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Background: Leukomalacia is a serious form of neonatal brain injury that often leads to neurodevelopmental impairment, and studies on neonatal leukomalacia and its long-term outcomes are lacking. The aim of this study was to analyze the clinical manifestations, imaging features, and long-term neurodevelopmental outcomes in preterm infants and term infants with leukomalacia., Methods: Newborns diagnosed with leukomalacia by head magnetic resonance imaging (MRI) and who were admitted to intensive care units from January 2015 to June 2020 were enrolled. All infants were followed up to June 2022 (2-7 years old), and their neurodevelopmental outcomes were evaluated. The clinical data and long- term outcomes of preterm infants and term infants was analyzed by Chi-square tests., Results: A total of 218 surviving infants with leukomalacia including 114 preterm infants and 104 term infants completed the follow-up. The major typesof leukomalacia on MRI were periventricular leukomalacia in the preterm group and subcortical cystic leukomalacia in the term group, respectively (χ 2  = 55.166; p < 0.001). When followed up to 2-7 years old, the incidence of neurodevelopmental impairment in the preterm group and term group was not significantly different (χ 2  = 0.917; p = 0.338). However, the incidence of cerebral palsy (CP) in the preterm group was significantly higher (χ 2  = 4.896; p = 0.027), while the incidence of intellectual disability (ID) (χ 2  = 9.445; p = 0.002), epilepsy (EP) (χ 2  = 23.049; p < 0.001), and CP combined with ID andEP (χ 2  = 4.122; p = 0.042) was significantly lower than that in the term group., Conclusions: Periventricular leukomalacia mainly occurred in preterm infants while subcortical cystic leukomalacia was commonly seen in term infants. Although the long-term neurodevelopmental outcomes of leukomalacia were both poor, preterm infants were more prone to CP, while term infants were more prone to ID, EP, and the combination of CP with ID and EP., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Oxidative stress plays a crucial role in the pathogenesis of hepatic encephalopathy (HE), but the mechanism remains unclear. GABAergic neurons in substantia nigra pars reticulata (SNr) contribute to the motor deficit of HE. The present study aims to investigate the effects of oxidative stress on HE in male mice. The results validate the existence of oxidative stress in both liver and SNr across two murine models of HE induced by thioacetamide (TAA) and bile duct ligation (BDL). Systemic mitochondria-targeted antioxidative drug mitoquinone (Mito-Q) rescues mitochondrial dysfunction and oxidative injury in SNr, so as to restore the locomotor impairment in TAA and BDL mice. Furthermore, the GAD2-expressing SNr population (SNr GAD2 ) is activated by HE. Both overexpression of mitochondrial uncoupling protein 2 (UCP2) targeted to SNr GAD2 and SNr GAD2 -targeted chemogenetic inhibition targeted to SNr GAD2 rescue mitochondrial dysfunction in TAA-induced HE. These results define the key role of oxidative stress in the pathogenesis of HE., (© 2023. The Author(s).)

Objective: The incidence of inappropriate and excessive empirical antibiotic therapy is unclear. The aim of this study was to determine the prevalence of different empirical antibiotic therapy prescriptions, related factors, and outcomes in hospitalized patients with bacterial infection., Methods: A retrospective cohort study was performed and patients with bacterial infection who were admitted between October 1, 2019, and September 30, 2020, were included. Multivariable analysis was performed by the logistic regression model., Results: A total of 536 (42.6%) of the 1257 included patients received inappropriate empirical antibiotic therapy (IEAT), and 368 (29.3%) patients received appropriate but unnecessarily broad-spectrum empirical antibiotic therapy (AUEAT). MDRO (adjusted OR 2.932 [95% CI 2.201~3.905]; p < 0.001) and fever on admission (adjusted OR 0.592 [95% CI 0.415~0.844]; p = 0.004) were correlates of IEAT; sepsis (adjusted OR 2.342 [95% CI 1.371~3.999]; p = 0.002), age (adjusted OR 1.019 [95% CI 1.008~1.030]; p < 0.001), MDRO (adjusted OR 0.664 [95% CI 0.469~0.941]; p = 0.021), and urinary tract infection (adjusted OR 0.352 [95% CI 0.203~0.611]; p < 0.001) were correlates of AUEAT. Patients who received AUEAT were more likely to have a poor prognosis (63 [17.8%] vs 101 [27.4%]; p = 0.002). Both IEAT (median [IQR], 24,971 [13,135-70,155] vs 31,489 [14,894-101,082] CNY; p = 0.007) and AUEAT (median [IQR], 24,971 [13,135-70,155] vs 30,960 [16,475-90,881] CNY; p = 0.002) increased hospital costs. 45.3% (570/1257) of patients were infected with MDRO and 62.9% of them received IEAT., Conclusion: Inappropriate and excessive empirical antibiotic use was widely prevalent among hospitalized patients. Either inappropriate or excessive use of antibiotics may increase the burden of healthcare costs, the latter of which may be associated with poor prognosis. Clinicians need to be more judicious in choosing antibiotic(s). The MDRO epidemic was severe, especially in patients who received IEAT. It is imperative to take effective measures to improve the current situation of antibiotic abuse and antimicrobial resistance., Competing Interests: The authors declare that they have no competing interests in this work., (© 2023 Luo et al.)

