The mechano-chemical circuit drives skin organoid self-organization.

Stem cells in organoids self-organize into tissue patterns with unknown mechanisms. Here, we use skin organoids to analyze this process. Cell behavior videos show that the morphological transformation from multiple spheroidal units with morphogenesis competence (CMU) to planar skin is characterized by two abrupt cell motility-increasing events before calming down. The self-organizing processes are controlled by a morphogenetic module composed of molecular sensors, modulators, and executers. Increasing dermal stiffness provides the initial driving force (driver) which activates Yap1 (sensor) in epidermal cysts. Notch signaling (modulator 1) in epidermal cyst tunes the threshold of Yap1 activation. Activated Yap1 induces Wnts and MMPs (epidermal executers) in basal cells to facilitate cellular flows, allowing epidermal cells to protrude out from the CMU. Dermal cell-expressed Rock (dermal executer) generates a stiff force bridge between two CMU and accelerates tissue mixing via activating Laminin and ß1-integrin. Thus, this self-organizing coalescence process is controlled by a mechano-chemical circuit. Beyond skin, self-organization in organoids may use similar mechano-chemical circuit structures.

Protective role of TRPV2 in synaptic plasticity through the ERK1/2-CREB-BDNF pathway in chronic unpredictable mild stress rats.

PURPOSE: Chronic stress is a significant risk factor for mood disorders such as depression, where synaptic plasticity plays a central role in pathogenesis. Transient Receptor Potential Vanilloid Type-2 (TRPV2) Ion Channels are implicated in hypothalamic-pituitary-adrenal axis disorders. Previous proteomic analysis indicated a reduction in TRPV2 levels in the chronic unpredictable mild stress (CUMS) rat model, yet its role in synaptic plasticity during depression remains to be elucidated. This study aims to investigate TRPV2's role in depression and its underlying mechanisms. METHODS: In vivo and in vitro experiments were conducted using the TRPV2-specific agonist probenecid and ERK1/2 inhibitors SCH772984. In vivo, rats underwent six weeks of CUMS before probenecid administration. Depressive-like behaviors were assessed through behavioral tests. ELISA kits measured 5-HT, DA, NE levels in rat hippocampal tissues. Hippocampal morphology was examined via Nissl staining. In vitro, rat hippocampal neuron cell lines were treated with ERK1/2 inhibitors SCH772984 and probenecid. Western blot, immunofluorescence, immunohistochemical staining, and RT-qPCR assessed TRPV2 expression, neurogenesis-related proteins, synaptic markers, and ERK1/2-CREB-BDNF signaling proteins. RESULTS: Decreased hippocampal TRPV2 levels were observed in CUMS rats. Probenecid treatment mitigated depressive-like behavior and enhanced hippocampal 5-HT, NE, and DA levels in CUMS rats. TRPV2 activation countered CUMS-induced synaptic plasticity inhibition. Probenecid activated the ERK1/2-CREB-BDNF pathway, suggesting TRPV2's involvement in this pathway via ERK1/2. CONCLUSION: These findings indicate that TRPV2 activation offers protective effects against depressive-like behaviors and enhances hippocampal synaptic plasticity in CUMS rats via the ERK1/2-CREB-BDNF pathway. TRPV2 emerges as a potential therapeutic target for depression.

Recognition memory deficits detected through eye-tracking in well-controlled children with self-limited epilepsy with centrotemporal spikes.

OBJECTIVE: Children with self-limited epilepsy characterized by centrotemporal spikes (SeLECTS) exhibit cognitive deficits in memory during the active phase, but there is currently a lack of studies and techniques to assess their memory development after well-controlled seizures. In this study, we employed eye-tracking techniques to investigate visual memory and its association with clinical factors and global intellectual ability, aiming to identify potential risk factors by examining encoding and recognition processes. METHODS: A total of 26 recruited patients diagnosed with SeLECTS who had been seizure-free for at least 2 years, along with 24 control subjects, underwent Wechsler cognitive assessment and an eye-movement-based memory task while video-electroencephalographic (EEG) data were recorded. Fixation and pupil data related to eye movements were utilized to detect distinct memory processes and subsequently to compare the cognitive performance of patients exhibiting different regression patterns on EEG. RESULTS: The findings revealed persistent impairments in visual memory among children with SeLECTS after being well controlled, primarily observed in the recognition stage rather than the encoding phase. Furthermore, the age at onset, frequency of seizures, and interictal epileptiform discharges exhibited significant correlations with eye movement data. SIGNIFICANCE: Children with SeLECTS exhibit persistent recognition memory impairment after being well controlled for the disease. Controlling the frequency of seizures and reducing prolonged epileptiform activity may improve memory cognitive development. The application of the eye-tracking technique may provide novel insights into exploring memory cognition as well as underlying mechanisms associated with pediatric epilepsy.

