Early Changes Research Articles

Early change in gastric-specific anxiety sensitivity as a predictor of eating disorder treatment outcome.

Eating disorders (EDs) are often accompanied by gastrointestinal (GI) distress. Anxiety sensitivity is the tendency to interpret sensations of anxiety as threatening or dangerous, and includes both broad physical symptoms (e.g., elevated heartrate) and GI-specific symptoms. Physical and GI-specific anxiety sensitivity may be important risk and maintaining factors in EDs. This study tested the hypothesis that greater reductions in both types of anxiety sensitivity during the first month of treatment would predict lower ED symptoms and trait anxiety at discharge and 6-month follow-up. Patients (n=424) in ED treatment reported physical and GI-specific anxiety sensitivity, ED symptoms, and trait anxiety at treatment admission, 1-month into treatment, discharge, and 6-month follow-up. Analyses were conducted with hierarchical linear regression with imputation, controlling for relevant covariates. Results indicated that early reduction in GI-specific but not general physical anxiety sensitivity predicted both lower ED symptoms and lower trait anxiety at discharge and 6-month follow-up. These findings demonstrate the importance of GI-specific anxiety sensitivity as a potential maintaining factor in EDs. Developing and refining treatments to target GI-specific anxiety sensitivity may have promise in improving the treatment not only of EDs, but also of commonly co-morbid anxiety disorders.

Automatic assessment of DWI-ASPECTS for acute ischemic stroke based on deep learning.

Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a standardized semi-quantitative method for early ischemic changes in acute ischemic stroke. However, ASPECTS is still affected by expert experience and inconsistent results between readers in clinical. This study aims to propose an automatic ASPECTS scoring model based on diffusion-weighted imaging (DWI) mode to help clinicians make accurate treatment plans. Eighty-two patients with stroke were included in the study. First, we designed a new deep learning network for segmenting ASPECTS scoring brain regions. The network is improved based on U-net, which integrates multiple modules. Second, we proposed using hybrid classifiers to classify brain regions. For brain regions with larger areas, we used brain grayscale comparison algorithm to train machine learning classifiers, while using hybrid feature training for brain regions with smaller areas. The average DICE coefficient of the segmented hindbrain area can reach 0.864. With the proposed hybrid classifier, our method performs significantly on both region-level ASPECTS and dichotomous ASPECTS. The sensitivity and accuracy on the test set are 95.51% and 93.43%, respectively. For dichotomous ASPECTS, the intraclass correlation coefficient (ICC) between our automated ASPECTS score and the expert reading was 0.87. This study proposed an automated model for ASPECTS scoring of patients with acute ischemic stroke based on DWI images. Experimental results show that the method of segmentation first and then classification is feasible. Our method has the potential to assist physicians in the Alberta Stroke Program with early CT scoring and clinical stroke diagnosis.

Cellular and Molecular Mechanisms of Neuronal Degeneration in Early-Stage Diabetic Retinopathy.

Studies on the early retinal changes in Diabetic Retinopathy (DR) have demonstrated that neurodegeneration precedes vascular abnormalities like microaneurysms or intraretinal hemorrhages. Therefore, there is a growing field of study to analyze the cellular and molecular pathways involved to allow for the development of novel therapeutics to prevent the onset or delay the progression of DR. Molecular Mechanisms: Oxidative stress and mitochondrial dysfunction contribute to neurodegeneration through pathways involving polyol, hexosamine, advanced glycation end products, and protein kinase C. Potential interventions targeting these pathways include aldose reductase inhibitors and protein kinase C inhibitors. Neurotrophic factor imbalances, notably brain-derived neurotrophic factor and nerve growth factor, also play a role in early neurodegeneration, and supplementation of these neurotrophic factors show promise in mitigating neurodegeneration. Cellular Mechanisms: Major cellular mechanisms of neurodegeneration include caspase-mediated apoptosis, glial cell reactivity, and glutamate excitotoxicity. Therefore, inhibitors of these pathways are potential therapeutic avenues. Vascular Component: The nitric oxide pathway, critical for neurovascular coupling, is disrupted in DR due to increased reactive oxygen species. Vascular Endothelial Growth Factor (VEGF), a long-known angiogenic factor, has demonstrated both damaging and neuroprotective effects, prompting a careful consideration of long-term anti-VEGF therapy. Current DR treatments primarily address vascular symptoms but fall short of preventing or halting the disease. Insights into the mechanisms of retinal neurodegeneration in the setting of diabetes mellitus not only enhance our understanding of DR but also pave the way for future therapeutic interventions aimed at preventing disease progression and preserving vision.

