Changes in thyroid hormones predict weight regain in patients with obesity who undergo metabolic surgery.

AIM: To investigate the relationship between thyroid function and weight regain in patients with obesity after metabolic surgery. METHODS: This retrospective study enrolled 162 patients who underwent metabolic surgery. Correlations between decreases in thyroid hormone levels and changes in weight, waist circumference (WC) and the Chinese visceral adiposity index (CVAI) were assessed. Binary logistic regression and receiver operating characteristic (ROC) curves were used to identify predictors and clinically useful cut-off values, respectively. RESULTS: The levels of thyroid-stimulating hormone (TSH) and free triiodothyronine (FT3) decreased markedly at 1 year after surgery, as did weight, body mass index (BMI), triglycerides, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, WC and CVAI. Decreases in TSH and FT3 after metabolic surgery were associated with changes in weight, BMI and CVAI. Binary logistic regression and ROC curve analyses confirmed that decreases in TSH can predict good weight loss after metabolic surgery to some extent. Finally, binary logistic regression and ROC curve analyses confirmed that changes in TSH can predict weight regain after metabolic surgery. CONCLUSIONS: Changes in TSH and FT3 after metabolic surgery were correlated with changes in weight and CVAI. Changes in thyroid hormones can predict weight regain in patients with obesity who underwent metabolic surgery.

A combination influenza mRNA vaccine candidate provided broad protection against diverse influenza virus challenge.

Influenza viruses present a significant threat to global health. The production of a universal vaccine is considered essential due to the ineffectiveness of current seasonal influenza vaccines against mutant strains. mRNA technology offers new prospects in vaccinology, with various candidates for different infectious diseases currently in development and testing phases. In this study, we encapsulated a universal influenza mRNA vaccine. The vaccine encoded influenza hemagglutinin (HA), nucleoprotein (NP), and three tandem repeats of matrix protein 2 (3M2e). Twice-vaccinated mice exhibited strong humoral and cell-mediated immune responses in vivo. Notably, these immune responses led to a significant reduction in viral load of the lungs in challenged mice, and also conferred protection against future wild-type H1N1, H3N2, or H5N1 influenza virus challenges. Our findings suggest that this mRNA-universal vaccine strategy for influenza virus may be instrumental in mitigating the impact of future influenza pandemics.

Vibration-Dependent Dual-Phosphorescent Cu4 Nanocluster with Remarkable Piezochromic Behavior.

The dual emission (DE) characteristics of atomically precise copper nanoclusters (Cu NCs) are of significant theoretical and practical interest. Despite this, the underlying mechanism driving DE in Cu NCs remains elusive, primarily due to the complexities of excited state processes. Herein, a novel [Cu4(PPh3)4(C≡C-p-NH2C6H4)3]PF6 (Cu4) NC, shielded by alkynyl and exhibiting DE, was synthesized. Hydrostatic pressure was applied to Cu4, for the first time, to investigate the mechanism of DE. With increasing pressure, the higher-energy emission peak of Cu4 gradually disappeared, leaving the lower-energy emission peak as the dominant emission. Additionally, the Cu4 crystal exhibited notable piezochromism transitioning from cyan to orange. Angle-dispersive synchrotron X-ray diffraction results revealed that the reduced inter-cluster distances under pressure brought the peripheral ligands closer, leading to the formation of new C-H⋅⋅⋅N and N-H⋅⋅⋅N hydrogen bonds in Cu4. It is proposed that these strengthened hydrogen bond interactions limit the ligands' vibration, resulting in the vanishing of the higher-energy peak. In situ high-pressure Raman and vibrationally resolved emission spectra demonstrated that the benzene ring C=C stretching vibration is the structural source of the DE in Cu4.

The Role of Peripheral Inflammatory Markers and Coagulation Factors in Patients with Central Nervous System (CNS) Immune Disease and Glioma.