A fast and non-invasive artificial intelligence olfactory-like system that aids diagnosis of Parkinson's disease.

BACKGROUND AND PURPOSE: Several previous studies have shown that skin sebum analysis can be used to diagnose Parkinson's disease (PD). The aim of this study was to develop a portable artificial intelligence olfactory-like (AIO) system based on gas chromatographic analysis of the volatile organic compounds (VOCs) in patient sebum and explore its application value in the diagnosis of PD. METHODS: The skin VOCs from 121 PD patients and 129 healthy controls were analyzed using the AIO system and three classic machine learning models were established, including the gradient boosting decision tree (GBDT), random forest and extreme gradient boosting, to assist the diagnosis of PD and predict its severity. RESULTS: A 20-s time series of AIO system data were collected from each participant. The VOC peaks at a large number of time points roughly concentrated around 5-12 s were significantly higher in PD subjects. The gradient boosting decision tree model showed the best ability to differentiate PD from healthy controls, yielding a sensitivity of 83.33% and a specificity of 84.00%. However, the system failed to predict PD progression scored by Hoehn-Yahr stage. CONCLUSIONS: This study provides a fast, low-cost and non-invasive method to distinguish PD patients from healthy controls. Furthermore, our study also indicates abnormal sebaceous gland secretion in PD patients, providing new evidence for exploring the pathogenesis of PD.

Intranasal delivery of phenytoin loaded layered double hydroxide nanoparticles improves therapeutic effect on epileptic seizures.

Improving the efficiency of antiseizure medication entering the brain is the key to reducing its peripheral toxicity. A combination of intranasal administration and nanomedicine presents a practical approach for treating epileptic seizures via bypassing the blood-brain barrier. In this study, phenytoin (PHT) loaded layered double hydroxide nanoparticles (BSA-LDHs-PHT) were fabricated via a coprecipitation - hydrothermal method for epileptic seizure control. In this study, we expound on the preparation method and characterization of BSA-LDHs-PHT. In-vitro drug release experiment shows both rapid and continuous drug release from BSA-LDHs-PHT, which is crucial for acute seizure control and chronic epilepsy therapy. In-vivo biodistribution assays after intranasal administration indicate excellent brain targeting ability of BSA-LDHs. Compared to BSA-Cyanine5.5, BSA-LDHs-Cyanine5.5 were associated with a higher brain/peripheral ratio across all tested time points. Following intranasal delivery with small doses of BSA-LDHs-PHT, the latency of seizures in the pentylenetetrazole-induced mouse models was effectively improved. Collectively, the present study successfully designed and applied BSA-LDHs-PHT as a promising strategy for treating epileptic seizures with an enhanced therapeutic effect.

A qualitative exploration of 40 Hz sound and music for older adults with mild cognitive impairment.

Emerging evidence suggests that 40 Hz auditory stimulation may benefit cognition. Nested within a randomized crossover trial, this qualitative study evaluates the acceptability and experience of three auditory interventions-self-selected music, 40 Hz sound, and a novel combination, termed 40 Hz music-in individuals with Mild Cognitive Impairment (MCI). Semi-structured interviews were conducted with individuals with MCI post-intervention exposure. Findings indicated a preference for self-selected music due to its memory-boosting and emotional benefits, while responses to 40 Hz sound were mixed, with several participants reporting discomfort. The composite 40 Hz music intervention showed promise, striking a balance by enhancing user experience and mitigating the 40 Hz sound's negative aspects. Engagement was influenced by personal music interests, listening routines, and support networks. This study highlights the potential of integrating 40 Hz sound with personalized music to offer a more acceptable 40 Hz auditory intervention for cognition in older adults with MCI.

Risk factors for amputation in diabetic foot ulcers: A retrospective analysis.