Markers of sensory dynamics in phenols-rich virgin olive oils under optimal storage conditions

Early changes in sensory quality of phenols-rich virgin olive oil (VOO) and their relationship with the chemical changes are less studied in the literature. Therefore, the objective of this study was to propose a predictive model of dynamics of sensory changes based on specific chemical markers. The evolution of the sensory quality of phenol-rich VOOs from Tuscan cultivars stored under optimal storage conditions (i.e., absence of light, no O2 exposure, low temperature) was investigated using a multi-step methodological approach combining sensory (official sensory analysis (so-called Panel Test), Descriptive Analysis and Temporal Dominance of Sensation) and chemical measurements. The sensory map from descriptive data was related to the phenolic and volatile profiles, measured using HPLC-DAD and HS-SPME-GC–MS, respectively. A predictive model of the sensory changes over storage based on chemical compounds was developed. Results showed that very early changes involving phenolic and volatile compounds profiles occur in VOOs stored under optimal storage conditions, which turn in changes in sensory properties evaluated by the official panel test, the descriptive analysis and the temporal dominance of sensation. Furthermore, a chemical marker of sensory dynamics of oils during storage was identified as the ratio between two groups of secoiridoids. The proposed model, supported by the mentioned chemical marker, has the potential of improving the control of sensory changes in phenols-rich virgin olive oils during storage in optimal conditions.

Assessment of regional and total skeletal metabolism using 18F-NaF PET/CT in patients with chronic kidney disease.

The study aims to assess regional and total bone metabolic activity in patients with chronic kidney disease using Na[18F]F PET and correlation between semi-quantitative indices and blood parameters. Seventy-two subjects (mean age 61.8 ± 13.8years) were included. Of these 24/72 patients had end-stage renal disease (ESRD) (GFR < 15mL/min/1.73 m2), 38/72 had chronic kidney disease (CKD) (GFR between 60 and 15mL/min/1.73 m2), and 10/72 were controls with normal renal function. All subjects underwent Na[18F]F PET-CT with a dose activity of 0.06mCi/Kg. Regional and total skeletal metabolism were assessed with mean SUVs in a skeletal volume of interest (VOI), bone to soft tissue index (B/S), global SUV mean (GSUV mean) of the whole bone, and uptake in the femoral neck. Statistically significant differences were observed in a number of 18F-NaF metrics like femoral neck metabolism in CKD and ERSD groups in comparison to control in right (P = 0.003) and left femur (P = 0.006), bone to soft tissue index in the femur (P = 0.016) and GSUV5 (P = 0.006). There is also a significant difference in SUV mean in lumbar vertebrae (L1-L4) among CKD, ESRD, and controls. There was a moderate correlation between 18F-NaF PET scan uptake and blood parameters such as ALP and PTH. Na[18F]F uptake parameters were significantly different in low versus high bone turnover state. The assessment of total skeleton and regional metabolism and bone turnover in CKD patients is feasible with Na[18F]F PET. Na[18F]F can help to detect early changes in bone metabolism and assess the progression of bone disease in this complex condition. Quantification with Na[18F]F PET might provide better assessment of the bone turnover. The difference in Na[18F]F uptake in CKD compared to controls is likely related to a change in bone turnover which, however, requires further validation.

