Insight into the role of a novel c-di-GMP effector protein in Rhodococcus ruber.

C-di-GMP is a ubiquitous second messenger in bacterium, which regulates cellular functions such as the formation of biofilm membrane, cell mobility, virulence, cell adhesion, cell cycle et al. These functions are associated with an increasing number of c-di-GMP effector proteins and/or riboswitchs. In the study, CEP1 (c-di-GMP effector protein 1), a novel c-di-GMP binding protein, was screened with a combination of affinity pull-down and LC/MS/MS methods. The binding of CEP1 and c-di-GMP was demonstrated by surface plasmon resonance, with the dissociation constants of 127 ± 1.03 µM. Quantitative real time PCR assay showed the mRNA levels of cep1 gene in Rhodococcus ruber SD3 increased to 63.29 times and 71.18 times after toluene and phenol stress, respectively. Furthermore, cep1 gene enhanced strain was constructed using shuttle plasmid pNV18, which showed improved growth compared to the wild-type strain in the presence of different organic solvents. The study provided an insight into a mechanism, by which c-di-GMP was connected with organic solvent tolerance of Rhodococcus ruber SD3.

Is it time to switch your T1W sequence? Assessing the impact of prospective motion correction on the reliability and quality of structural imaging.

New large neuroimaging studies, such as the Adolescent Brain Cognitive Development study (ABCD) and Human Connectome Project (HCP) Development studies are adopting a new T1-weighted imaging sequence with prospective motion correction (PMC) in favor of the more traditional 3-Dimensional Magnetization-Prepared Rapid Gradient-Echo Imaging (MPRAGE) sequence. Here, we used a developmental dataset (ages 5-21, N = 348) from the Healthy Brain Network (HBN) Initiative to directly compare two widely used MRI structural sequences: one based on the Human Connectome Project (MPRAGE) and another based on the ABCD study (MPRAGE+PMC). We aimed to determine if the morphometric measurements obtained from both protocols are equivalent or if one sequence has a clear advantage over the other. The sequences were also compared through quality control measurements. Inter- and intra-sequence reliability were assessed with another set of participants (N = 71) from HBN that performed two MPRAGE and two MPRAGE+PMC sequences within the same imaging session, with one MPRAGE (MPRAGE1) and MPRAGE+PMC (MPRAGE+PMC1) pair at the beginning of the session and another pair (MPRAGE2 and MPRAGE+PMC2) at the end of the session. Intraclass correlation coefficients (ICC) scores for morphometric measurements such as volume and cortical thickness showed that intra-sequence reliability is the highest with the two MPRAGE+PMC sequences and lowest with the two MPRAGE sequences. Regarding inter-sequence reliability, ICC scores were higher for the MPRAGE1 - MPRAGE+PMC1 pair at the beginning of the session than the MPRAGE1 - MPRAGE2 pair, possibly due to the higher motion artifacts in the MPRAGE2 run. Results also indicated that the MPRAGE+PMC sequence is robust, but not impervious, to high head motion. For quality control metrics, the traditional MPRAGE yielded better results than MPRAGE+PMC in 5 of the 8 measurements. In conclusion, morphometric measurements evaluated here showed high inter-sequence reliability between the MPRAGE and MPRAGE+PMC sequences, especially in images with low head motion. We suggest that studies targeting hyperkinetic populations use the MPRAGE+PMC sequence, given its robustness to head motion and higher reliability scores. However, neuroimaging researchers studying non-hyperkinetic participants can choose either MPRAGE or MPRAGE+PMC sequences, but should carefully consider the apparent tradeoff between relatively increased reliability, but reduced quality control metrics when using the MPRAGE+PMC sequence.

Measurement reliability for individual differences in multilayer network dynamics: Cautions and considerations.

