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Impairments in somatosensory function are a common and often debilitating consequence of neurological injury, with few effective interventions. Building on success in rehabilitation for motor dysfunction, the delivery of vagus nerve stimulation (VNS) combined with tactile rehabilitation has emerged as a potential approach to enhance recovery of somatosensation. In order to maximize the effectiveness of VNS therapy and promote translation to clinical implementation, we sought to optimize the stimulation paradigm and identify neural mechanisms that underlie VNS-dependent recovery. To do so, we characterized the effect of tactile rehabilitation combined with VNS across a range of stimulation intensities on recovery of somatosensory function in a rat model of chronic sensory loss in the forelimb. Consistent with previous studies in other applications, we find that moderate intensity VNS yields the most effective restoration of somatosensation, and both lower and higher VNS intensities fail to enhance recovery compared to rehabilitation without VNS. We next used the optimized, moderate intensity to evaluate the mechanisms that underlie recovery. We find that moderate intensity VNS enhances transcription of Arc, a canonical mediator of synaptic plasticity, in the cortex, and that transcript levels were correlated with the degree of somatosensory recovery. Moreover, we observe that blocking plasticity by depleting acetylcholine in the cortex prevents the VNS-dependent enhancement of somatosensory recovery. Collectively, these findings identify neural mechanisms that subserve VNS-dependent somatosensation recovery and provide a basis for selecting optimal stimulation parameters in order to facilitate translation of this potential intervention.

This article communicates results on regular depolarization cascades in periodically kicked feedforward chains of excitable two-dimensional FitzHugh–Nagumo systems driven by sufficiently strong excitatory forcing at the front node. The study documents a parameter exploration by way of changes to the forcing period, upon which the dynamics undergoes a transition from simple depolarization to more complex behavior, including the emergence of mixed-mode oscillations. Both rigorous studies and careful numerical observations are presented. In particular, we provide rigorous proofs for existence and stability of periodic traveling waves of depolarization, as well as the existence and propagation of a simple mixed-mode oscillation that features depolarization and refraction in alternating fashion. Detailed numerical investigation reveals a mechanism for the emergence of complex mixed-mode oscillations featuring a potentially high number of large amplitude voltage spikes interspersed by an occasional small amplitude reset that fails to cross threshold. Further careful numerical investigation provides insights into the propagation of this complex phenomenology in the downstream, where we see an effective filtration property of the network; the latter amounts to a successive reduction in the complexity of mixed-mode oscillations down the chain.

Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with similar properties. Generated structures can be utilized for virtual screening or training semi-supervized predictive models in the downstream tasks. While there are plenty of generative models, it is unclear how to compare and rank them. In this work, we introduce a benchmarking platform called Molecular Sets (MOSES) to standardize training and comparison of molecular generative models. MOSES provides training and testing datasets, and a set of metrics to evaluate the quality and diversity of generated structures. We have implemented and compared several molecular generation models and suggest to use our results as reference points for further advancements in generative chemistry research. The platform and source code are available at https://github.com/molecularsets/moses .

We identify the sodium leak channel non-selective protein (NALCN) as a key regulator of cancer metastasis and nonmalignant cell dissemination. Among 10,022 human cancers, NALCN loss-of-function mutations were enriched in gastric and colorectal cancers. Deletion of Nalcn from gastric, intestinal or pancreatic adenocarcinomas in mice did not alter tumor incidence, but markedly increased the number of circulating tumor cells (CTCs) and metastases. Treatment of these mice with gadolinium—a NALCN channel blocker—similarly increased CTCs and metastases. Deletion of Nalcn from mice that lacked oncogenic mutations and never developed cancer caused shedding of epithelial cells into the blood at levels equivalent to those seen in tumor-bearing animals. These cells trafficked to distant organs to form normal structures including lung epithelium, and kidney glomeruli and tubules. Thus, NALCN regulates cell shedding from solid tissues independent of cancer, divorcing this process from tumorigenesis and unmasking a potential new target for antimetastatic therapies. The ion channel NALCN regulates cell shedding in mice and enhances metastasis in mouse models of cancer. Disseminated cells without oncogenic mutations form normal structures at secondary sites, suggesting that cell shedding is a physiological process that is hijacked during tumorigenesis.