OBJECTIVE: Gliomas are associated with high rates of disability and mortality, and currently, there is a lack of specific and sensitive biomarkers for diagnosis. The ideal biomarkers should be detected early through noninvasive methods. Our research aims to develop a rapid, convenient, noninvasive diagnostic method for gliomas, as well as for grading and differentiation. METHOD: We retrospectively collected data from patients who underwent surgery for glioma, trigeminal neuralgia/hemifacial spasmschwannoma, and those diagnosed with multiple sclerosis at our institution from January 2018 to December 2020. Inflammatory markers and coagulation factor levels were collected on admission, and neutrophil count (NLR), (WBC count minus neutrophil count) / lymphocyte count, platelet count / lymphocyte count, lymphocyte count / monocyte count, and albumin count [g/L] + total lymphocyte count × 5 were calculated for patients. Analyze the significance of biomarkers in the diagnosis and grading of gliomas, the diagnosis of MS, and the differential diagnosis of them. RESULTS: We evaluated 155 healthy individuals, 64 trigeminal neuralgia/hemifacial spasm patients, 47 MS patients, 316 schwannoma patients, and 814 with glioma patients. Compared with healthy controls and MS group, the preoperative levels of NLR, (WBC count minus neutrophil count) / lymphocyte count, D-dimer, Fibrinogen, Antithrobin, and Factor VIII of glioma patients were significantly higher in glioma patients and positively correlated with the grade of glioma. Conversely, 0020 lymphocyte count / Monocyte count and albumin count [g/L] + total lymphocyte count × 5 were significantly lower and negatively correlated with glioma grading. ROC curves confirmed that for the diagnosis of glioma, NLR showed a maximum area under the curve value of 0.8616 (0.8322-0.8910), followed by D-dimer and Antithrombin, with area under the curve values of 0.8205 (0.7601-0.8809) and 0.8455 (0.8153-0.8758), respectively. NLR and d-dimer also showed great sensitivity in the diagnosis of MS and differential diagnosis with gliomas. CONCLUSIONS: Our study demonstrated that multiple inflammatory markers and coagulation factors could be utilized as biomarkers for the glioma diagnosis, grading, and differential diagnosis of MS. Furthermore, the combination of these markers exhibited high sensitivity and specificity.

Optomechanical Frequency Comb Based on Multiple Nonlinear Dynamics.

Phonon-based frequency combs that can be generated in the optical and microwave frequency domains have attracted much attention due to the small repetition rates and the simple setup. Here, we experimentally demonstrate a new type of phonon-based frequency comb in a silicon optomechanical crystal cavity including both a breathing mechanical mode (∼GHz) and flexural mechanical modes (tens of MHz). We observe strong mode competition between two approximate flexural mechanical modes, i.e., 77.19 and 90.17 MHz, resulting in only one preponderant lasing, while maintaining the lasing of the breathing mechanical mode. These simultaneous observations of two-mode phonon lasing state and significant mode competition are counterintuitive. We have formulated comprehensive theories to elucidate this phenomenon in response to this intriguing outcome. In particular, the self-pulse induced by the free carrier dispersion and thermo-optic effects interacts with two approximate flexural mechanical modes, resulting in the repetition rate of the comb frequency-locked to exact fractions of one of the flexural mechanical modes and the mode hopping between them. This phonon-based frequency comb has at least 260 comblines and a repetition rate as low as a simple fraction of the flexural mechanical frequency. Our demonstration offers an alternative optomechanical frequency comb for sensing, timing, and metrology applications.

Enterotoxin-related genes PPFIA4 and SCN3B promote colorectal cancer development and progression.