Multilayer network models have been proposed as an effective means of capturing the dynamic configuration of distributed neural circuits and quantitatively describing how communities vary over time. Beyond general insights into brain function, a growing number of studies have begun to employ these methods for the study of individual differences. However, test-retest reliabilities for multilayer network measures have yet to be fully quantified or optimized, potentially limiting their utility for individual difference studies. Here, we systematically evaluated the impact of multilayer community detection algorithms, selection of network parameters, scan duration, and task condition on test-retest reliabilities of multilayer network measures (i.e., flexibility, integration, and recruitment). A key finding was that the default method used for community detection by the popular generalized Louvain algorithm can generate erroneous results. Although available, an updated algorithm addressing this issue is yet to be broadly adopted in the neuroimaging literature. Beyond the algorithm, the present work identified parameter selection as a key determinant of test-retest reliability; however, optimization of these parameters and expected reliabilities appeared to be dataset-specific. Once parameters were optimized, consistent with findings from the static functional connectivity literature, scan duration was a much stronger determinant of reliability than scan condition. When the parameters were optimized and scan duration was sufficient, both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks were reliable, although reliability in the movie watching condition was significantly higher than in the other three tasks. The minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, and 30 min for the other three tasks. Our results caution the field against the use of default parameters without optimization based on the specific datasets to be employed - a process likely to be limited for most due to the lack of test-retest samples to enable parameter optimization.

U-net model for brain extraction: Trained on humans for transfer to non-human primates.

Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.

Evaluating fMRI-Based Estimation of Eye Gaze During Naturalistic Viewing.

The collection of eye gaze information during functional magnetic resonance imaging (fMRI) is important for monitoring variations in attention and task compliance, particularly for naturalistic viewing paradigms (e.g., movies). However, the complexity and setup requirements of current in-scanner eye tracking solutions can preclude many researchers from accessing such information. Predictive eye estimation regression (PEER) is a previously developed support vector regression-based method for retrospectively estimating eye gaze from the fMRI signal in the eye's orbit using a 1.5-min calibration scan. Here, we provide confirmatory validation of the PEER method's ability to infer eye gaze on a TR-by-TR basis during movie viewing, using simultaneously acquired eye tracking data in five individuals (median angular deviation < 2°). Then, we examine variations in the predictive validity of PEER models across individuals in a subset of data (n = 448) from the Child Mind Institute Healthy Brain Network Biobank, identifying head motion as a primary determinant. Finally, we accurately classify which of the two movies is being watched based on the predicted eye gaze patterns (area under the curve = 0.90 ± 0.02) and map the neural correlates of eye movements derived from PEER. PEER is a freely available and easy-to-use tool for determining eye fixations during naturalistic viewing.

Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI.

A variety of psychiatric, behavioral and cognitive phenotypes have been linked to brain ''functional connectivity'' -- the pattern of correlation observed between different brain regions. Most commonly assessed using functional magnetic resonance imaging (fMRI), here, we investigate the connectivity-phenotype associations with functional connectivity measured with electroencephalography (EEG), using phase-coupling. We analyzed data from the publicly available Healthy Brain Network Biobank. This database compiles a growing sample of children and adolescents, currently encompassing 1657 individuals. Among a variety of assessment instruments we focus on ten phenotypic and additional demographic measures that capture most of the variance in this sample. The largest effect sizes are found for age and sex for both fMRI and EEG. We replicate previous findings of an association of Intelligence Quotient (IQ) and Attention Deficit Hyperactivity Disorder (ADHD) with the pattern of fMRI functional connectivity. We also find an association with socioeconomic status, anxiety and the Child Behavior Checklist Score. For EEG we find a significant connectivity-phenotype relationship with IQ. The actual spatial patterns of functional connectivity are quite different between fMRI and source-space EEG. However, within EEG we observe clusters of functional connectivity that are consistent across frequency bands. Additionally we analyzed reproducibility of functional connectivity. We compare connectivity obtained with different tasks, including resting state, a video and a visual flicker task. For both EEG and fMRI the variation between tasks was smaller than the variability observed between subjects. We also found an increase of reliability with increasing frequency of the EEG, and increased sampling duration. We conclude that, while the patterns of functional connectivity are distinct between fMRI and phase-coupling of EEG, they are nonetheless similar in their robustness to the task, and similar in that idiosyncratic patterns of connectivity predict individual phenotypes.

The phase of prestimulus alpha oscillations affects tactile perception.

Previous studies have shown that neural oscillations in the 8- to 12-Hz range influence sensory perception. In the current study, we examined whether both the power and phase of these mu/alpha oscillations predict successful conscious tactile perception. Near-threshold tactile stimuli were applied to the left hand while electroencephalographic (EEG) activity was recorded over the contralateral right somatosensory cortex. We found a significant inverted U-shaped relationship between prestimulus mu/alpha power and detection rate, suggesting that there is an intermediate level of alpha power that is optimal for tactile perception. We also found a significant difference in phase angle concentration at stimulus onset that predicted whether the upcoming tactile stimulus was perceived or missed. As has been shown in the visual system, these findings suggest that these mu/alpha oscillations measured over somatosensory areas exert a strong inhibitory control on tactile perception and that pulsed inhibition by these oscillations shapes the state of brain activity necessary for conscious perception. They further suggest that these common phasic processing mechanisms across different sensory modalities and brain regions may reflect a common underlying encoding principle in perceptual processing that leads to momentary windows of perceptual awareness.

