatory Shock.

Acute respiratory distress syndrome (ARDS) is a critical illness in critically unwell patients, characterized by refractory hypoxemia and shock. This study evaluates an early detection tool and investigates the relationship between hypoxia and circulatory shock in ARDS, to improve diagnostic precision and therapy customization. We used a porcine model, inducing ARDS with mechanical ventilation and intratracheal plus intravenous lipopolysaccharide (LPS) injection. Hemodynamic changes were monitored using an Acumen IQ sensor and a ForeSight Elite sensor connected to the HemoSphere platform. We evaluated tissue damage, inflammatory response, and hypoxia-inducible factor (HIF) alterations using enzyme-linked immunosorbent assay and immunohistochemistry. The results showed severe hypotension and increased heart rates post-LPS exposure, with a notable rise in the hypotension prediction index (HPI) during acute lung injury (p = 0.024). Tissue oxygen saturation dropped considerably in the right brain region. Interestingly, post-injury HIF-2α levels were lower at the end of the experiment. Our findings imply that the HPI can effectively predict ARDS-related hypotension. HIF expression levels may serve as possible markers of rapid ARDS progression. Further research should be conducted on the clinical value of this novel approach in critical care, as well as the relationship between the HIF pathway and ARDS-associated hypotension.

Gender variability in machine learning based subcortical neuroimaging for Parkinson’s disease diagnosis

PurposeThis study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top-performing algorithms and assess gender-related differences in accuracy.Design/methodology/approachMultiple ML algorithms will be compared for their ability to classify PD vs healthy controls using MRI scans of the brain structures like the putamen, thalamus, brainstem, accumbens, amygdala, caudate, hippocampus and pallidum. Analysis will include gender-specific performance comparisons.FindingsThe study reveals that ML classifier performance in diagnosing PD varies across subcortical brain regions and shows gender differences. The Extra Trees classifier performed best in men (86.36% accuracy in the putamen), while Naive Bayes performed best in women (69.23%, amygdala). Regions like the accumbens, hippocampus and caudate showed moderate accuracy (65–70%) in men and poor performance in women. The results point out a significant gender-based performance gap, highlighting the need for gender-specific models to improve diagnostic precision across complex brain structures.Originality/valueThis study highlights the significant impact of gender on machine learning diagnosis of PD using data from subcortical brain regions. Our novel focus on these regions uncovers their diagnostic potential, improves model accuracy and emphasizes the need for gender-specific approaches in medical AI. This work could ultimately lead to earlier PD detection and more personalized treatment.

Decoding the muscle transcriptome of patients with late onset Pompe disease reveals markers of disease progression.

Late-onset Pompe Disease (LOPD) is a rare genetic disorder caused by the deficiency of acid alpha-glucosidase leading to progressive cellular dysfunction due to the accumulation of glycogen in the lysosome. The mechanism of relentless muscle damage - a classic manifestation of the disease - has been extensively studied by analysing the whole muscle tissue; however, little, if any, is known about transcriptional heterogeneity among nuclei within the multinucleated skeletal muscle cells. This is the first report of application of single nuclei RNA sequencing to uncover changes in the gene expression profile in muscle biopsies from eight patients with LOPD and four muscle samples from age and gender matched healthy controls. We matched these changes with histology findings using GeoMx Spatial Transcriptomics to compare the transcriptome of control myofibers from healthy individuals with non-vacuolated (histologically unaffected) and vacuolated (histologically affected) myofibers of LODP patients. We observed an increase in the proportion of slow and regenerative muscle fibers and macrophages in LOPD muscles. The expression of the genes involved in glycolysis was reduced, whereas the expression of the genes involved in the metabolism of lipids and amino acids was increased in non-vacuolated fibers, indicating early metabolic abnormalities. Additionally, we detected upregulation of autophagy genes, and downregulation of the genes involved in ribosomal and mitochondrial function leading to defective oxidative phosphorylation. The upregulation of the genes associated with inflammation, apoptosis and muscle regeneration was observed only in vacuolated fibers. Notably, enzyme replacement therapy - the only available therapy for the disease - showed a tendency to restore metabolism dysregulation, particularly within slow fibers. A combination of single nuclei RNA sequencing and spatial transcriptomics revealed the landscape of normal and the diseased muscle, and highlighted the early abnormalities associated with the disease progression. Thus, the application of these two new cutting-edge technologies provided insight into the molecular pathophysiology of muscle damage in LOPD and identified potential avenues for therapeutic intervention.

Utilising deep learning networks to classify ZEB2 expression images in cervical cancer.

Aims/Background Cervical cancer continues to be a significant cause of cancer-related deaths among women, especially in low-resource settings where screening and follow-up care are lacking. The transcription factor zinc finger E-box-binding homeobox 2 (ZEB2) has been identified as a potential marker for tumour aggressiveness and cancer progression in cervical cancer tissues. Methods This study presents a hybrid deep learning system developed to classify cervical cancer images based on ZEB2 expression. The system integrates multiple convolutional neural network models-EfficientNet, DenseNet, and InceptionNet-using ensemble voting. We utilised the gradient-weighted class activation mapping (Grad-CAM) visualisation technique to improve the interpretability of the decisions made by the convolutional neural networks. The dataset consisted of 649 annotated images, which were divided into training, validation, and testing sets. Results The hybrid model exhibited a high classification accuracy of 94.4% on the test set. The Grad-CAM visualisations offered insights into the model's decision-making process, emphasising the image regions crucial for classifying ZEB2 expression levels. Conclusion The proposed hybrid deep learning model presents an effective and interpretable method for the classification of cervical cancer based on ZEB2 expression. This approach holds the potential to substantially aid in early diagnosis, thereby potentially enhancing patient outcomes and mitigating healthcare costs. Future endeavours will concentrate on enhancing the model's accuracy and investigating its applicability to other cancer types.

The activities of daily living partially mediate the relationship between rapid eye movement sleep behavior disorder and depressive symptoms in Parkinson's disease.

A longitudinal study was conducted to investigate whether rapid eye movement sleep behavior disorder affect depression in patients with Parkinson's disease through activities of daily living. A total of 387 Parkinson's disease patients' six-year follow-up data (one follow-up per year) were obtained from the Parkinson's Progression Markers Initiative. To allow causal effects to manifest, this study increased the lag period and divided the data from the six follow-ups into two groups: wave 1 (wave refers to time points), wave 3, and wave 5 as one group, and wave 2, wave 4, and wave6 as the other group. The time interval between two time points in each group was two years. To comprehensively and deeply analyze the dynamic relationships between variables, accurately infer causal relationships, control for individual differences, and detect the stability of these relationships, this study constructed the fixed effects cross-lagged panel model (CLPM), the random effects CLPM (RE-CLPM) model, and the Equating CLPM and Equating RE-CLPM models with applied restriction conditions. Additionally, a reverse path was added to verify the reverse prediction effect. The most suitable data analysis model was selected to explore the relationships between the study variables. Furthermore, the longitudinal mediating effect of daily living activities between rapid eye movement sleep behavior disorder and depression was investigated. In the models, Equating cross-lagged panel model was the best. The lag effect was positive and significant. In wave 1, 3, 5, activities of daily living mediated 11.82% on the path from rapid eye movement sleep behavior disorder to depression; in wave 2, 4, 6, it mediated 13.13%. Therefore, attention should be paid to the treatment of activities of daily living. Longitudinal changes in activities of daily living have indirect effects on the relationship between rapid eye movement sleep behavior disorder and depression, which highlights the importance of changes in activities of daily living ability in Parkinson's disease patients with rapid eye movement sleep behavior disorder.