Taurine-conjugated bile acids and their link to hepatic S1PR2 play a significant role in hepatitis C-related liver disease.

BACKGROUND: Bile acids mediate gut-liver cross-talk through bile acid receptors. Serum, hepatic, and microbial bile acid metabolism was evaluated in HCV-compensated chronic liver disease. METHODS: Patients underwent liver biopsy; portal and peripheral blood were obtained before (HCVi), and 6 months after sustained virologic response (SVR), splenic blood was obtained only after SVR. The fecal microbiome and liver transcriptome were evaluated using RNA-Seq. Twenty-four bile acids were measured in serum, summed as free, taurine-conjugated bile acids (Tau-BAs), and glycine-conjugated bile acids. RESULTS: Compared to SVR, HCVi showed elevated conjugated bile acids, predominantly Tau-BA, compounded in HCVi cirrhosis. In the liver, transcription of bile acids uptake, synthesis, and conjugation was decreased with increased hepatic spillover into systemic circulation in HCVi. There was no difference in the transcription of microbial bile acid metabolizing genes in HCVi. Despite an overall decrease, Tau-BA remained elevated in SVR cirrhosis, mainly in splenic circulation. Only conjugated bile acids, predominantly Tau-BA, correlated with serum proinflammatory markers and hepatic proinflammatory pathways, including NLRP3 and NFKB. Among hepatic bile acid receptors, disease-associated conjugated bile acids showed the strongest association with hepatic spingosine-1-phosphate receptor 2 (S1PR2). CONCLUSIONS: Enhanced expression of hepatic S1PR2 in HCVi and HCVi-cirrhosis and strong associations of S1PR2 with Tau-BAs suggest pathological relevance of Tau-BA-hepatic S1PR2 signaling in chronic liver disease. These findings have therapeutic implications in chronic liver diseases.

Machine-learning-based Structural Analysis of Interactions between Antibodies and Antigens.

Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem. To tackle this challenge, we implemented a deep learning model to characterize interaction patterns between antibodies and their corresponding antigens. With high accuracy, our model can distinguish between antibody-antigen complexes and other types of protein-protein complexes. More intriguingly, we can identify antigens from other common protein binding regions with an accuracy of higher than 70% even if we only have the epitope information. This indicates that antigens have distinct features on their surface that antibodies can recognize. Additionally, our model was unable to predict the partnerships between antibodies and their particular antigens. This result suggests that one antigen may be targeted by more than one antibody and that antibodies may bind to previously unidentified proteins. Taken together, our results support the precision of antibody-antigen interactions while also suggesting positive future progress in the prediction of specific pairing.

Management of Pain and Headache After Traumatic Brain Injury.

This article will identify common causes of pain following traumatic brain injury (TBI), discuss current treatment strategies for these complaints, and help tailor treatments for both acute and chronic settings. We will also briefly discuss primary and secondary headache disorders, followed by common secondary pain disorders that may be related to trauma.

Molecular targets of glucocorticoids that elucidate their therapeutic efficacy in aggressive lymphomas.

Glucocorticoids have been used for decades to treat lymphomas without an established mechanism of action. Using functional genomic, proteomic, and chemical screens, we discover that glucocorticoids inhibit oncogenic signaling by the B cell receptor (BCR), a recurrent feature of aggressive B cell malignancies, including diffuse large B cell lymphoma and Burkitt lymphoma. Glucocorticoids induce the glucocorticoid receptor (GR) to directly transactivate genes encoding negative regulators of BCR stability (LAPTM5; KLHL14) and the PI3 kinase pathway (INPP5D; DDIT4). GR directly represses transcription of CSK, a kinase that limits the activity of BCR-proximal Src-family kinases. CSK inhibition attenuates the constitutive BCR signaling of lymphomas by hyperactivating Src-family kinases, triggering their ubiquitination and degradation. With the knowledge that glucocorticoids disable oncogenic BCR signaling, they can now be deployed rationally to treat BCR-dependent aggressive lymphomas and used to construct mechanistically sound combination regimens with inhibitors of BTK, PI3 kinase, BCL2, and CSK.

A Comprehensive Analysis of Tobacco Smoking History as a Risk for Outcomes after Endoscopic Transsphenoidal Resection of Pituitary Adenoma.