Differential tractography and whole brain connectometry in primary motor area gliomas resection: A feasibility study

ObjectiveEstablish the evolution of the connectome before and after resection of motor area glioma using a comparison of connectome maps and high-definition differential tractography (DifT). MethodsDifT was done using normalized quantitative anisotropy (NQA) with DSI Studio. The quantitative analysis involved obtaining mean NQA and fractional anisotropy (FA) values for the disrupted pathways tracing the corticospinal tract (CST), and white fiber network changes over time. ResultsWe described the baseline tractography, DifT, and white matter network changes from two patients who underwent resection of an oligodendroglioma (Case 1) and an IDH mutant astrocytoma, grade 4 (Case 2). Case 1. There was a slight decrease in the diffusion signal of the compromised CST in the immediate postop. The NQA and FA values increased at the 1-year follow-up (0.18 vs. 0.32 and 0.35 vs. 0.44, respectively). Case 2. There was an important decrease in the immediate postop, followed by an increase in the follow-up. In the 1-year follow-up, the patient presented with radiation necrosis and tumor recurrence, increasing NQA from 0.18 in the preop to 0.29. Fiber network analysis: whole-brain connectome comparison demonstrated no significant changes in the immediate postop. However, in the 1-year follow up there was a notorious reorganization of the fibers in both cases, showing the decreased density of connections. ConclusionsConnectome studies and DifT constitute new potential tools to predict early reorganization changes in a patient’s networks, showing the brain plasticity capacity, and helping to establish timelines for the progression of the tumor and treatment-induced changes.

Diffusion Tensor Tractography in Patient with Cervical Spondylosis and Correlation with Clinical Findings

Introduction: Diffusion tensor imaging is a non-invasive advanced method that provides information on white matter microstructural integrity. Fractional anisotropy and apparent diffusion coefficient have been used to quantify the degree of axonal damage and demyelination. The study aims to assess the role of diffusion tensor parameters in detecting early myelopathic changes and correlate them with clinical findings. Methods: This cross-sectional prospective study including 60 patients with clinical history of cervical myelopathy was carried out at the Department of Radiology from June 2021 to July 2022. Nurick score was used to assess the severity clinically and diffusion tensor images of the cervical spine were obtained using 3 Tesla MRI. These diffusion tensor values at the level of maximum stenosis were compared with those of the non-stenotic level. A correlation between the diffusion tensor parameters and clinical Nurick score was done. Results: The fractional anisotropy values were significantly lower (0.51 ± 0.14 vs 0.76 ± 0.47) and apparent diffusion coefficient values were significantly higher (1.20 ± 0.20 vs 1.04 ± 0.12) at the level of maximum stenosis compared with non-stenotic levels. There was a moderate positive and strong negative correlation between the apparent diffusion coefficient and fractional anisotropy values with the Nurick score. Conclusions: A significant reduction in the fractional anisotropy and an increase in apparent diffusion coefficient values at the level of stenosis was observed in individuals without a high T2 signal within the cord. Thus, these parameters might have a potential role in the early diagnosis of cervical spondylotic myelopathy

Impact of Polyethylene Terephthalate Microplastics on Drosophila melanogaster Biological Profiles and Heat Shock Protein Levels.

Microplastics and nanoplastics are abundant in the environment. Further research is necessary to examine the consequences of microplastic contamination on living species, given its widespread presence. In our research, we determined the toxic effects of PET microplastics on Drosophila melanogaster at the cellular and genetic levels. Our study revealed severe cytotoxicity in the midgut of larvae and the induction of oxidative stress after 24 and 48 h of treatment, as indicated by the total protein, Cu-Zn SOD, CAT, and MDA contents. For the first time, cell damage in the reproductive parts of the ovaries of female flies, as well as in the accessory glands and testes of male flies, has been observed. Furthermore, a decline in reproductive health was noted, resulting in decreased fertility among the flies. By analyzing stress-related genes such as hsp83, hsp70, hsp60, and hsp26, we detected elevated expression of hsp83 and hsp70. Our study identified hsp83 as a specific biomarker for detecting early redox changes in cells caused by PET microplastics in all the treated groups, helping to elucidate the primary defense mechanism against PET microplastic toxicity. This study offers foundational insights into the emerging environmental threats posed by microplastics, revealing discernible alterations at the genetic level.