To identify the role of enterotoxin-related genes in colorectal cancer (CRC) development and progression. Upregulated differentially expressed genes shared by three out of five Gene Expression Omnibus (GEO) data sets were included to screen the key enterotoxin-induced oncogenes (EIOGs) according to criteria oncogene definition, enrichment, and protein-protein interaction (PPI) network analysis, followed by prognosis survival, immune infiltration, and protential drugs analyses was performed via integration of RNA-sequencing data and The Cancer Genome Atlas-derived clinical profiles. We screened nine common key EIOGs from at least three GEO data sets. A Cox proportional hazards regression models verified that more alive cases, decreased overall survival, and highest 4-year survival prediction in CRC patients with high-risk score. Protein tyrosine phosphatase receptor type F polypeptide-interacting protein alpha-4 (PPFIA4), STY11, SCN3B, and SPTBN5 were shared in the same PPI network. Immune infiltration results showed that SCN3B and synaptotagmin 11 expression were obviously associated with B cell, macrophage, myeloid dendritic cell, neutrophils, and T cell CD4+ and CD8+ in both colon adenocarcinoma and rectal adenocarcinoma. CHIR-99021, MLN4924, and YK4-279 were identified as the potential drugs for treatment. Finally, upregulated EIOGs genes PPFIA4 and SCN3B were found in colon adenocarcinoma and PPFIA4 and SCN3B were proved to promote cell proliferation and migration in vitro. We demonstrated here that EIOGs promoting a malignancy phenotype was related with poor survival and prognosis in CRC, which might be served as novel therapeutic targets in CRC management.

Novel lactylation-related signature to predict prognosis for pancreatic adenocarcinoma.

BACKGROUND: Lactate, previously considered a metabolic byproduct, is pivotal in cancer progression and maintaining the immunosuppressive tumor microenvironment. Further investigations confirmed that lactate is a primary regulator, introducing recently described post-translational modifications of histone and non-histone proteins, termed lysine lactylation. Pancreatic adenocarcinomas are characterized by increased glycolysis and lactate accumulation. However, our understanding of lactylation-related genes in pancreatic adenocarcinomas remains limited. AIM: To construct a novel lactylation-related gene signature to predict the survival of patients with pancreatic cancer. METHODS: RNA-seq and clinical data of pancreatic adenocarcinoma (PDAC) were obtained from the GTEx (Genotype-Tissue Expression) and TCGA (The Cancer Genome Atlas) databases via Xena Explorer, and GSE62452 datasets from GEO. Data on lactylation-related genes were obtained from publicly available sources. Differential expressed genes (DEGs) were acquired by using R package "DESeq2" in R. Univariate COX regression analysis, LASSO Cox and multivariate Cox regressions were produced to construct the lactylation-related prognostic model. Further analyses, including functional enrichment, ESTIMATE, and CIBERSORT, were performed to analyze immune status and treatment responses in patients with pancreatic cancer. PDAC and normal human cell lines were subjected to western blot analysis under lactic acid intervention; two PDAC cell lines with the most pronounced lactylation were selected. Subsequently, RT-PCR was employed to assess the expression of LRGs genes; SLC16A1, which showed the highest expression, was selected for further investigation. SLC16A1-mediated lactylation was analyzed by immunofluorescence, lactate production analysis, colony formation, transwell, and wound healing assays to investigate its role in promoting the proliferation and migration of PDAC cells. In vivo validation was performed using an established tumor model. RESULTS: In this study, we successfully identified 10 differentially expressed lactylation-related genes (LRGs) with prognostic value. Subsequently, a lactylation-related signature was developed based on five OS-related lactylation-related genes (SLC16A1, HLA-DRB1, KCNN4, KIF23, and HPDL) using Lasso Cox hazard regression analysis. Subsequently, we evaluated the clinical significance of the lactylation-related genes in pancreatic adenocarcinoma. A comprehensive examination of infiltrating immune cells and tumor mutation burden was conducted across different subgroups. Furthermore, we demonstrated that SLC16A1 modulates lactylation in pancreatic cancer cells through lactate transport. Both in vivo and in vitro experiments showed that decreasing SLC16A1 Level and its lactylation significantly inhibited tumor progression, indicating the potential of targeting the SLC16A1/Lactylation-associated signaling pathway as a therapeutic strategy against pancreatic adenocarcinoma. CONCLUSION: We constructed a novel lactylation-related prognostic signature to predict OS, immune status, and treatment response of patients with pancreatic adenocarcinoma, providing new strategic directions and antitumor immunotherapies.

Rapid progression of condyloma acuminatum caused by IL-17A antibody treatment: a case report.