Downhill running and caloric restriction attenuate insulin resistance associated skeletal muscle atrophy via the promotion of M2-like macrophages through TRIB3-AKT pathway.

BACKGROUD: Downhill running has recently become a promising exercise modality for metabolic syndrome, but the effect and precise mechanism of downhill running training on insulin resistance (IR) induced skeletal muscle atrophy remains unclear. The current study aimed to explore the benefits of downhill running training accompanied by a low-fat diet on skeletal muscle atrophy in IR mice and its possible mechanisms. METHODS: For in vivo study, high fat diet (HFD) -induced IR mice were submitted to the downhill running training or/and caloric restriction for 8 weeks. In vitro study was performed using co-cultured RAW264.7 macrophages and C2C12 myoblasts model. Glucose tolerance test (GTT), insulin tolerance test (ITT), immunofluorescence staining, Western blot analysis, hematoxylin and eosin (H&E) staining, enzyme-linked immunosorbent assay (ELISA), Cell counting kit-8 (CCK-8) assays and glucose uptake assays were employed to explore the benefits and possible mechanisms of downhill running training accompanied by a low-fat diet on IR mice. RESULTS: Our data revealed that HFD induces IR, which leading to skeletal muscle atrophy. Downhill running accompanied by caloric restriction mitigated HFD-induced IR and improve skeletal muscle atrophy. Further study suggested that descended TRIB3 mediated the favorable impact of downhill running on IR induced skeletal muscle atrophy by suppressing M1-like macrophages and promoting M2-like macrophages. Macrophages-specific knockdown of TRIB3 exerted similar effects on the macrophage polarization and IR related myogenesis to downhill running training accompanied by caloric restriction. In contrast, macrophages-specific overexpression of TRIB3 descended phosphorylation of AKT, further activated M1-like macrophages and aggravated IR related inhibition of myogenesis. CONCLUSIONS: This finding demonstrated the beneficial effects of downhill running training and caloric restriction on IR related skeletal muscle atrophy by promoting M2-like macrophages through TRIB3-AKT pathway.

Predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma using deep learning.

Background: The aim of this study is to design a deep learning (DL) model to preoperatively predict the occurrence of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC). Methods: This research collected preoperative ultrasound (US) images and clinical factors of 611 PTMC patients. The clinical factors were analyzed using multivariate regression. Then, a DL model based on US images and clinical factors was developed to preoperatively predict CLNM. The model's efficacy was evaluated using the receiver operating characteristic (ROC) curve, along with accuracy, sensitivity, specificity, and the F1 score. Results: The multivariate analysis indicated an independent correlation factors including age ≥55 (OR = 0.309, p < 0.001), tumor diameter (OR = 2.551, p = 0.010), macrocalcifications (OR = 1.832, p = 0.002), and capsular invasion (OR = 1.977, p = 0.005). The suggested DL model utilized US images achieved an average area under the curve (AUC) of 0.65, slightly outperforming the model that employed traditional clinical factors (AUC = 0.64). Nevertheless, the model that incorporated both of them did not enhance prediction accuracy (AUC = 0.63). Conclusions: The suggested approach offers a reference for the treatment and supervision of PTMC. Among three models used in this study, the deep model relied generally more on image modalities than the data modality of clinic records when making the predictions.

Real-world use of first-line pembrolizumab + platinum + taxane combination regimens in recurrent / metastatic head and neck squamous cell carcinoma.