Early evaluation of circulating tumor DNA as marker of therapeutic efficacy and prognosis in breast cancer patients during primary systemic therapy

BackgroundWe assessed the potential role of serial circulating tumor DNA (ctDNA) as a biomarker to monitor treatment response to primary systemic therapy (PST) in breast cancer and evaluated the predictive value of ctDNA to further identify patients with residual disease. MethodsWe prospectively enrolled 208 plasma samples collected at three time points (before PST, after 2 cycles of treatment, before surgery) of 72 patients with stage Ⅱ-III breast cancer. Somatic mutations in plasma samples were identified using a customized 128-gene capture panel with next-generation sequencing. The correlation between early change in ctDNA levels and treatment response or long-term clinical outcomes was assessed. Results37 of 72 (51.4%) patients harbored detectable ctDNA alterations at baseline. Patients with complete response showed a larger decrease in ctDNA levels during PST. The median relative change of variant allele fraction (VAF) was −97.4%, −46.7%, and +21.1% for patients who subsequently had a complete response (n = 11), partial response (n = 11), and no response (n = 15) (p = 0.0012), respectively. In addition, the relative change of VAF between the pretreatment and first on-treatment blood draw exhibited the optimal predictive value to tumor response after PST (area under the curve, AUC = 0.7448, p = 0.02). More importantly, early change of ctDNA levels during treatment have significant prognostic value for patients with BC, there was a significant correlation between early decrease of VAF and longer recurrence-free survival compared to those with an VAF increase (HR = 12.54; 95% CI, 2.084 to 75.42, p = 0.0063). ConclusionEarly changes of ctDNA are strongly correlated with therapeutic efficacy to PST and clinical outcomes in BC patients. The integration of preoperative ctDNA evaluation could help improving the perioperative management for BC patients receiving PST.

Neuronal microstructural changes in the human brain are associated with neurocognitive aging.

Gray matter (GM) alterations play a role in aging-related disorders like Alzheimer's disease and related dementias, yet MRI studies mainly focus on macroscopic changes. Although reliable indicators of atrophy, morphological metrics like cortical thickness lack the sensitivity to detect early changes preceding visible atrophy. Our study aimed at exploring the potential of diffusion MRI in unveiling sensitive markers of cortical and subcortical age-related microstructural changes and assessing their associations with cognitive and behavioral deficits. We leveraged the Human Connectome Project-Aging cohort that included 707 participants (394 female; median age = 58, range = 36-90 years) and applied the powerful mean apparent diffusion propagator model to measure microstructural parameters, along with comprehensive behavioral and cognitive test scores. Both macro- and microstructural GM characteristics were strongly associated with age, with widespread significant microstructural correlations reflective of cellular morphological changes, reduced cellular density, increased extracellular volume, and increased membrane permeability. Importantly, when correlating MRI and cognitive test scores, our findings revealed no link between macrostructural volumetric changes and neurobehavioral performance. However, we found that cellular and extracellular alterations in cortical and subcortical GM regions were associated with neurobehavioral performance. Based on these findings, it is hypothesized that increased microstructural heterogeneity and decreased neurite orientation dispersion precede macrostructural changes, and that they play an important role in subsequent cognitive decline. These alterations are suggested to be early markers of neurocognitive performance that may distinctly aid in identifying the mechanisms underlying phenotypic aging and subsequent age-related functional decline.