In pre-weaned ruminants, the microbiota colonizes rapidly in the rumen after birth and constantly interacts with the host to sustain health and metabolism. The developing microbial community is more malleable, so its manipulation may improve ruminant health and productivity as well as may have long-term effects on ruminants. Hence, understanding the process of rumen microbiota establishment is helpful for nutritional interventions of rumen microbiota in pre-weaned ruminants. This paper reviews the latest advances in the colonization of rumen microbiota while providing insights into the most suitable time for manipulating rumen microbial colonization in early life. In addition, different factors that affect rumen microbiota establishment during the pre-weaned ruminants are discussed in the current manuscript. The purpose of this review is to aid in the development of guidelines for manipulating rumen microbiota to improve animal productivity and health.

Human mobility influenced the spread of the COVID-19 virus, as revealed by the high spatiotemporal granularity location service data gathered from smart devices. We conducted time series clustering analysis to delineate the relationships between human mobility patterns (HMPs) and their social determinants in California (CA) using aggregated smart device tracking data from SafeGraph. We first identified four types of temporal patterns for five human mobility indicator changes by applying dynamic-time-warping self-organizing map clustering methods. We then performed an analysis of variance and linear discriminant analysis on the HMPs with 17 social, economic, and demographic variables. Asians, children under five, adults over 65, and individuals living below the poverty line were found to be among the top contributors to the HMPs, including the HMP with a significant increase in the median home dwelling time and the HMP with emerging weekly patterns in full-time and part-time work devices. Our findings show that the CA shelter-in-place policy had varying impacts on HMPs, with socially disadvantaged places showing less compliance. The HMPs may help practitioners to anticipate the efficacy of non-pharmaceutical interventions on cases and deaths in pandemics., Competing Interests: Conflicts of Interest: The authors declare no conflicts of interest.

There are sex differences in the severity, mechanisms, and outcomes of neonatal hypoxia-ischemia (HI) brain injury, and apoptosis-inducing factor (AIF) may play a critical role in this discrepancy. Based on previous findings that AIF overexpression aggravates neonatal HI brain injury, we further investigated potential sex differences in the severity and molecular mechanisms underlying the injury using mice that overexpress AIF from homozygous transgenes. We found that the male sex significantly aggravated AIF-driven brain damage, as indicated by the injury volume in the gray matter (2.25 times greater in males) and by the lost volume of subcortical white matter (1.71 greater in males) after HI. As compared to females, male mice exhibited more severe brain injury, correlating with reduced antioxidant capacities, more pronounced protein carbonylation and nitration, and increased neuronal cell death. Under physiological conditions (without HI), the doublecortin-positive area in the dentate gyrus of females was 1.15 times larger than in males, indicating that AIF upregulation effectively promoted neurogenesis in females in the long term. We also found that AIF stimulated carbohydrate metabolism in young males. Altogether, these findings corroborate earlier studies and further demonstrate that AIF is involved in oxidative stress, which contributes to the sex-specific differences observed in neonatal HI brain injury., (© 2022. The Author(s).)