•Imaging of brain glucose metabolism using MR spectroscopic imaging and 2H labeling.•Direct (2H DMI at 7T) and indirect detection (1H QELT at 3T) were compared.•Comparable enrichment of labeled compounds was observed with both methods.•Reproduced 7T DMI results using 1H QELT at clinical 3T without additional hardware. Deuterium metabolic imaging (DMI) and quantitative exchange label turnover (QELT) are novel MR spectroscopy techniques for non-invasive imaging of human brain glucose and neurotransmitter metabolism with high clinical potential. Following oral or intravenous administration of non-ionizing [6,6′-2H2]-glucose, its uptake and synthesis of downstream metabolites can be mapped via direct or indirect detection of deuterium resonances using 2H MRSI (DMI) and 1H MRSI (QELT), respectively. The purpose of this study was to compare the dynamics of spatially resolved brain glucose metabolism, i.e., estimated concentration enrichment of deuterium labeled Glx (glutamate+glutamine) and Glc (glucose) acquired repeatedly in the same cohort of subjects using DMI at 7T and QELT at clinical 3T. Five volunteers (4 m/1f) were scanned in repeated sessions for 60 min after overnight fasting and 0.8 g/kg oral [6,6′-2H2]-glucose administration using time-resolved 3D 2H FID-MRSI with elliptical phase encoding at 7T and 3D 1H FID-MRSI with a non-Cartesian concentric ring trajectory readout at clinical 3T. One hour after oral tracer administration regionally averaged deuterium labeled Glx4 concentrations and the dynamics were not significantly different over all participants between 7T 2H DMI and 3T 1H QELT data for GM (1.29±0.15 vs. 1.38±0.26 mM, p=0.65 & 21±3 vs. 26±3 µM/min, p=0.22) and WM (1.10±0.13 vs. 0.91±0.24 mM, p=0.34 & 19±2 vs. 17±3 µM/min, p=0.48). Also, the observed time constants of dynamic Glc6 data in GM (24±14 vs. 19±7 min, p=0.65) and WM (28±19 vs. 18±9 min, p=0.43) dominated regions showed no significant differences. Between individual 2H and 1H data points a weak to moderate negative correlation was observed for Glx4 concentrations in GM (r=-0.52, p<0.001), and WM (r=-0.3, p<0.001) dominated regions, while a strong negative correlation was observed for Glc6 data GM (r=-0.61, p<0.001) and WM (r=-0.70, p<0.001). This study demonstrates that indirect detection of deuterium labeled compounds using 1H QELT MRSI at widely available clinical 3T without additional hardware is able to reproduce absolute concentration estimates of downstream glucose metabolites and the dynamics of glucose uptake compared to 2H DMI data acquired at 7T. This suggests significant potential for widespread application in clinical settings especially in environments with limited access to ultra-high field scanners and dedicated RF hardware.

European honey bees are highly important in crop pollination, increasing the value of global agricultural production by billions of dollars. Current knowledge about virulence and pathogenicity of Deformed wing virus (DWV), a major factor in honey bee colony mortality, is limited. With this study, we close the gap between field research and laboratory investigations by establishing a complete in vitro model for DWV pathogenesis. Infectious DWV was rescued from a molecular clone of a DWV-A genome that induces DWV symptoms such as crippled wings and discoloration. The expression of DWV proteins, production of infectious virus progeny, and DWV host cell tropism could be confirmed using newly generated anti-DWV monoclonal antibodies. The recombinant RNA fulfills Koch's postulates circumventing the need of virus isolation and propagation of pure virus cultures. In conclusion, we describe the development and application of a reverse genetics system for the study of DWV pathogenesis.

The development of sustainable alternatives to chemical and mechanical biofilm removal for submerged technical devices used in freshwater and marine environments represents a major technical challenge. In this context, the antibiotic impact of blue light with its low absorption underwater provides a potentially useful alternative. However, former technical limitations led to hours of treatment. Here, we applied high-power blue laser irradiation (1500 W) with a wavelength of 448 nm to demonstrate its strong antibiotic and algicidal effect on different bacteria and algae in seconds. High-power blue light treatment (139 W/cm2) for only 8.9 s led to the efficient deactivation of all tested organisms. Analyses of the underlying biological mechanisms revealed the absorption of the blue light by endogenous chromophores (flavins, tetrapyrroles) with the generation of reactive oxygen species (ROS). In agreement, Escherichia coli transcriptome analyses demonstrated a stress response at the level of DNA damage repair, respiration, and protein biosynthesis. Spectroscopic measurements of the irradiated algae indicated the irreversible damage of chlorophyll by photooxidation with the formation of singlet oxygen. In conclusion, high-power blue laser radiation provides a strong sustainable tool for the removal of biofouling in a very short time for applications in aquatic systems.