Anti interleukin (IL)-17A therapy is a common and effective treatment for psoriasis, but there are also risks of infection. In this case, we presented a patient who experienced a swift progression of condyloma acuminatum on the genitals during psoriasis treatment with secukinumab, a human IL-17A antagonist. Through this case, we strongly suspect that anti IL-17A treatment may promote the onset and rapid progression of low-risk HPV-associated condyloma acuminatum.

Chimeric Antigen Receptor T-Cell Access in Patients with Relapsed/Refractory Large B-Cell Lymphoma: Association of Access with Social Determinants of Health and Travel Time to Treatment Centers.

Large B-cell lymphoma (LBCL) is the most common type of non-Hodgkin lymphoma. Chimeric antigen receptor T-cell (CAR T) therapy represents a novel treatment with curative potential for relapsed or refractory (R/R) LBCL, but there are access barriers to this innovative therapy that are not well-studied. Study objectives were: (1) Assess the impact of geographic factors and social determinants of health (SDOH) on access to treatment with CAR T in a sample of patients with R/R LBCL and ≥2 prior lines of therapy (LOT). (2) Compare and contrast patient characteristics, SDOH, and travel time between patients with R/R LBCL who received CAR T and those who did not. An observational, nested case-control study of patients with R/R LBCL, ≥2 prior LOT, not in a clinical trial, identified using 100% Medicare Fee-For-Service and national multi-payer claims databases. Patients were linked to near-neighborhood SDOH using 9-digit ZIP-code address. Driving distance and time between residence and nearest CAR T treatment center (TC) was calculated. Patients were stratified based on treatments received upon third LOT initiation (Index Date) or later: (1) received CAR T and (2) did not receive CAR T. Multivariable logistic regression was used to evaluate factors associated with CAR T. 5011 patients met inclusion criteria, with 628 (12.5%) in the CAR T group. Regression models found the likelihood of receiving CAR T decreased with patient age (odds ratio [OR] = .96, P < .001), and males were 29% more likely to receive CAR T (OR = 1.29, P = .02). Likelihood of CAR T increased with Charlson Comorbidity Index (CCI; OR = 1.07, P < .001) indicating patients with more comorbidities were more likely to receive CAR T. Black patients were less than half as likely to receive CAR T than White patients (OR = .44, P = .01). Asian patients did not significantly differ from White patients (OR = 1.43, P = .24), and there was a trend for Hispanic patients to have a slightly lower likelihood of CAR T (OR = .50, P = .07). Higher household income was associated with receipt of CAR T, with the lowest income group more than 50% less likely to receive CAR T than the highest (OR = .44, P = .002), and the second lowest income group more than 30% less likely (OR = .68, P = .02). Finally, likelihood of CAR T therapy was reduced when the driving time to the nearest TC was 121 to 240 minutes (reference group: ≤30 minutes; OR = .64, P = .04). Travel times between 31 and 121 or greater than 240 minutes were not significantly different from ≤30 minutes. Payer type was collinear with age and could not be included in the regression analysis, but patients with commercial insurance were 1.5 to 3 times more likely to receive CAR T than other payers on an unadjusted basis. We identified significant disparities in access to CAR T related to demographics and SDOH. Patients who were older, female, low income, or Black were less likely to receive CAR T. The positive association of CCI with CAR T requires further research. Given the promising outcomes of CAR T, there is urgent need to address identified disparities and increase efforts to overcome access barriers.

Advancements in Research on Duck Tembusu Virus Infections.

Duck Tembusu Virus (DTMUV) is a pathogen of the Flaviviridae family that causes infections in poultry, leading to significant economic losses in the duck farming industry in recent years. Ducks infected with this virus exhibit clinical symptoms such as decreased egg production and neurological disorders, along with serious consequences such as ovarian hemorrhage, organ enlargement, and necrosis. Variations in morbidity and mortality rates exist across different age groups of ducks. It is worth noting that DTMUV is not limited to ducks alone; it can also spread to other poultry such as chickens and geese, and antibodies related to DTMUV have even been found in duck farm workers, suggesting a potential risk of zoonotic transmission. This article provides a detailed overview of DTMUV research, delving into its genomic characteristics, vaccines, and the interplay with host immune responses. These in-depth research findings contribute to a more comprehensive understanding of the virus's transmission mechanism and pathogenic process, offering crucial scientific support for epidemic prevention and control.