CerviLearnNet: Advancing cervical cancer diagnosis with reinforcement learning-enhanced convolutional networks

Women tend to face many problems throughout their lives; cervical cancer is one of the most dangerous diseases that they can face, and it has many negative consequences. Regular screening and treatment of precancerous lesions play a vital role in the fight against cervical cancer. It is becoming increasingly common in medical practice to predict the early stages of serious illnesses, such as heart attacks, kidney failure, and cancer, using machine learning-based techniques. To overcome these obstacles, we propose the use of auxiliary modules and a special residual block, to record contextual interactions between object classes and to support the object reference strategy. Unlike the latest state-of-the-art classification method, we create a new architecture called the Reinforcement Learning Cancer Network, “RL-CancerNet”, which diagnoses cervical cancer with incredible accuracy. We trained and tested our method on two well-known publicly available datasets, SipaKMeD and Herlev, to assess it and enable comparisons with earlier methods. Cervical cancer images were labeled in this dataset; therefore, they had to be marked manually. Our study shows that, compared to previous approaches for the assignment of classifying cervical cancer as an early cellular change, the proposed approach generates a more reliable and stable image derived from images of datasets of vastly different sizes, indicating that it will be effective for other datasets.

Early transfusion patterns improve the Molecular International Prognostic Scoring System (IPSS-M) prediction in myelodysplastic syndromes.

The Molecular International Prognostic Scoring System (IPSS-M) is the new gold standard for diagnostic outcome prediction in patients with myelodysplastic syndromes (MDS). This study was designed to assess the additive prognostic impact of dynamic transfusion parameters during early follow-up. We retrieved complete transfusion data from 677 adult Swedish MDS patients included in the IPSS-M cohort. Time-dependent erythrocyte transfusion dependency (E-TD) was added to IPSS-M features and analyzed regarding overall survival and leukemic transformation (acute myeloid leukemia). A multistate Markov model was applied to assess the prognostic value of early changes in transfusion patterns. Specific clinical and genetic features were predicted for diagnostic and time-dependent transfusion patterns. Importantly, transfusion state both at diagnosis and within the first year strongly predicts outcomes in both lower (LR) and higher-risk (HR) MDSs. In multivariable analysis, 8-month landmark E-TD predicted shorter survival independently of IPSS-M (p<0.001). A predictive model based on IPSS-M and 8-month landmark E-TD performed significantly better than a model including only IPSS-M. Similar trends were observed in an independent validation cohort (n=218). Early transfusion patterns impacted both future transfusion requirements and outcomes in a multistate Markov model. The transfusion requirement is a robust and available clinical parameter incorporating the effects of first-line management. In MDS, it provides dynamic risk information independently of diagnostic IPSS-M and, in particular, clinical guidance to LR MDS patients eligible for potentially curative therapeutic intervention.

Machine Learning-Based Perivascular Space Volumetry in Alzheimer Disease.

Impaired perivascular clearance has been suggested as a contributing factor to the pathogenesis of Alzheimer disease (AD). However, it remains unresolved when the anatomy of the perivascular space (PVS) is altered during AD progression. Therefore, this study investigates the association between PVS volume and AD progression in cognitively unimpaired (CU) individuals, both with and without subjective cognitive decline (SCD), and in those clinically diagnosed with mild cognitive impairment (MCI) or mild AD. A convolutional neural network was trained using manually corrected, filter-based segmentations (n = 1000) to automatically segment the PVS in the centrum semiovale from interpolated, coronal T2-weighted magnetic resonance imaging scans (n = 894). These scans were sourced from the national German Center for Neurodegenerative Diseases Longitudinal Cognitive Impairment and Dementia Study. Convolutional neural network-based segmentations and those performed by a human rater were compared in terms of segmentation volume, identified PVS clusters, as well as Dice score. The comparison revealed good segmentation quality (Pearson correlation coefficient r = 0.70 with P < 0.0001 for PVS volume, detection rate in cluster analysis = 84.3%, and Dice score = 59.0%). Subsequent multivariate linear regression analysis, adjusted for participants' age, was performed to correlate PVS volume with clinical diagnoses, disease progression, cerebrospinal fluid biomarkers, lifestyle factors, and cognitive function. Cognitive function was assessed using the Mini-Mental State Examination, the Comprehensive Neuropsychological Test Battery, and the Cognitive Subscale of the 13-Item Alzheimer's Disease Assessment Scale. Multivariate analysis, adjusted for age, revealed that participants with AD and MCI, but not those with SCD, had significantly higher PVS volumes compared with CU participants without SCD (P = 0.001 for each group). Furthermore, CU participants who developed incident MCI within 4.5 years after the baseline assessment showed significantly higher PVS volumes at baseline compared with those who did not progress to MCI (P = 0.03). Cognitive function was negatively correlated with PVS volume across all participant groups (P ≤ 0.005 for each). No significant correlation was found between PVS volume and any of the following parameters: cerebrospinal fluid biomarkers, sleep quality, body mass index, nicotine consumption, or alcohol abuse. The very early changes of PVS volume may suggest that alterations in PVS function are involved in the pathophysiology of AD. Overall, the volumetric assessment of centrum semiovale PVS represents a very early imaging biomarker for AD.