Objectives This study seeks to comprehensively analyze the impact of smoking history on outcomes after endoscopic transsphenoidal hypophysectomy (TSH) for pituitary adenoma. Design This was a retrospective study. Setting This study was done at the tertiary care center. Participants Three hundred and ninety-eight adult patients undergoing TSH for a pituitary adenoma. Main Outcome Measures Clinical and tumor characteristics and operative factors were collected. Patients were categorized as never, former, or active smokers, and the pack-years of smoking history was collected. Years since cessation of smoking was obtained for former smokers. Specific outcomes included postoperative cerebrospinal fluid (CSF) leak, length of hospitalization, 30-day return to the operating room, and 30-day readmission. Smoking history details were comprehensively analyzed for association with outcomes. Results Any history of smoking tobacco was associated with return to the operating room (odds ratio [OR] = 2.67, 95% confidence interval [CI]: 1.05-6.76, p = 0.039), which was for persistent CSF leak in 58.3%. Among patients with postoperative CSF leak, any history of smoking was associated with need for return to the operating room to repair the CSF leak (OR = 5.25, 95% CI: 1.07-25.79, p = 0.041). Pack-years of smoking was positively associated with a return to the operating room (OR = 1.03, 95% CI: 1.01-1.06, p = 0.048). In all multivariable models, all negative outcomes were significantly associated with the covariate: occurrence of intraoperative CSF leak. Conclusion This is the first study to show smoking may have a negative impact on healing of CSF leak repairs after TSH, requiring a return to the operating room. This effect appears to be dose dependent on the smoking history. Secondarily, intraoperative CSF leak as covariate in multivariable models was significantly associated with all negative outcomes.

I Am Curious About Cannabis Edibles-What Should I Know?

This JAMA Internal Medicine Patient Page describes the advantages, risks, and safe use of cannabis edibles.

Recalcitrant Pyoderma Gangrenosum of the Face: A Case Report.

Pyoderma gangrenosum (PG) is a rare autoinflammatory neutrophilic dermatosis. The ulcerative subtype presents with a tender nodule or pustule that progresses into a painful, necrotic ulcer.New lesions arise after minor trauma in one-third of patients, a phenomenon termed "pathergy." We present a 62-year-old Caucasian female with primary sclerosing cholangitis, hepatic cirrhosis, chronic hepatitis B, and severe PG. At the initial presentation, she had lesions on her face and four extremities. She had severe full-thickness ulcerations on the bilateral cheeks and underwent incision and drainage with washout of bilateral maxillary abscesses, left sinus curettage, and wound debridement. She has required multiple hospitalizations for severe flares. Treatment with steroids was complicated by spinal compression fractures. Steroid-sparring agents were ineffective. Her lesions involved bilateral cheeks, temples, temporal scalp, and eyelids with oroantral fistulae. Her facial ulcerations included a large septal perforation causing saddle nose deformity and eradication of a branch of the left facial nerve causing incomplete eye closure. She underwent bilateral facial wound irrigation with antibiotic irrigation and wound debridement. Due to social factors, she has been lost to follow-up and a definitive etiology of her PG has not yet been elucidated. Although rare, PG should remain a consideration in patients with ulcerative lesions on the head and neck. Wound debridement is typically discouraged given the risk of pathergy, but there may be a role for surgical intervention in adequately immunosuppressed patients.

DSG-GAN:A dual-stage-generator-based GAN for cross-modality synthesis from PET to CT.

PET/CT devices typically use CT images for PET attenuation correction, leading to additional radiation exposure. Alternatively, in a standalone PET imaging system, attenuation and scatter correction cannot be performed due to the absence of CT images. Therefore, it is necessary to explore methods for generating pseudo-CT images from PET images. However, traditional PET-to-CT synthesis models encounter conflicts in multi-objective optimization, leading to disparities between synthetic and real images in overall structure and texture. To address this issue, we propose a staged image generation model. Firstly, we construct a dual-stage generator, which synthesizes the overall structure and texture details of images by decomposing optimization objectives and employing multiple loss functions constraints. Additionally, in each generator, we employ improved deep perceptual skip connections, which utilize cross-layer information interaction and deep perceptual selection to effectively and selectively leverage multi-level deep information and avoid interference from redundant information. Finally, we construct a context-aware local discriminator, which integrates context information and extracts local features to generate fine local details of images and reasonably maintain the overall coherence of the images. Experimental results demonstrate that our approach outperforms other methods, with SSIM, PSNR, and FID metrics reaching 0.8993, 29.6108, and 29.7489, respectively, achieving the state-of-the-art. Furthermore, we conduct visual experiments on the synthesized pseudo-CT images in terms of image structure and texture. The results indicate that the pseudo-CT images synthesized in this study are more similar to real CT images, providing accurate structure information for clinical disease analysis and lesion localization.