Intelligent nanovesicle for remodeling tumor microenvironment and circulating tumor chemoimmunotherapy amplification.

Imperceptible examination and unideal treatment effect are still intractable difficulties for the clinical treatment of pancreatic ductal adenocarcinoma (PDAC). At present, despite 5-fluorouracil (5-FU), as a clinical first-line FOLFIRINOX chemo-drug, has achieved significant therapeutic effects. Nevertheless, these unavoidable factors such as low solubility, lack of biological specificity and easy to induce immunosuppressive surroundings formation, severely limit their treatment in PDAC. As an important source of energy for many tumor cells, tryptophan (Trp), is easily degraded to kynurenine (Kyn) by indolamine 2,3- dioxygenase 1 (IDO1), which activates the axis of Kyn-AHR to form special suppressive immune microenvironment that promotes tumor growth and metastasis. However, our research findings that 5-FU can induce effectively immunogenic cell death (ICD) to further treat tumor by activating immune systems, while the secretion of interferon-γ (IFN-γ) re-induce the Kyn-AHR axis activation, leading to poor treatment efficiency. Therefore, a metal matrix protease-2 (MMP-2) and endogenous GSH dual-responsive liposomal-based nanovesicle, co-loading with 5-FU (anti-cancer drug) and NLG919 (IDO1 inhibitor), was constructed (named as ENP919@5-FU). The multifunctional ENP919@5-FU can effectively reshape the tumor immunosuppression microenvironment to enhance the effect of chemoimmunotherapy, thereby effectively inhibiting cancer growth. Mechanistically, PDAC with high expression of MMP-2 will propel the as-prepared nanovesicle to dwell in tumor region via shedding PEG on the nanovesicle surface, effectively enhancing tumor uptake. Subsequently, the S-S bond containing nanovesicle was cut via high endogenous GSH, leading to the continued release of 5-FU and NLG919, thereby enabling circulating chemoimmunotherapy to effectively cause tumor ablation. Moreover, the combination of ENP919@5-FU and PD-L1 antibody (αPD-L1) showed a synergistic anti-tumor effect on the PDAC model with abdominal cavity metastasis. Collectively, ENP919@5-FU nanovesicle, as a PDAC treatment strategy, showed excellent antitumor efficacy by remodeling tumor microenvironment to circulate tumor chemoimmunotherapy amplification, which has promising potential in a precision medicine approach.

Sodium-glucose exchanger 2 inhibitor canagliflozin promotes mitochondrial metabolism and alleviates salt-induced cardiac hypertrophy via preserving SIRT3 expression.

INTRODUCTION: Excess salt intake is not only an independent risk factor for heart failure, but also one of the most important dietary factors associated with cardiovascular disease worldwide. Metabolic reprogramming in cardiomyocytes is an early event provoking cardiac hypertrophy that leads to subsequent cardiovascular events upon high salt loading. Although SGLT2 inhibitors, such as canagliflozin, displayed impressive cardiovascular health benefits, whether SGLT2 inhibitors protect against cardiac hypertrophy-related metabolic reprogramming upon salt loading remain elusive. OBJECTIVES: To investigate whether canagliflozin can improve salt-induced cardiac hypertrophy and the underlying mechanisms. METHODS: Dahl salt-sensitive rats developed cardiac hypertrophy by feeding them an 8% high-salt diet, and some rats were treated with canagliflozin. Cardiac function and structure as well as mitochondrial function were examined. Cardiac proteomics, targeted metabolomics and SIRT3 cardiac-specific knockout mice were used to uncover the underlying mechanisms. RESULTS: In Dahl salt-sensitive rats, canagliflozin showed a potent therapeutic effect on salt-induced cardiac hypertrophy, accompanied by lowered glucose uptake, reduced accumulation of glycolytic end-products and improved cardiac mitochondrial function, which was associated with the recovery of cardiac expression of SIRT3, a key mitochondrial metabolic regulator. Cardiac-specific knockout of SIRT3 not only exacerbated salt-induced cardiac hypertrophy but also abolished the therapeutic effect of canagliflozin. Mechanistically, high salt intake repressed cardiac SIRT3 expression through a calcium-dependent epigenetic modifications, which could be blocked by canagliflozin by inhibiting SGLT1-mediated calcium uptake. SIRT3 improved myocardial metabolic reprogramming by deacetylating MPC1 in cardiomyocytes exposed to pro-hypertrophic stimuli. Similar to canagliflozin, the SIRT3 activator honokiol also exerted therapeutic effects on cardiac hypertrophy. CONCLUSION: Cardiac mitochondrial dysfunction caused by SIRT3 repression is a critical promotional determinant of metabolic pattern switching underlying salt-induced cardiac hypertrophy. Improving SIRT3-mediated mitochondrial function by SGLT2 inhibitors-mediated calcium handling would represent a therapeutic strategy against salt-related cardiovascular events.