Introduction: The programmed death-1 (PD-1) immune checkpoint inhibitor pembrolizumab is currently approved in the US for the first-line (1L) treatment of recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC), either alone or in combination with platinum and 5-fluorouracil (5-FU). However, the toxicity of 5-FU has motivated the study of alternate combinations that replace 5-FU with a taxane. The objective of the current study was to describe the baseline characteristics, treatment patterns and sequences, and real-world outcomes of individuals receiving pembrolizumab + platinum + taxane as 1L treatment for R/M HNSCC in the US. Methods: This was a retrospective study of US adults ≥18 years of age receiving pembrolizumab + platinum + taxane as 1L treatment for R/M HNSCC, using electronic health record data from a nationwide de-identified database. Real-world overall survival (rwOS), time on treatment (rwToT), and time to next treatment (rwTTNT) outcomes were assessed using Kaplan-Meier analysis. Results: The study population comprised 83 individuals (80.7% male) with a median age of 64 years. The most common tumor site was the oropharynx (48.2%); 70.0% of these tumors were HPV-positive. A total of 71.1% of the study population had an Eastern Cooperative Oncology Group performance status of 0-1 at index date, 71.8% had a combined positive score for programmed death ligand-1 (PD-L1) expression of ≥1, and 30.8% had a score of ≥20. The median (95% CI) rwOS was 14.9 (8.8-23.3) months, rwToT was 5.3 (4.0-8.2) months, and rwTTNT was 8.7 (6.8-12.3) months. Among the 24 individuals who received a subsequent therapy, the most common second-line therapies were cetuximab-based (n = 9) or pembrolizumab-containing (n = 8) regimens. Conclusions: The rwOS and other real-world outcomes observed for this study population further support pembrolizumab + platinum + taxane combination therapy as a potential 1L treatment option for R/M HNSCC.

Effects of Mesenchymal Stem Cells-Derived Extracellular Vesicles on Inhibition of Hepatic Fibrosis by Delivering miR-200a.

BACKGROUND: Hepatic fibrosis (HF) is a common pathological feature of chronic hepatic diseases. We aimed to illuminate the significance of amniotic mesenchymal stem cells (AMSCs)-derived extracellular vesicles (AMSCs-EVs) in HF. METHODS: Human AMSCs-EVs were isolated and identified. HF mice were constructed and treated with EVs. The fibrosis was observed by staining experiments and Western blot (WB) assay. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), and hepatic hydroxyproline (Hyp) were detected to confirm liver function. For the in vitro experiments, human hepatic stellate cells were induced with transforming growth factor-ß and treated with EVs. To measure the degree of HF, the expression of alpha-smooth muscle actin (α-SMA) and Collagen I was detected by WB assay, and cell proliferation was detected by cell counting kit 8 assay. The levels of miR-200a, Zinc finger E-box binding homeobox 1 (ZEB1), and phosphoinositide-3-kinase regulatory subunit 3 (PIK3R3) were detected by WB and real-time quantitative polymerase chain reaction. The binding of ZEB1 to PIK3R3 and miR-200a to ZEB1 was analyzed by chromatin immunoprecipitation and dual luciferase assays to validate their relationships. RESULTS: Human AMSCs and AMSCs-EVs were obtained. Serum ALT, AST, TBIL, and hepatic Hyp were increased, implying the fibrosis degree was aggravated in HF mice, which was decreased again after EV treatment. EVs inhibited HF degree by reducing α-SMA and Collagen I and promoting cell proliferation. AMSCs-EVs delivered miR-200a into hepatocytes, which up-regulated miR-200a expression, inhibited ZEB1 expression, and reduced its enrichment on the PIK3R3 promoter, therefore inhibiting PIK3R3 expression and alleviating HF. Overexpression of ZEB1 or PIK3R3 attenuated the anti-fibrotic effect of AMSCs-EVs. CONCLUSION: Human AMSCs-derived EVs mediated miR-200a delivery and inhibition of intracellular ZEB1/PIK3R3 axis to exert anti-fibrosis effects.

An Overview of the Role of Membrane Proteins in Microbial Solvents Tolerance.

BACKGROUND: Solvent tolerance is a desired feature of microorganisms for their application in biotechnology. Organic solvent-tolerant microorganisms are able to thrive in the presence of organic solvents. Several mechanisms have been proposed to elucidate their intrinsic tolerance to organic solvents. OBJECTIVE: The present review aims to summarize the state of the art of the roles of membrane proteins in microbial organic solvent tolerance. Strategies and challenges for improving the protective function of membrane proteins in organic solvent stress are also proposed. RESULTS: Membrane proteins related to transporter, signal transduction, and material and energy metabolism are involved in solvent tolerance. Optimization of the expression level of membrane proteins and engineering of membrane proteins are utilized to tackle the toxicity caused by organic solvents. CONCLUSIONS: Membrane proteins occupy a strikingly important position in microbial solvent tolerance. Further research on novel methods in membrane proteins, trade-offs among overexpression and toxicity of membrane proteins and solvent yield, and a direct relationship between signaling pathways and solvent tolerance will advance the utilization of organic solvent-tolerant microorganisms in biotechnology.