The Impact of the Post-Traumatic Facial Reconstruction on Postoperative Weight Loss.

Objectives: To identify risk factors and evaluate the impact of various facial fractures and reconstruction surgeries on postoperative weight change. Methods: Retrospective, monocentric study was performed at a tertiary care center. Medical history, type and mechanism of fracture, operative factors, and postoperative weights at follow-up appointments for 145 adult patients undergoing surgical repair for maxillofacial fractures were collected. Further information was obtained on postoperative diet and whether patients received maxillomandibular fixation (MMF). Univariate and multivariate analyses were utilized to evaluate effects of surgical reconstruction after facial trauma on postoperative weight loss. Results: Patients lost 3.2 ± 4.9 kg (95% confidence interval = 2.7-4.1, P < .0001) on average, with maximum loss between date of surgery and first follow-up. Univariate analysis demonstrated that intensive care unit admission (5.9 kg, SD 5.4, P = .001), nasogastric tube placement (5.1 kg, SD 4.6, P = .012), and MMF (4.4 kg, SD 5.4, P < .0001) were associated with more severe weight loss. Multivariate analyses showed that only MMF remained a significant risk factor for increased weight loss (avg. 6.0, standard error 1.93, t value 3.11, P = .0024). Conclusions: We report significant weight loss following facial trauma and reconstruction, which emphasizes the need to perform further studies on nutrition protocols for this patient population to optimize wound healing.

Contraception in patients with liver disease and liver transplant.

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Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records.

BACKGROUND: Although the 2019 EULAR/ACR classification criteria for systemic lupus erythematosus (SLE) has required at least a positive anti-nuclear antibody (ANA) titer (≥ 1:80), it remains challenging for clinicians to identify patients with SLE. This study aimed to develop a machine learning (ML) approach to assist in the detection of SLE patients using genomic data and electronic health records. METHODS: Participants with a positive ANA (≥ 1:80) were enrolled from the Taiwan Precision Medicine Initiative cohort. The Taiwan Biobank version 2 array was used to detect single nucleotide polymorphism (SNP) data. Six ML models, Logistic Regression, Random Forest (RF), Support Vector Machine, Light Gradient Boosting Machine, Gradient Tree Boosting, and Extreme Gradient Boosting (XGB), were used to identify SLE patients. The importance of the clinical and genetic features was determined by Shapley Additive Explanation (SHAP) values. A logistic regression model was applied to identify genetic variations associated with SLE in the subset of patients with an ANA equal to or exceeding 1:640. RESULTS: A total of 946 SLE and 1,892 non-SLE controls were included in this analysis. Among the six ML models, RF and XGB demonstrated superior performance in the differentiation of SLE from non-SLE. The leading features in the SHAP diagram were anti-double strand DNA antibodies, ANA titers, AC4 ANA pattern, polygenic risk scores, complement levels, and SNPs. Additionally, in the subgroup with a high ANA titer (≥ 1:640), six SNPs positively associated with SLE and five SNPs negatively correlated with SLE were discovered. CONCLUSIONS: ML approaches offer the potential to assist in diagnosing SLE and uncovering novel SNPs in a group of patients with autoimmunity.

Dynamic Elevation of Aromatic Amino Acids in Hepatitis C Virus-Induced Cirrhosis After a Standard Meal.

INTRODUCTION: Perturbations in aromatic (AAAs) and branched-chain amino acids (BCAAs) are seen in decompensated liver disease. The aim of this study was to evaluate the dynamic, postprandial relationship between hepatitis C virus-induced liver disease and amino acid concentrations in patients with compensated liver disease. METHODS: Patients infected with hepatitis C virus underwent a baseline liver biopsy to determine Ishak Fibrosis Score and evaluate the liver transcriptome. Patients ate a standard meal and underwent peripheral vein sampling at defined intervals. Quantitative analysis of amino acids was performed using liquid chromatography-tandem mass spectrometry. RESULTS: At baseline, there was no difference in AAA and BCAA concentrations between patients with cirrhosis and non-cirrhotic patients. After a standard meal, AAAs, but not BCAAs, were elevated in patients with cirrhosis compared with non-cirrhotic patients at every time point. The HepQuant SHUNT fraction was significantly higher in patients with cirrhosis and positively correlated with AAA concentration at all time points, but not BCAA. Analysis of the hepatic transcriptome demonstrated greater downregulation of the AAA degradation pathways than the BCAA degradation pathways. DISCUSSION: At baseline, cirrhotic patients with compensated liver disease have adequate reserve liver function to metabolize AAAs and BCAAs. When faced with a metabolic stressor, such as a standard meal, patients with cirrhosis are less able to metabolize the increased load of AAAs. This impairment correlates with portosystemic shunting. Further evaluation of AAA levels in compensated liver disease might further the understanding of the liver-muscle axis and the role it may play in the development of sarcopenia in liver disease.