IV Thrombolysis in Basilar Artery Occlusion: Outcomes and Comparison With Endovascular Thrombectomy.

Recanalization is considered a prerequisite for favorable outcome in basilar artery occlusion (BAO). Intravenous thrombolysis (IVT) has been successfully used for eligible patients with BAO well beyond the 4.5-hour time window but has been largely underrepresented in the best medical management arms in recent randomized controlled trials of recanalization therapy in BAO. We aimed to analyze the outcomes of patients with BAO treated with IVT only and to compare IVT with endovascular thrombectomy (EVT). This observational single-center, retrospective cohort study included consecutive patients with BAO treated with IVT and/or EVT up to 48 hours of symptom onset during 1995-2022. The primary outcome was favorable functional outcome (modified Rankin Scale 0-3) at 3 months collected by a stroke physician by phone. In the first part, we described the outcomes and factors associated with functional outcome in the IVT-only cohort during 1995-2022. In the second part, we used doubly robust inverse probability-weighted regression adjustment models to compare functional outcome of patients treated with IVT vs EVT+/-IVT during 2010-2022. In the whole cohort of 376 patients with acute BAO treated with recanalization therapy, 245 (65.2%) received only IVT. In the IVT-only cohort, most patients had moderate-to-severe clinical presentation (median NIH Stroke Scale 18) but no extensive early ischemic changes in the posterior circulation on admission. Half of them had onset-to-treatment time over 6 hours. 46.5% of the IVT-treated patients achieved 3-month favorable functional outcome, whereas mortality was 35.9%. sICH occurred in 11.1%. In a multivariable analysis, younger age, milder symptom severity, and less baseline ischemic changes predicted favorable functional outcome. In the 2010-2022 cohort, when compared with patients treated with EVT+/-IVT (n = 121), the IVT-only cohort (n = 122) had higher odds for favorable functional outcome (IVT 58.2% vs EVT 43.0% (aOR 2.82 [95% CI 1.31-6.05]). IVT alone produced outcomes comparable with those in recent trials of endovascular BAO recanalization. Furthermore, in head-to-head comparison in our cohort, the IVT-only approach was more often associated with favorable outcome than EVT+/-IVT. Thus, it should not be overlooked as the first-line recanalization therapy in acute BAO, even in longer time windows. This study provides Class IV evidence that tPA is as effective as thrombectomy for basilar artery thrombosis.