Diabetic foot ulcers (DFUs) are chronic, difficult-to-heal wounds with a very high incidence of amputation. For patients with DFUs, prevention of amputation is crucial. However, the risk factors associated with DFU amputation and the extent to which different risk factors increase the risk of amputation are still uncertain. This study intends to provide a clinical basis for early intervention in DFU by retrospectively analysing the risk factors for DFU amputation. A retrospective analysis of 200 patients with DFUs admitted between October 2019 and October 2023 was conducted. Sixty-eight of the 200 underwent amputations. The overall amputation rate was 34%. Multiple logistic regression model showed that neutrophil/lymphocyte ratio (OR = 1.943; 95% CI:1.826-2.139), white blood cell (OR = 1.143; 95% CI:1.034-1.267), C-reactive protein (OR = 1.307; 95% CI:1.113-2.194) and Wagner grading (OR = 2.783; 95% CI: 1.751-4.302) were independent risk factors for amputation, while haemoglobin (OR = 0.742; 95% CI:0.638-0.965) and high density lipoprotein were independent protective factors for amputation (OR = 0.168; 95% CI:0.037-0.716), and further Receiver Operating Characteristic Curve curves showed that they showed high accuracy and were good predictors of amputation of DFUs.

Bayesian biclustering for microbial metagenomic sequencing data via multinomial matrix factorization.

High-throughput sequencing technology provides unprecedented opportunities to quantitatively explore human gut microbiome and its relation to diseases. Microbiome data are compositional, sparse, noisy, and heterogeneous, which pose serious challenges for statistical modeling. We propose an identifiable Bayesian multinomial matrix factorization model to infer overlapping clusters on both microbes and hosts. The proposed method represents the observed over-dispersed zero-inflated count matrix as Dirichlet-multinomial mixtures on which latent cluster structures are built hierarchically. Under the Bayesian framework, the number of clusters is automatically determined and available information from a taxonomic rank tree of microbes is naturally incorporated, which greatly improves the interpretability of our findings. We demonstrate the utility of the proposed approach by comparing to alternative methods in simulations. An application to a human gut microbiome data set involving patients with inflammatory bowel disease reveals interesting clusters, which contain bacteria families Bacteroidaceae, Bifidobacteriaceae, Enterobacteriaceae, Fusobacteriaceae, Lachnospiraceae, Ruminococcaceae, Pasteurellaceae, and Porphyromonadaceae that are known to be related to the inflammatory bowel disease and its subtypes according to biological literature. Our findings can help generate potential hypotheses for future investigation of the heterogeneity of the human gut microbiome.

Spatial molecular profiling: platforms, applications and analysis tools.

Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.

The small RNA PrrH of Pseudomonas aeruginosa regulates hemolysis and oxidative resistance in bloodstream infection.

Small regulatory RNAs (sRNAs) regulate multiple physiological functions in bacteria, and sRNA PrrH can regulate iron homeostasis and virulence. However, the function of PrrH in Pseudomonas aeruginosa (P. aeruginosa) bloodstream infection (BSI) is largely unknown. The aim of this study was to investigate the role of PrrH in P. aeruginosa BSI model. First, P. aeruginosa PAO1 was co-cultured with peripheral blood cells for 6 h. qRT-PCR results showed a transient up-regulation of PrrH expression at 1 h. Simultaneously, the expression of iron uptake genes fpvA, pvdS and phuR were upregulated. In addition, the use of iron chelator 2,2'-dipyridyl to create low-iron conditions caused up-regulation of PrrH expression, a result similar to the BSI model. Furthermore, the addition of FeCl3 was found to decrease PrrH expression. These results support the hypothesis that the expression of PrrH is regulated by iron in BSI model. Then, to clarify the effect of PrrH on major cells in the blood, we used PrrH mutant, overexpressing and wild-type strains to act separately on erythrocytes and neutrophils. On one hand, the hemolysis assay revealed that PrrH contributes to the hemolytic activity of PAO1, and its effect was dependent on the T3SS system master regulator gene exsA, yet had no association with the hemolytic phospholipase C (plcH), pldA, and lasB elastase genes. On the other hand, PrrH mutant enhanced the oxidative resistance of PAO1 in the neutrophils co-culture assay, H2O2-treated growth curve and conventional plate spotting assays. Furthermore, the katA was predicted to be a target gene of PrrH by bioinformatics software, and then verified by qRT-PCR and GFP reporter system. In summary, dynamic changes in the expression of prrH are iron-regulated during PAO1 bloodstream infection. In addition, PrrH promotes the hemolytic activity of P. aeruginosa in an exsA-dependent manner and negatively regulates katA to reduce the oxidative tolerance of P. aeruginosa.