Advances in measurement technology are producing increasingly time-resolved environmental exposure data. We aim to gain new insights into exposures and their potential health impacts by moving beyond simple summary statistics (e.g., means, maxima) to characterize more detailed features of high-frequency time series data. This study proposes a novel variant of the Self-Organizing Map (SOM) algorithm called Dynamic Time Warping Self-Organizing Map (DTW-SOM) for unsupervised pattern discovery in time series. This algorithm uses DTW, a similarity measure that optimally aligns interior patterns of sequential data, both as the similarity measure and training guide of the neural network. We applied DTW-SOM to a panel study monitoring indoor and outdoor residential temperature and particulate matter air pollution (PM 2.5 ) for 10 patients with asthma from 7 households near Salt Lake City, UT; the patients were followed for up to 373 days each. Compared to previous SOM algorithms using timestamp alignment on time series data, the DTW-SOM algorithm produced fewer quantization errors and more detailed diurnal patterns. DTW-SOM identified the expected typical diurnal patterns in outdoor temperature which varied by season, as well diurnal patterns in PM 2.5 which may be related to daily asthma outcomes. In summary, DTW-SOM is an innovative feature engineering method that can be applied to highly time-resolved environmental exposures assessed by sensors to identify typical diurnal (or hourly or monthly) patterns and provide new insights into the health effects of environmental exposures., (© 2021. The Author(s).)

Apoptosis inducing factor (AIF) has been shown to be a major contributor to neuron loss in the immature brain after hypoxia-ischemia (HI). Indeed, mice bearing a hypomorphic mutation causing reduced AIF expression are protected against neonatal HI. To further investigate the possible molecular mechanisms of this neuroprotection, we generated an AIF knock-in mouse by introduction of a latent transgene coding for flagged AIF protein into the Rosa26 locus, followed by its conditional activation by a ubiquitously expressed Cre recombinase. Such AIF transgenic mice overexpress the pro-apoptotic splice variant of AIF (AIF1) at both the mRNA (5.9 times higher) and protein level (2.4 times higher), but not the brain-specific AIF splice-isoform (AIF2). Excessive AIF did not have any apparent effects on the phenotype or physiological functions of the mice. However, brain injury (both gray and white matter) after neonatal HI was exacerbated in mice overexpressing AIF, coupled to enhanced translocation of mitochondrial AIF to the nucleus as well as enhanced caspase-3 activation in some brain regions, as indicated by immunohistochemistry. Altogether, these findings corroborate earlier studies demonstrating that AIF plays a causal role in neonatal HI brain injury.

Background: Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity., Objective: We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points., Methods: We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing., Results: In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities., Conclusions: In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected, it was slightly lower in the more real-world study using wrist-worn smartwatches in children (BREATHE) than in the more tightly controlled study using waist-worn smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data., (©Kenan Li, Rima Habre, Huiyu Deng, Robert Urman, John Morrison, Frank D Gilliland, José Luis Ambite, Dimitris Stripelis, Yao-Yi Chiang, Yijun Lin, Alex AT Bui, Christine King, Anahita Hosseini, Eleanne Van Vliet, Majid Sarrafzadeh, Sandrah P Eckel. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 07.02.2019.)

For disease network intervention, up-regulating enzyme activities is equally as important as down-regulating activities. However, the design of enzyme activators presents a challenging route for drug discovery. Previous studies have suggested that activating 15-lipoxygenase (15-LOX) is a promising strategy to intervene the arachidonic acid (AA) metabolite network and control inflammation. To prove this concept, we used a computational approach to discover a previously unknown allosteric site on 15-LOX. Both allosteric inhibitors and novel activators were discovered using this site. The influence of activating 15-LOX on the AA metabolite network was then investigated experimentally. The activator was found to increase levels of 15-LOX products and reduce production of pro-inflammatory mediators in human whole blood assays. These results demonstrate the promising therapeutic value of enzyme activators and aid in further development of activators of other proteins.

The abundant research examining aspects of social-ecological resilience, vulnerability, and hazards and risk assessment has yielded insights into these concepts and suggested the importance of quantifying them. Quantifying resilience is complicated by several factors including the varying definitions of the term applied in the research, difficulties involved in selecting and aggregating indicators of resilience, and the lack of empirical validation for the indices derived. This paper applies a new model, called the resilience inference measurement (RIM) model, to quantify resilience to climate-related hazards for 52 U.S. counties along the northern Gulf of Mexico. The RIM model uses three elements (exposure, damage, and recovery indicators) to denote two relationships (vulnerability and adaptability), and employs both K-means clustering and discriminant analysis to derive the resilience rankings, thus enabling validation and inference. The results yielded a classification accuracy of 94.2% with 28 predictor variables. The approach is theoretically sound and can be applied to derive resilience indices for other study areas at different spatial and temporal scales.