Criteria to identify patients who are ready to be liberated from mechanical ventilation (MV) are imprecise, often resulting in prolonged MV or reintubation, both of which are associated with adverse outcomes. Daily protocol-driven assessment of the need for MV leads to earlier extubation but requires dedicated personnel. We sought to determine whether machine learning (ML) applied to the electronic health record could predict next-day extubation. We examined 37 clinical features aggregated from 12AM-8AM on each patient-ICU-day from a single-center prospective cohort study of patients in our quaternary care medical ICU who received MV. We also tested our models on an external test set from a community hospital ICU in our health care system. We used three data encoding/imputation strategies and built XGBoost, LightGBM, logistic regression, LSTM, and RNN models to predict next-day extubation. We compared model predictions and actual events to examine how model-driven care might have differed from actual care. Our internal cohort included 448 patients and 3,095 ICU days, and our external test cohort had 333 patients and 2,835 ICU days. The best model (LSTM) predicted next-day extubation with an AUROC of 0.870 (95% CI 0.834–0.902) on the internal test cohort and 0.870 (95% CI 0.848–0.885) on the external test cohort. Across multiple model types, measures previously demonstrated to be important in determining readiness for extubation were found to be most informative, including plateau pressure and Richmond Agitation Sedation Scale (RASS) score. Our model often predicted patients to be stable for extubation in the days preceding their actual extubation, with 63.8% of predicted extubations occurring within three days of true extubation. Our findings suggest that an ML model may serve as a useful clinical decision support tool rather than complete replacement of clinical judgement. However, any ML-based model should be compared with protocol-based practice in a prospective, randomized controlled trial to determine improvement in outcomes while maintaining safety as well as cost effectiveness.

Comprehensive and comparable estimates of health spending in each country are a key input for health policy and planning, and are necessary to support the achievement of national and international health goals. Previous studies have tracked past and projected future health spending until 2040 and shown that, with economic development, countries tend to spend more on health per capita, with a decreasing share of spending from development assistance and out-of-pocket sources. We aimed to characterise the past, present, and predicted future of global health spending, with an emphasis on equity in spending across countries. We estimated domestic health spending for 195 countries and territories from 1995 to 2016, split into three categories—government, out-of-pocket, and prepaid private health spending—and estimated development assistance for health (DAH) from 1990 to 2018. We estimated future scenarios of health spending using an ensemble of linear mixed-effects models with time series specifications to project domestic health spending from 2017 through 2050 and DAH from 2019 through 2050. Data were extracted from a broad set of sources tracking health spending and revenue, and were standardised and converted to inflation-adjusted 2018 US dollars. Incomplete or low-quality data were modelled and uncertainty was estimated, leading to a complete data series of total, government, prepaid private, and out-of-pocket health spending, and DAH. Estimates are reported in 2018 US dollars, 2018 purchasing-power parity-adjusted dollars, and as a percentage of gross domestic product. We used demographic decomposition methods to assess a set of factors associated with changes in government health spending between 1995 and 2016 and to examine evidence to support the theory of the health financing transition. We projected two alternative future scenarios based on higher government health spending to assess the potential ability of governments to generate more resources for health. Between 1995 and 2016, health spending grew at a rate of 4·00% (95% uncertainty interval 3·89–4·12) annually, although it grew slower in per capita terms (2·72% [2·61–2·84]) and increased by less than $1 per capita over this period in 22 of 195 countries. The highest annual growth rates in per capita health spending were observed in upper-middle-income countries (5·55% [5·18–5·95]), mainly due to growth in government health spending, and in lower-middle-income countries (3·71% [3·10–4·34]), mainly from DAH. Health spending globally reached $8·0 trillion (7·8–8·1) in 2016 (comprising 8·6% [8·4–8·7] of the global economy and $10·3 trillion [10·1–10·6] in purchasing-power parity-adjusted dollars), with a per capita spending of US$5252 (5184–5319) in high-income countries, $491 (461–524) in upper-middle-income countries, $81 (74–89) in lower-middle-income countries, and $40 (38–43) in low-income countries. In 2016, 0·4% (0·3–0·4) of health spending globally was in low-income countries, despite these countries comprising 10·0% of the global population. In 2018, the largest proportion of DAH targeted HIV/AIDS ($9·5 billion, 24·3% of total DAH), although spending on other infectious diseases (excluding tuberculosis and malaria) grew fastest from 2010 to 2018 (6·27% per year). The leading sources of DAH were the USA and private philanthropy (excluding corporate donations and the Bill & Melinda Gates Foundation). For the first time, we included estimates of China's contribution to DAH ($644·7 million in 2018). Globally, health spending is projected to increase to $15·0 trillion (14·0–16·0) by 2050 (reaching 9·4% [7·6–11·3] of the global economy and $21·3 trillion [19·8–23·1] in purchasing-power parity-adjusted dollars), but at a lower growth rate of 1·84% (1·68–2·02) annually, and with continuing disparities in spending between countries. In 2050, we estimate that 0·6% (0·6–0·7) of health spending will occur in currently low-income countries, despite these countries comprising an estimated 15·7% of the global population by 2050. The ratio between per capita health spending in high-income and low-income countries was 130·2 (122·9–136·9) in 2016 and is projected to remain at similar levels in 2050 (125·9 [113·7–138·1]). The decomposition analysis identified governments’ increased prioritisation of the health sector and economic development as the strongest factors associated with increases in government health spending globally. Future government health spending scenarios suggest that, with greater prioritisation of the health sector and increased government spending, health spending per capita could more than double, with greater impacts in countries that currently have the lowest levels of government health spending. Financing for global health has increased steadily over the past two decades and is projected to continue increasing in the future, although at a slower pace of growth and with persistent disparities in per-capita health spending between countries. Out-of-pocket spending is projected to remain substantial outside of high-income countries. Many low-income countries are expected to remain dependent on development assistance, although with greater government spending, larger investments in health are feasible. In the absence of sustained new investments in health, increasing efficiency in health spending is essential to meet global health targets. Bill & Melinda Gates Foundation.