A Gallbladder Cancer Survival Prediction Model Based on Multimodal Fusion Analysis.

BACKGROUND: Gallbladder cancer is the sixth most common malignant gastrointestinal tumor. Radical surgery is currently the only effective treatment, but patient prognosis is poor, with a 5-year survival rate of only 5-10%. Establishing an effective survival prediction model for gallbladder cancer patients is crucial for disease status assessment, early intervention, and individualized treatment approaches. The existing gallbladder cancer survival prediction model uses clinical data-radiotherapy and chemotherapy, pathology, and surgical scope-but fails to utilize laboratory examination and imaging data, limiting its prediction accuracy and preventing sufficient treatment plan guidance. AIMS: The aim of this work is to propose an accurate survival prediction model, based on the deep learning 3D-DenseNet network, integrated with multimodal medical data (enhanced CT imaging, laboratory test results, and data regarding systemic treatments). METHODS: Data were collected from 195 gallbladder cancer patients at two large tertiary hospitals in Shanghai. The 3D-DenseNet network extracted deep imaging features and constructed prognostic factors, from which a multimodal survival prediction model was established, based on the Cox regression model and incorporating patients' laboratory test and systemic treatment data. RESULTS: The model had a C-index of 0.787 in predicting patients' survival rate. Moreover, the area under the curve (AUC) of predicting patients' 1-, 3-, and 5-year survival rates reached 0.827, 0.865, and 0.926, respectively. CONCLUSIONS: Compared with the monomodal model based on deep imaging features and the tumor-node-metastasis (TNM) staging system-widely used in clinical practice-our model's prediction accuracy was greatly improved, aiding the prognostic assessment of gallbladder cancer patients.

The relationship between older adults' technology use, in-person engagement, and pandemic-related mental health.

OBJECTIVE: The objectives of this study are to 1) describe changes in in-person communication/activity and changes in older adult technology use during the COVID-19 pandemic and 2) examine whether less in-person communication/activity mediates the relationship between pandemic-related mental health and technology use. METHOD: Linear regressions (stratified by age and financial strain) and structural equation modeling were employed using a nationally representative, cross-sectional survey of 3,188 older adults from the 2020 National Health and Aging Trends Study's COVID-19 Questionairre. RESULTS: Older adults engaged in more technology-based activity (b = 0.24; p<.001), more technology-based health care communication (b = 0.22; p<.001), and more technology-based food acquisition (b = 0.21; p<.001) during the COVID-19 pandemic, as compared to before the pandemic. Results indicate that adults <80 years old demonstrated greater increases in technology-based activity, technology-based health communication, and technology-based food acquisition, compared to adults ≥80 years old. Change in in-person communication significantly mediated the relationship between pandemic-related mental health and technology-based communication (standardized coefficient= -0.012; p=.005), and change in in-person activity significantly mediated the relationship between pandemic-related mental health and technology-based activity (standardized coefficient= -0.017; p=.020). CONCLUSIONS: This study suggests that older adults are utilizing technology more, and therefore should be considered in technology design and dissemination. Technology use could be an important positive response to help those with pandemic related worries stay safely engaged with friends and family. Technologies should be produced that are modifiable for older adults with disabilities and affordable for older adults with fixed incomes.

Efficacy of Transcranial Direct Current Stimulation Combined with Conventional Swallowing Rehabilitation Training on Post-stroke Dysphagia.