Investigation of Immunogenicity Assessment of Biosimilar Monoclonal Antibodies in the United States.

Immunogenicity is critical for biologics. However, reference biologics labeling documents do not necessarily mention immunogenicity impact, rendering the development of biosimilars more challenging. We aimed to investigate the comparative assessment of immunogenicity profiles between biosimilars and their respective reference biologics in the review reports of the biosimilar monoclonal antibody applications approved by the Center for Drug Evaluation and Research (CDER), US Food and Drug Administration (FDA) as of March 13, 2022, covering 22 applications approved between April 5, 2016, and December 17, 2021. The maximum differences in anti-drug antibody (ADA) and neutralizing antibody (NAb) incidences between biosimilars and reference products mostly fell within ± 15% (-13.6% to 12%) and ± 20% (-17.4% to 17.1%, except extreme values of -23.4% and 66.7%), respectively. In comparison with antineoplastic agents, more immunosuppressants had ADA-positive (11/11, 100.0% vs. 8/10, 80.0%)/NAb-positive (11/11, 100.0% vs. 3/10, 30.0%) subjects, and the distribution of the aforementioned incidence differences was wider. The investigated biosimilars with available data for analysis demonstrated a high degree of consistency with their reference products in terms of the impact on pharmacokinetic parameters. No increase in immunogenicity was found in available switching studies. Most (16/22, 72.7%) biosimilars were issued post-marketing requirements that were not directly related to immunogenicity concerns. The FDA considered the totality of evidence assessing clinical consequences of immunogenicity differences, if any. Additional information on titers and subgroup analysis may be warranted to elucidate the critical attributes of immunogenicity impact and to aid in forming cost-effective strategies for biosimilar development.

Silencing METTL14 alleviates liver injury in non-alcoholic fatty liver disease by regulating mitochondrial homeostasis.

Mitochondrial dysfunction is an important pathogenic factor in non-alcoholic fatty liver disease (NAFLD). Methyltransferase-like 14 (METTL14) has been implicated in mitochondrial fission processes. This research aimed to investigate the mechanism of METTL14 in the mitochondrial function of NAFLD. We first established NAFLD mouse models and cell models, recording body and liver weights and examining pathological changes in liver tissues. Subsequently, serum levels of liver function indices (aspartate aminotransferase [AST], alanine aminotransferase [ALT], total cholesterol [TC], and triglycerides [TG]), inflammatory markers (tumor necrosis factor-alpha [TNF-α], interleukin [IL]-6, and IL-1ß), and mitochondrial dysfunction indicators (fission 1 protein [Fis1], dynamin-related protein 1 [Drp1], mitofusin 2 [Mfn2], SID1 transmembrane family member 2 [SIDT2], and mitochondrial membrane potential [MMP]) in the liver and cells were evaluated. The N6-methyladenosine (m6A) modification level of primary microRNA (pri-miRNA) and m6A enrichment on pri-miR-34a were quantified. Co-immunoprecipitation and dual-luciferase reporter gene assays were utilized to validate gene interactions. Our findings revealed highly elevated METTL14 expression in NAFLD mouse and cell models. Silencing METTL14 reduced weight gain and mitigated adverse liver function indices, inflammation, hepatic steatosis, and structural damage in NAFLD mice. It also led to a decrease in Fis1/Drp1 levels and an increase in MMP/Mfn2 in the liver and cells. Moreover, METTL14 increased the m6A level, promoting the binding of DiGeorge syndrome critical region 8 (DGCR8) to pri-miR-34a, which enhanced miR-34a-5p expression. Databases and dual-luciferase reporter gene assays indicated that miR-34a-5p could suppress SIDT2 expression. The overexpression of miR-34a-5p or inhibition of SIDT2 expression negated the alleviative effects of METTL14 silencing on mitochondrial homeostasis imbalance. In conclusion, METTL14, through m6A modification, modulates the miR-34a-5p/SIDT2 axis, impairing mitochondrial homeostasis in NAFLD.

Topological phase transitions and flat bands on an islamic lattice.