I Am Worried I Have Sciatica-What Do I Need to Know?

This JAMA Internal Medicine Patient Page reviews sciatica, its symptoms, and treatment options for those who have it.

Machine-learning-based Structural Analysis of Interactions between Antibodies and Antigens.

Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem. To tackle this challenge, we implemented a deep learning model to characterize interaction patterns between antibodies and their corresponding antigens. With high accuracy, our model can distinguish between antibody-antigen complexes and other types of protein-protein complexes. More intriguingly, we can identify antigens from other common protein binding regions with an accuracy of higher than 70% even if we only have the epitope information. This indicates that antigens have distinct features on their surface that antibodies can recognize. Additionally, our model was unable to predict the partnerships between antibodies and their particular antigens. This result suggests that one antigen may be targeted by more than one antibody and that antibodies may bind to previously unidentified proteins. Taken together, our results support the precision of antibody-antigen interactions while also suggesting positive future progress in the prediction of specific pairing.

Geometric frustration of hard-disk packings on cones.

Conical surfaces pose an interesting challenge to crystal growth: A crystal growing on a cone can wrap around and meet itself at different radii. We use a disk-packing algorithm to investigate how this closure constraint can geometrically frustrate the growth of single crystals on cones with small opening angles. By varying the crystal seed orientation and cone angle, we find that-except at special commensurate cone angles-crystals typically form a seam that runs along the axial direction of the cone, while near the tip, a disordered particle packing forms. We show that the onset of disorder results from a finite-size effect that depends strongly on the circumference and not on the seed orientation or cone angle. This finite-size effect occurs also on cylinders, and we present evidence that on both cylinders and cones, the defect density increases exponentially as circumference decreases. We introduce a simple model for particle attachment at the seam that explains the dependence on the circumference. Our findings suggest that the growth of single crystals can become frustrated even very far from the tip when the cone has a small opening angle. These results may provide insights into the observed geometry of conical crystals in biological and materials applications.

"It's What We Can Do Right Now": Professional Identity Formation Among Internal Medicine Residents During the COVID-19 Pandemic.

PURPOSE: The COVID-19 pandemic represents a consequential moment of disruption for medical training that has far-reaching implications for professional identity formation (PIF). To date, this has not been studied. As medical education grapples with a postpandemic era, it is essential to gain insight into how the pandemic has influenced PIF to better support its positive influences and mitigate its more detrimental effects. This study examined how PIF occurred during the COVID-19 pandemic to better adapt future medical training. METHOD: Constructivist grounded theory guided the iterative data collection and analyses. The authors conducted semistructured group interviews with 24 Ontario internal medicine residents in postgraduate years (PGYs) 1 to 3 between November 2020 and July 2021. Participants were asked to reflect on their day-to-day clinical and learning experiences during the pandemic. RESULTS: Twenty-four internal medicine residents were interviewed (12 PGY-1 [50.0%], 9 PGY-2 [37.5%], and 3 PGY-3 [12.5%]). Participants described how navigating patient care and residency training through the pandemic consistently drew their attention to various system problems. How participants responded to these problems was shaped by an interplay among their personal values, their level of personal wellness or burnout, self-efficacy, institutional values, and the values of their supervisors and work community. As they were influenced by these factors, some were led toward acting on the problem(s) they identified, whereas others had a sense of resignation and deferred action. These interactions were evident in participants' experiences with communication, advocacy, and learning. CONCLUSIONS: Residents' professional identities are continuously shaped by how they perceive, reconcile, and address various challenges. As residents navigate tensions between personally held values and apparent system values, individuals in supervisory positions should be mindful of their influence as role models who empower values and practices that are recognized by participants to be important aspects of physician identity.