[...]the two main treatment modalities for TC include wide local excision (WLE) or Mohs micrographic surgery (MMS). Treatment outcomes of trichilemmal carcinoma based on method of surgical treatment Manuscript Demographics (Age gender) Anatomic site Pre-operative size (cm) Follow-up timeframe (months) Recurrence? Wide local excision cases 1987 Mehregan 34 M Left parietal scalp 5 × 3 × 2.5 12 N N ^ 72 M Left parietal scalp 5 × 6.5 × 2 8 N N ^ 73 F Scalp 2 × 2 × 1.5 6 N N 1992 Boscaino 68 M Leg 1.8 48 N N ^ 80 M Neck 2 36 N N ^ 62 F Nose 1.5 36 N N ^ 58 F Neck 1 24 N N ^ 85 M Neck 1 24 N N ^ 68 M Scalp 0.9 6 N N ^ 80 M Thigh 2.3 2 N N 1992 Swanson 77 M Right dorsal wrist 1.1 22 N N ^ 79 M Left shoulder 0.4 18 N N ^ 65 M Left temple 0.5 11 N N ^ 63 M Right pinna 1.3 36 N N ^ 87 M Left ear 2 13 N N ^ 88 M Nose 1.3 17 N N ^ 72 F Scalp 1.5 92 N N ^ 55 F Nose 0.8 48 N N 1993 Misago 92 F Right temple 5.0 × 3.5 18 N N 1994 Dekio 79 M Right thigh 1.5 × 1.7 0.5 N - patient died 2 weeks after excision due to strong dyspnea of unknown cause N 1996 Ko 73 M Left lower leg 4.3 32 N N ^ 43 M Parietal scalp 5 × 5 × 2 24 N N 1997 Yotsuyanagi 84 M Nasal dorsum 3 × 3 24 N N 1998 Markal 60 M Median Forehead 15 × 15 × 10 12 N N 1999 Chan 67 M Left supraclavicular fossa 1.5 24 Y N ^ Surgery #2 Left supraclavicular fossa 1.5 × 1.0 18 N N ^ Surgery #2 - New location Sternal angle 3.5 × 2.5 18 N N ^ Surgery #2 - New location Right second intercostal space 2.0 × 1.5 18 N N 2001 Bae 32 M Right parietal scalp - 4 Y Y ^ 32 M Right parietal scalp - 6 Y Y ^ 53 F Left frontal scalp 1.5 24 N N 2002 Plumb 54 M Left temporal scalp 2.3 × 2.3 × 1.5 12 N N 2005 Lee 42 F Right lower eyelid 3.0 × 2.0 31 N N 2007 Kanitakis Surgery #2 Left lower back 1 N N 2009 Garg 85 F Scalp 1.5 14 N N 2009 Lee 51 M Left upper palpebral conjunctivae 0.5 × 0.5 6 N N 2010 Kulahchi 20 M Scalp 3 6 Y N ^ Surgery #2 Scalp - 6 Y N ^ Surgery #3 Scalp - 12 N N 2011 Aneiros-Fernandez 63 M Right dorsal wrist 14 × 9 3 N N 2011 Harris 53 M Medial left orbit 4.5 × 5.5 2 N - died of unrelated causes at 2 months N 2012 Wang 79 M Vertex scalp 10 × 8 × 8 24 N N 2013 Chai 56 M Right scalp 12 × 8 × 6 12 N N 2013 MoraloğluÖ 39 F Left labium majus 1.6 × 1.5 48 Y N 2013 Wilkie 92 F Right postero-lateral neck 1 × 0.6 × 0.6 6 N N 2014 Dubhashi 26 F Right parietal scalp 6 × 4 1 Y N 2014 Tikku 63 M Right occipital scalp 4 × 3 × 2 18 N N 2015 Alici 52 F Scalp 2.5 24 N N 2015 Hiramatsu 63 M Left forehead 1.5 × 1.0 