We construct an islamic lattice by considering the nearest-neighbor (NN) hoppings with staggered magnetic fluxes and the next-NN hoppings. This model supports abundant quantum phases for various values of filling fractions. At1/4filling, Chern insulator (CI) phases with Chern numbersC=±1, -2and a zero-Chern-number topological insulator (ZCNTI) phase exist. At3/8filling, several CI phases with Chern numbersC=±1, 3and the ZCNTI phase are obtained. For the filling fraction 3/4, CI phases with Chern numbersC=±1, 2and two ZCNTI phase areas appear. Interestingly, these ZCNTI phases host both robust corner states and gapless edge states which can be characterized by the quantized polarization and quadrupole moment. We further find that staggered magnetic fluxes can give rise to the ZCNTI state at1/4and3/4fillings. Phase diagrams for filling fractions1/8,1/2,5/8and7/8are presented as well. In addition, flat bands are obtained for various filling fractions by tuning the hopping parameters. At 1/8 filling, a best topological flat band (TFB) with flatness ratio about 12 appears. Several trivial flat bands but with total Chern number|C|=1emerge in this model and exactly flat band is found at 3/8 filling. We further investigateν=1/2fractional Chern insulate state when hard-core bosons fill into this TFB model.

Age-based factors modulating the required thyroxine dose to achieve thyrotropin suppression in intermediate-and high-risk papillary thyroid cancer.

Background: Guidelines widely recommend thyrotropin suppression to reduce the risk of recurrence in intermediate- and high-risk papillary thyroid cancer (PTC) after total thyroidectomy. However, an insufficient or excessive dosage may result in a number of symptoms/complications especially in older patients. Patients and methods: We constructed a retrospective cohort including 551 PTC patient encounters. Using propensity score matching and logistic regression models, we determined the independent risk factors affecting levothyroxine therapy at different ages. Our outcomes included: expected TSH level and an unexpected TSH level, which was based on the initial thyroid-stimulating hormone (TSH) goal< 0.1 mIU/L with usual dosage of L-T4 (1.6 µg/kg/day). Results: From our analysis, more than 70% of patients undergoing total thyroidectomy did not achieve the expected TSH level using an empirical medication regimen, and the effect of the drug was affected by age (odds ratio [OR], 1.063; 95% CI, 1.032-1.094), preoperative TSH level (OR, 0.554; 95% CI, 0.436-0.704) and preoperative fT3 level (OR, 0.820; 95% CI, 0.727-0.925). In patients with age < 55 years old, preoperative TSH level (OR, 0.588; 95% CI, 0.459-0.753), and preoperative fT3 level (OR, 0.859; 95% CI, 0.746-0.990) were two independent protective factors, while, in patients with age ≥ 55 years old, only preoperative TSH level (OR, 0.490; 95% CI, 0.278-0.861) was the independent protective factors to achieve expected TSH level. Conclusion: Our retrospective analysis suggested the following significant risk factors of getting TSH suppression in PTC patients: age (≥55 years), lower preoperative TSH and fT3 levels.

Analysis of real-world data to investigate evolving treatment sequencing patterns in advanced non-small cell lung cancers and their impact on survival.

Background: Although optimal sequencing of systemic therapy in cancer care is critical to achieving maximal clinical benefit, there is a lack of analysis of treatment sequencing in advanced non-small cell lung cancer (aNSCLC) in real-world settings. Methods: A retrospective cohort study of 13,340 lung cancer patients within the Mount Sinai Health System (MSHS) was performed. Systemic therapy data of aNSCLC in 2,106 patients was the starting point in our analysis to investigate how treatment sequencing has evolved, the impact of sequencing patterns on clinical outcomes, and the effectiveness of 2nd line chemotherapy after patients progressed on immune checkpoint inhibitor (ICI)-based therapy as the 1st line of therapy (LOT). Results: There is a significant shift to more ICI-based therapy and multiple lines of targeted therapy after 2015. We compared clinical outcomes of two patient populations with different treatment sequencing patterns, with the 1st group receiving chemotherapy as the 1st LOT followed by ICI-based treatment, and the 2nd group treated in the opposite order receiving a 1st line ICI-containing regimen followed by a 2nd line chemotherapy. No statistically significant difference in overall survival (OS) was observed between the two groups [group 2 vs. group 1, adjusted hazard ratio (aHR) =1.36, P=0.39]. We assessed the efficacy of the 2nd line chemotherapy in three patient populations given either 1st line ICI single agent, 1st line ICI-chemotherapy combination, or 1st line chemotherapy alone, there was no statistically significant difference in time-to-next treatment (TTNT) and in OS among the three patient groups. Conclusions: Analysis of real-world data has shown two treatment sequencing patterns in aNSCLC, ICI followed by chemotherapy or chemotherapy followed by ICI, achieved similar clinical benefit. The chemotherapies routinely used following platinum doublet 1st LOT, is effective as the 2nd line option after ICI-chemotherapy combination in the 1st line setting.

Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning-Based Natural Language Processing.

BACKGROUND: Ground-glass opacities (GGOs) appearing in computed tomography (CT) scans may indicate potential lung malignancy. Proper management of GGOs based on their features can prevent the development of lung cancer. Electronic health records are rich sources of information on GGO nodules and their granular features, but most of the valuable information is embedded in unstructured clinical notes. OBJECTIVE: We aimed to develop, test, and validate a deep learning-based natural language processing (NLP) tool that automatically extracts GGO features to inform the longitudinal trajectory of GGO status from large-scale radiology notes. METHODS: We developed a bidirectional long short-term memory with a conditional random field-based deep-learning NLP pipeline to extract GGO and granular features of GGO retrospectively from radiology notes of 13,216 lung cancer patients. We evaluated the pipeline with quality assessments and analyzed cohort characterization of the distribution of nodule features longitudinally to assess changes in size and solidity over time. RESULTS: Our NLP pipeline built on the GGO ontology we developed achieved between 95% and 100% precision, 89% and 100% recall, and 92% and 100% F1-scores on different GGO features. We deployed this GGO NLP model to extract and structure comprehensive characteristics of GGOs from 29,496 radiology notes of 4521 lung cancer patients. Longitudinal analysis revealed that size increased in 16.8% (240/1424) of patients, decreased in 14.6% (208/1424), and remained unchanged in 68.5% (976/1424) in their last note compared to the first note. Among 1127 patients who had longitudinal radiology notes of GGO status, 815 (72.3%) were reported to have stable status, and 259 (23%) had increased/progressed status in the subsequent notes. CONCLUSIONS: Our deep learning-based NLP pipeline can automatically extract granular GGO features at scale from electronic health records when this information is documented in radiology notes and help inform the natural history of GGO. This will open the way for a new paradigm in lung cancer prevention and early detection.

The clinical significance of circulating tumor cells and T lymphocyte subtypes in pancreatic cancer patients.

Circulating tumor cells (CTCs) are sensitive and reliable biomarkers for tracing relapsed and metastatic cancer. Here, we explore the clinical significance of CTCs and T lymphocyte subtypes in patients with pancreatic cancer. A total of 106 patients with the pancreatic cancer were enrolled in this study. The enrichment and identification of CTCs were achieved before treatment by a PatrolCTC detection technique. Flow cytometry (FACS) was used to characterize CD4, CD8, natural killer (NK) cells, and Tregulatory (Treg) lymphocyte subtypes. Interleukin-2 (IL-2), Interleukin-4 (IL-4), Interleukin-17A (IL-17A), Interleukin-10 (IL-10), and Interferon γ (IFN-γ) were measured by meso-scale discovery (MSD) assay. Among these patients, 44 (41.5%) patients with pancreatic ductal adenocarcinoma (PDAC) were female and 62 (58.5%) cases were male. Case numbers with II-IV tumor-node-metastasis (TNM) stages were 32 (30.2%), 50 (47.2%), and 24 (22.6%), respectively. The positive rate of CTCs before surgery was 37.5% (12/32), 88.0% (44/50) and 100% (24/24) in stage II, III, and IV patients, respectively. Total CTCs, mixed CTCs, and mesenchymal CTCs (MCTCs) were strongly relevant to shorter progression-free survival (PFS) of the patients. In addition, total CTCs (≥6) and positive MCTCs were also significantly correlated with recurrence and metastasis. The patients with high CTCs also had low levels of CD4, CD4/CD8 ratio, NK cells, IL-2, and IFNγ. In contrast, Treg cells had significant elevation in PDAC patients. These results indicated that CTCs number in PDAC patients was an independent indicator for worse PFS. High CTCs also had strong correlation with weak cellular immunity functions.

An analysis-ready and quality controlled resource for pediatric brain white-matter research.

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.