Longitudinal cognitive decline characterizes the profile of non-PD-manifest GBA1 mutation carriers

With disease-modifying treatment for Parkinson’s disease (PD) associated with variants in the glucocerebrosidase gene (GBA1) under way, the challenge to design clinical trials with non-PD-manifest GBA mutation carriers (GBA1NMC) comes within close reach. To delineate trajectories of motor and non-motor markers as well as serum neurofilament light (sNfL) levels and to evaluate clinical endpoints as outcomes for clinical trials in GBA1NMC, longitudinal data of 56 GBA1NMC carriers and 112 age- and sex-matched GBA1 wildtype participants (GBA1wildtype) with up to 9 years of follow-up was analyzed using linear mixed-effects models (LMEM) and Kaplan–Meier survival analysis of clinical endpoints for motor and cognitive function. GBA1NMC showed worse performance in Pegboard, 20 m fast walking, global cognition as well as in executive and memory function at baseline. Longitudinally, LMEM revealed a higher annual increase of the MDS-UPDRS III bradykinesia subscore in GBA1NMC compared to GBA1wildtype, but comparable trajectories of all other motor and non-motor markers as well as sNfL. Kaplan–Meier survival analysis showed a significantly earlier progression to clinical endpoints of cognitive decline in GBA1NMC. Incidence of PD was significantly higher in GBA1NMC. In conclusion, our study extends data on GBA1NMC indicating early cognitive decline as a potentially characteristic feature. Comprehensive longitudinal assessments of cognitive function are crucial to delineate the evolution of early changes in GBA1NMC enabling a more accurate stratification and allow for a more precise definition of trial design and sample size.

Associations between static and dynamic changes of platelet counts and in-hospital mortality in critical patients with acute heart failure

To investigate the predictive value of baseline platelet count and its short-term dynamic changes in the prognosis of patients with acute heart failure (AHF) in the intensive care unit. Patients diagnosed with AHF in the medical information mart for intensive care III and their clinical data were retrospectively filtered. Patients were divided into survivor and non-survivor groups based on their prognosis during hospitalization, and differences in baseline data between groups were compared. Logistic regression models and restricted cubic spline (RCS) plots were performed to evaluate the relationship between baseline platelet counts and in-hospital mortality. Changes and trends in platelet counts were compared between the survivor and non-survivor groups after adjusting for confounders with the generalized additive mixing model (GAMM). A total of 2930 critical patients with acute heart failure were included, of which 2720 were survivors and 210 were non-survivors. Multiple logistic regression models revealed that baseline platelet count was an independent factor in hospital mortality (OR 0.997, 95% CI 0.994–0.999, P-value = 0.018). The RCS plot demonstrated a U-shaped dose–response relationship between baseline platelet count and in-hospital mortality. GAMM analysis suggested that the platelet counts decreased and then increased in the survivor group and gradually decreased in the non-survivor group, with a gradual increase of difference between two groups. After adjusting for confounders, the mean daily increase was −6.014 (95% CI −7.076–4.953, P-value < 0.001). Baseline platelet demonstrated a U-shaped dose–response relationship with adverse outcomes in critical patients with AHF. Early elevation of platelet was correlated with higher in-hospital mortality, indicating that tracking early changes in platelet might help determine the short-term prognosis of critical patients with AHF.

Weight Loss Trajectories and Short-Term Prediction in an Online Weight Management Program.

The extent to which early weight loss in behavioral weight control interventions predicts long-term success remains unclear. In this study, we developed an algorithm aimed at classifying weight change trajectories and examined its ability to predict long-term weight loss based on weight early change. We utilized data from 667 de-identified individuals who participated in a commercial weight loss program (Instinct Health Science), comprising 69,363 weight records. Sequential polynomial regression models were employed to classify participants into distinct weight trajectory patterns based on key model parameters. Next, we applied multinomial logistic models to evaluate if early weight loss in the first 14 days and prolonged duration of participation were significantly associated with long-term weight loss patterns. The mean percentage of weight loss was 7.9 ± 5.1% over 133 ± 69 days. Our analysis revealed four main weight loss trajectory patterns: a steady decrease over time (30.6%), a decrease to a plateau with subsequent decline (15.8%), a decrease to a plateau with subsequent increase (46.9%), and no substantial decrease (6.7%). Early weight change rate and total participating duration emerged as significant factors in differentiating long-term weight loss patterns. These findings contribute to support the provision of tailored advice in the early phase of behavioral interventions for weight loss.