CytoSIP: an annotated structural atlas for interactions involving cytokines or cytokine receptors.

Therapeutic agents targeting cytokine-cytokine receptor (CK-CKR) interactions lead to the disruption in cellular signaling and are effective in treating many diseases including tumors. However, a lack of universal and quick access to annotated structural surface regions on CK/CKR has limited the progress of a structure-driven approach in developing targeted macromolecular drugs and precision medicine therapeutics. Herein we develop CytoSIP (Single nucleotide polymorphisms (SNPs), Interface, and Phenotype), a rich internet application based on a database of atomic interactions around hotspots in experimentally determined CK/CKR structural complexes. CytoSIP contains: (1) SNPs on CK/CKR; (2) interactions involving CK/CKR domains, including CK/CKR interfaces, oligomeric interfaces, epitopes, or other drug targeting surfaces; and (3) diseases and phenotypes associated with CK/CKR or SNPs. The database framework introduces a unique tri-level SIP data model to bridge genetic variants (atomic level) to disease phenotypes (organism level) using protein structure (complexes) as an underlying framework (molecule level). Customized screening tools are implemented to retrieve relevant CK/CKR subset, which reduces the time and resources needed to interrogate large datasets involving CK/CKR surface hotspots and associated pathologies. CytoSIP portal is publicly accessible at https://CytoSIP.biocloud.top , facilitating the panoramic investigation of the context-dependent crosstalk between CK/CKR and the development of targeted therapeutic agents.

Emotional "Contagion" in Piglets after Sensory Avoidance of Rewarding and Punishing Treatment.

In the pig farming industry, it is recommended to avoid groups when treating individuals to reduce adverse reactions in the group. However, can this eliminate the adverse effects effectively? Piglets were assigned to the Rewarding Group (RG), the Punishing Group (PG), and the Paired Control Group (PCG). There were six replicates in each group, with two paired piglets per replicate. One piglet of the RG and PG was randomly selected as the Treated pig (TP), treated with food rewards or electric shock, and the other as the Naive pig (NP). The NPs in the RG and PG were unaware of the treatment process, and piglets in the PCG were not treated. The behavior and heart rate changes of all piglets were recorded. Compared to the RG, the NPs in the PG showed longer proximity but less contact behavior, and the TPs in the PG showed more freezing behavior. The percentage change in heart rate of the NPs was synchronized with the TPs. This shows that after sensory avoidance, the untreated pigs could also feel the emotions of their peers and their emotional state was affected by their peers, and the negative emotions in the pigs lasted longer than the positive emotions. The avoidance process does not prevent the transfer of negative emotions to peers via emotional contagion from the stimulated pig.

Psychological pain and sociodemographic factors classified suicide attempt and non-suicidal self-injury in adolescents.