To observe the clinical effects of transcranial direct current stimulation (tDCS) combined with conventional swallowing rehabilitation training on post-stroke dysphagia and explore its long-term efficacy. A total of 40 patients with dysphagia after the first stroke were randomly divided into a treatment group (n = 20) and a conventional group (n = 20). The treatment group received tDCS combined with conventional swallowing rehabilitation training, while the conventional group only received conventional swallowing rehabilitation training. The Standardized Swallowing Assessment (SSA) Scale and the Penetration-Aspiration Scale (PAS) were used to assess dysphagia before and after treatment, at the end of 10 treatments, and at the 3-month follow-up. The changes in infection indicators [the white blood cell (WBC), C-reactive protein (CRP) and procalcitonin (PCT)], the oxygenation indicator [arterial partial pressure of oxygen (PaO2)] and nutrition-related indicators [hemoglobin (Hb) and serum prealbumin (PAB)] were compared before and after treatment. The SSA and PAS scores were lower in both groups after treatment than before treatment, and the difference was statistically significant (P < 0.01). The SSA and PAS scores of the treatment group were lower than those of the conventional group before and after treatment and during follow-up, and the difference was statistically significant (P < 0.05, P < 0.01). A within-group comparison showed that WBC, CRP and PCT after treatment were lower than those before treatment, and the difference was statistically significant (P < 0.05). The PaO2, Hb and serum PAB were higher after treatment than before treatment, with a statistically significant difference (P < 0.05). The WBC, CRP and PCT of the tDCS group were lower than those of the conventional group, and PaO2, Hb and serum PAB were higher in the treatment group than in the conventional group, with a statistically significant difference (P < 0.01). The tDCS combined with conventional swallowing rehabilitation training can improve dysphagia with a better effect than conventional swallowing rehabilitation training and has a certain long-term efficacy. In addition, tDCS combined with conventional swallowing rehabilitation training can improve nutrition and oxygenation and reduce infection levels.

Exposure to negative foreign COVID-19 news predicts lower support for immigration policy in China.

We present a framework for studying the spillover effect of negative foreign COVID-19 news on attitudes towards immigration. Our framework proposes that exposure to negative COVID-19 news from foreign countries can activate negative associations with foreigners, reduce positive attitudes towards them, and increase perceived threat, ultimately leading to decreased support for immigration. We conducted three studies to test this framework. Study 1 found that exposure to negative COVID-19 news about a foreign country increased negative valence associations with that country. Study 2 showed that exposure to more negative COVID-19 news about foreign countries was associated with lower acceptance of immigration policies in real life. Study 3 replicated the spillover effect of negative news exposure using a scenario manipulation. The effects of negative news exposure on immigration policy acceptance in both Studies 2 and 3 were mediated by changes in foreigner attitudes and intergroup threat. Our results demonstrate the important spillover effect of negative foreign COVID-19 news exposure on immigration attitudes and highlight the association perspective as a foundation for understanding attitude changes during the COVID-19 pandemic.

A Bayesian zero-inflated negative binomial regression model for the integrative analysis of microbiome data.

Microbiome omics approaches can reveal intriguing relationships between the human microbiome and certain disease states. Along with identification of specific bacteria taxa associated with diseases, recent scientific advancements provide mounting evidence that metabolism, genetics, and environmental factors can all modulate these microbial effects. However, the current methods for integrating microbiome data and other covariates are severely lacking. Hence, we present an integrative Bayesian zero-inflated negative binomial regression model that can both distinguish differentially abundant taxa with distinct phenotypes and quantify covariate-taxa effects. Our model demonstrates good performance using simulated data. Furthermore, we successfully integrated microbiome taxonomies and metabolomics in two real microbiome datasets to provide biologically interpretable findings. In all, we proposed a novel integrative Bayesian regression model that features bacterial differential abundance analysis and microbiome-covariate effects quantifications, which makes it suitable for general microbiome studies.

Bayesian modeling of spatial molecular profiling data via Gaussian process.

MOTIVATION: The location, timing and abundance of gene expression (both mRNA and proteins) within a tissue define the molecular mechanisms of cell functions. Recent technology breakthroughs in spatial molecular profiling, including imaging-based technologies and sequencing-based technologies, have enabled the comprehensive molecular characterization of single cells while preserving their spatial and morphological contexts. This new bioinformatics scenario calls for effective and robust computational methods to identify genes with spatial patterns. RESULTS: We represent a novel Bayesian hierarchical model to analyze spatial transcriptomics data, with several unique characteristics. It models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binomial model that greatly increases model stability and robustness. Besides, the Bayesian inference framework allows us to borrow strength in parameter estimation in a de novo fashion. As a result, the proposed model shows competitive performances in accuracy and robustness over existing methods in both simulation studies and two real data applications. AVAILABILITY AND IMPLEMENTATION: The related R/C++ source code is available at https://github.com/Minzhe/BOOST-GP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TP53-mediated miR-2861 promotes osteogenic differentiation of BMSCs by targeting Smad7.