12 Y Y 2016 Liu 42 M Median Posterior Scalp 22 × 17 × 19 28 N N 2016 Sofianos 46 F Vertex scalp 5 × 5 3 Y Y 2017 Waqas 51 M Scalp - 48 N N 2018 Evrenos, Kerem, et al. 56 M Temporal 8 × 5 × 2.5 46 N N ^ 83 M Nasal dorsum 4.5 × 4.5 × 2.5 6 N N ^ 71 F Frontal 4 × 3.5 × 0.7 17 N N ^ 78 M Scalp 4 × 3.5 × 2 28 N N ^ 93 F Temporal 10 × 10 × 3 12 N N ^ 62 M Frontal 3 × 2.5 24 N N ^ 82 M Xiphoid/chest wall 4 × 5 4 N N ^ 79 M Frontal 4 × 4.3 18 N N 2018 Evrenos, Bali, et al. 80 F Posterior scalp 9 × 10 × 4 18 N N ^ 66 F Scalp 4.5 × 4 × 3 6 N N ^ 55 F Scalp 7 × 7 × 3.5 12 N N ^ 94 M Right temple 3 × 3 × 0.4 16 N Unknown ^ 66 F Right posterior scalp 15 × 16 × 5.5 7 N N 2020 Bhargava 82 M Posterior helix of right ear 2.0 × 1.5 5 N N 2021 Dboush 45 M Right axilla 5 24 N N 2021 Oley 82 F Right Nostril 15 × 8 36 N N 2022 Benslimane Kamal Unknown Vertex scalp 10 × 6 2 Y Unknown 2022 Jia 24 F Scalp 0.8 × 0.9 15 N N 2022 Mekkaoui 72 M Right forehead 2 6 N N 2022 Zhou 65 M Helical ear 3.0 × 2.0 14 N N 2023 Gao 88 M Medial right orbit 2 × 2 4 Y N 2023 Lee, Han, et al. 77 M Left elbow 3 × 2 12 N N 2023 Lee, Choi, et al. 89 F Left cheek 1 × 1.5 × 1.2 6 N N 2023 Sun 48 M Right temporal scalp 1.5 54 N N ^ 58 F Left occipital scalp 2.2 × 2.0 × 1.5 24 N N ^ 84 F Right breast - 6 N N 2024 Gorman 58 F Mid back 16 × 15 × 5 1.5 N N Mohs micrographic surgery cases 1997 Billingsley 60 M Right posterior helix 1 20 N N 2003 Allee 65 M Left cheek 4.5 × 3 cm 12 Y N ^ 65 M Left cheek 24 N N 2003 Garrett 59 M Chest 72 N N 2003 Lai 65 M Upper eyelid 1.5 × 0.6 24 N N 2004 Garrett 77 M Right cheek - 60 N N ^ 59 M Mid chest - 48 N N 2005 Tierney 33 M Frontal scalp 10 × 10 16 N N 2008 Kim 70 M Left cheek 1.0 × 1.0 19 N N 2010 Bajoghli 65 M Posterior scalp 5.5 × 5.0 7 N N 2015 Fielke 67 M Right frontal scalp 2.5 6 N N 2015 Tolkachjov 69 F Nasal dorsum 0.5 × 0.6 4 N N ^ 58 M Right ear 1.0 × 1.0 56 N N ^ 71 M Vertex calp 2.4 × 2.5 13 N N ^ 72 M Left retroauricular 2.8 × 1.9 37 N N ^ 59 M Forehead 2.0 × 3.0 50 N N ^ 67 F Left cheek 2.0 × 0.8 19 N N ^ 93 M Right preauricular - 12 N N 2018 Singh 56 F Left parietal scalp 12 N N 2021 Alshaalan 46 F Occipital scalp 2.5 × 2.3 13 N N ^ 54 F Left temporal scalp 2.2 × 1.4 11 N N M = male. Trichilemmal carcinoma [See PDF for image] Fig. 1 PRISMA flow diagram for systematic review Upon review of the cases in the literature, MMS for the treatment of TC demonstrated a decreased recurrence rate compared to WLE (4.76% vs. 10.13% respectively).