PTH treatment before cyclic joint loading improves cartilage health and attenuates load-induced osteoarthritis development in mice.

Osteoarthritis (OA) treatment is limited by the lack of effective nonsurgical interventions to slow disease progression. Here, we examined the contributions of the subchondral bone properties to OA development. We used parathyroid hormone (PTH) to modulate bone mass before OA initiation and alendronate (ALN) to inhibit bone remodeling during OA progression. We examined the spatiotemporal progression of joint damage by combining histopathological and transcriptomic analyses across joint tissues. The additive effect of PTH pretreatment before OA initiation and ALN treatment during OA progression most effectively attenuated load-induced OA pathology. Individually, PTH directly improved cartilage health and slowed the development of cartilage damage, whereas ALN primarily attenuated subchondral bone changes associated with OA progression. Joint damage reflected early transcriptomic changes. With both treatments, the structural changes were associated with early modulation of immunoregulation and immunoresponse pathways that may contribute to disease mechanisms. Overall, our results demonstrate the potential of subchondral bone-modifying therapies to slow the progression of OA.

Detraining among Athletes-Is Withdrawal of Adaptive Cardiovascular Changes a Hint for the Differential Diagnosis of Physically Active People?

An athlete's training aims to achieve the highest possible sports results by improving physical dispositions which lead to cardiac adaptive changes. The annual training cycle is divided into periods. The preparatory period begins with gradually increasing training intensity and volume until the competitive period occurs, when the athlete's maximum performance is expected. Finally, the athlete enters a phase of loss of fitness, which is called detraining. Detraining is a time of resting both physically and mentally from the training regime and usually lasts about 4 weeks for endurance athletes. We collected data from much research on athletes' detraining. According to these data, the earliest change after detraining seems to be a decrease in left ventricular wall thickness and left ventricular mass, followed by decreased performance parameters, diastolic diameter of the left ventricle and size of the left atrium. A reversal of adaptive changes affects the left heart chamber first, then the right atrium and, finally, the right ventricle. Training reduction is often proposed as a method of differentiating an athlete's heart from cardiomyopathies. The aim of this study is to consider the diagnostic value of detraining in differentiating athletes' hearts from cardiomyopathies. We suggest that detraining cannot be conclusive in differentiating the disease from adaptive changes. Although a withdrawal of the characteristic morphological, functional and electrocardiographic changes occurs in healthy athletes during detraining, it can also concern individuals with cardiomyopathies due to the lower expression of abnormal features after decreased training loads. Therefore, a quick diagnosis and individual assessments using imaging and genetic tests are essential to recommend a proper type of activity.

Soundscape Characterization Using Autoencoders and Unsupervised Learning.

Passive acoustic monitoring (PAM) through acoustic recorder units (ARUs) shows promise in detecting early landscape changes linked to functional and structural patterns, including species richness, acoustic diversity, community interactions, and human-induced threats. However, current approaches primarily rely on supervised methods, which require prior knowledge of collected datasets. This reliance poses challenges due to the large volumes of ARU data. In this work, we propose a non-supervised framework using autoencoders to extract soundscape features. We applied this framework to a dataset from Colombian landscapes captured by 31 audiomoth recorders. Our method generates clusters based on autoencoder features and represents cluster information with prototype spectrograms using centroid features and the decoder part of the neural network. Our analysis provides valuable insights into the distribution and temporal patterns of various sound compositions within the study area. By utilizing autoencoders, we identify significant soundscape patterns characterized by recurring and intense sound types across multiple frequency ranges. This comprehensive understanding of the study area's soundscape allows us to pinpoint crucial sound sources and gain deeper insights into its acoustic environment. Our results encourage further exploration of unsupervised algorithms in soundscape analysis as a promising alternative path for understanding and monitoring environmental changes.