Bone defect seriously affects the quality of life. Meanwhile, osteogenic differentiation in BMSCs could regulate the progression of bone defect. Transcription factors are known to regulate the osteogenic differentiation in BMSCs. The study aimed to investigate the detailed mechanism by which TP53 regulates the osteogenic differentiation. To study bone defect in vitro, BMSCs were isolated from spinal cord injury rats. CCK-8 assay was applied to test the cell viability. The mineralized nodules in BMSCs was tested by alizarin red staining. Meanwhile, TUNEL staining and flow cytometry were performed to test the cell apoptosis. mRNA expression was tested by qRT-PCR. Starbase and dual-luciferase reporter assay were used to predict the downstream mRNA of miR-2861. Moreover, western blot was applied to detect the protein expressions (TP53 and Smad7). BMSCs were successfully isolated from rats. The expressions of miR-2861 were significantly upregulated in osteogenic medium, compared with growth medium. MiR-2861 inhibitor significantly decreased the levels of OCN, ALP, BSP, and Runx2 in BMSCs. In addition, miR-2861 inhibitor notably inhibited the mineralized nodules, viability, and induced the apoptosis of BMSCs. Smad7 was identified to be the downstream target of miR-2861, and knockdown of Smad7 notably reversed miR-2861 inhibitor-induced inhibition of osteogenic differentiation and promotion of apoptosis in BMSCs. Moreover, miR-2861 was transcriptionally regulated by TP53 in BMSCs. TP53-meidiated miR-2861 promotes osteogenic differentiation of BMSCs by targeting Smad7. Thereby, our research might provide new methods for bone defect treatment.

A Bayesian modified Ising model for identifying spatially variable genes from spatial transcriptomics data.

A recent technology breakthrough in spatial molecular profiling (SMP) has enabled the comprehensive molecular characterizations of single cells while preserving spatial information. It provides new opportunities to delineate how cells from different origins form tissues with distinctive structures and functions. One immediate question in SMP data analysis is to identify genes whose expressions exhibit spatially correlated patterns, called spatially variable (SV) genes. Most current methods to identify SV genes are built upon the geostatistical model with Gaussian process to capture the spatial patterns. However, the Gaussian process models rely on ad hoc kernels that could limit the models' ability to identify complex spatial patterns. In order to overcome this challenge and capture more types of spatial patterns, we introduce a Bayesian approach to identify SV genes via a modified Ising model. The key idea is to use the energy interaction parameter of the Ising model to characterize spatial expression patterns. We use auxiliary variable Markov chain Monte Carlo algorithms to sample from the posterior distribution with an intractable normalizing constant in the model. Simulation studies using both simulated and synthetic data showed that the energy-based modeling approach led to higher accuracy in detecting SV genes than those kernel-based methods. When applied to two real spatial transcriptomics (ST) datasets, the proposed method discovered novel spatial patterns that shed light on the biological mechanisms. In summary, the proposed method presents a new perspective for analyzing ST data.

Manganese(III)-promoted highly stereoselective phosphorylation of acyclic tertiary enamides to synthesize E-selective ß-phosphoryl enamides.

A concise manganese(III)-promoted stereoselective ß-phosphorylation of acyclic tertiary enamides and diverse H-phosphine oxides was achieved. This reaction proceeds with absolute E-selectivity in contrast to Z-selectivity obtained in other previous works and affords various E-selective ß-phosphorylated tertiary enamides in high efficiency. To the best of our knowledge, this is the first case of E-selective ß-phosphorylation of tertiary enamides through C-H functionalization. In addition, the method features broad substrate scope, good functional group compatibility and efficient scale-up.

SAFARI: shape analysis for AI-segmented images.

BACKGROUND: Recent developments to segment and characterize the regions of interest (ROI) within medical images have led to promising shape analysis studies. However, the procedures to analyze the ROI are arbitrary and vary by study. A tool to translate the ROI to analyzable shape representations and features is greatly needed. RESULTS: We developed SAFARI (shape analysis for AI-segmented images), an open-source R package with a user-friendly online tool kit for ROI labelling and shape feature extraction of segmented maps, provided by AI-algorithms or manual segmentation. We demonstrated that half of the shape features extracted by SAFARI were significantly associated with survival outcomes in a case study on 143 consecutive patients with stage I-IV lung cancer and another case study on 61 glioblastoma patients. CONCLUSIONS: SAFARI is an efficient and easy-to-use toolkit for segmenting and analyzing ROI in medical images. It can be downloaded from the comprehensive R archive network (CRAN) and accessed at https://lce.biohpc.swmed.edu/safari/ .