This study aimed to utilize machine learning to explore the psychological similarities and differences between suicide attempt (SA) and non-suicidal self-injury (NSSI), with a particular focus on the role of psychological pain. A total of 2385 middle school students were recruited using cluster sampling. The random forest algorithm was used with 25 predictors to develop classification models of SA and NSSI, respectively, and to estimate the importance scores of each predictor. Based on these scores and related theories, shared risk factors (control feature set) and distinct risk factors (distinction feature set) were selected and tested to distinguish between NSSI and SA. The machine learning algorithm exhibited fair to good performance in classifying SA history [Area Under Curves (AUCs): 0.65-0.87] and poor performance in classifying NSSI history (AUC: 0.61-0.68). The distinction feature set comprised pain avoidance, family togetherness, and deviant peer affiliation, while the control feature set included pain arousal, painful feelings, and crisis events. The distinction feature set slightly but stably outperformed the control feature set in classifying SA from NSSI. The three-dimensional psychological pain model, especially pain avoidance, might play a dominant role in understanding the similarities and differences between SA and NSSI.

Identifying suicide attempter in major depressive disorder through machine learning: the importance of pain avoidance, event-related potential features of affective processing.

How to achieve a high-precision suicide attempt classifier based on the three-dimensional psychological pain model is a valuable issue in suicide research. The aim of the present study is to explore the importance of pain avoidance and its related neural features in suicide attempt classification models among patients with major depressive disorder. By recursive feature elimination with cross-validation and support-vector-machine algorithms, scores from the measurements and the task-based EEG signals were chosen to achieve a suicide attempt classification model. In the multimodal suicide attempt classifier with an accuracy of 83.91% and an area under the curve of 0.90, pain avoidance ranked as the top one in the optimal feature set. Theta (reward positive feedback minus neutral positive feedback) was the shared neural representation ranking as the top one of event-related potential features in pain avoidance and suicide attempt classifiers. In conclusion, the suicide attempt classifier based on pain avoidance and its related affective processing neural features has excellent accuracy among patients with major depressive disorder. Pain avoidance is a stable and strong indicator for identifying suicide risks in both traditional analyses and machine-learning approaches. A novel methodology is needed to clarify the relationship between cognitive and affective processing evoked by punishment stimuli and pain avoidance.

Evaluation of models to predict prognosis in patients with advanced hepatocellular carcinoma treated with TACE combined with apatinib.

BACKGROUND: The HAP, Six-and-Twelve, Up to Seven, and ALBI scores have been substantiated as reliable prognostic markers in patients presenting with intermediate and advanced hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE) treatment. Given this premise, our research aims to assess the predictive efficacy of these models in patients with intermediate and advanced HCC receiving a combination of TACE and Apatinib. Additionally, we have conducted a meticulous comparative analysis of these four scoring systems to discern their respective predictive capacities and efficacies in combined therapy. METHODS: Performing a retrospective analysis on the clinical data from 200 patients with intermediate and advanced HCC, we studied those who received TACE combined with Apatinib at the First Affiliated Hospital of the University of Science and Technology of China between June 2018 and December 2022. To identify the factors affecting survival, the study performed univariate and multivariate Cox regression analyses, with calculations of four different scores: HAP, Six-and-Twelve, Up to Seven, and ALBI. Lastly, Harrell's C-index was employed to compare the prognostic abilities of these scores. RESULTS: Cox proportional hazards model results revealed that the ALBI score, presence of portal vein tumor thrombus (PVTT, )and tumor size are independent determinants of prognostic survival. The Kaplan-Meier analyses showed significant differences in survival rates among patients classified by the HAP, Six-and-Twelve, Up to Seven, and ALBI scoring methods. Of the evaluated systems, the HAP scoring demonstrated greater prognostic precision, with a Harrell's C-index of 0.742, surpassing the alternative models (P < 0.05). In addition, an analysis of the area under the AU-ROC curve confirms the remarkable superiority of the HAP score in predicting short-term survival outcomes. CONCLUSION: Our study confirms the predictive value of HAP, Six-and-Twelve, Up to Seven, and ALBI scores in intermediate to advanced Hepatocellular Carcinoma (HCC) patients receiving combined Transarterial Chemoembolization (TACE) and Apatinib therapy. Notably, the HAP model excels in predicting outcomes for this specific HCC subgroup.

Successful treatment of refractory prurigo nodularis with abrocitinib.

Prurigo nodularis is frequently difficult to manage with conventional therapy. Given the pathogenesis and refractory nature, we demonstrate a case in which inhibition of JAK-STAT signaling may significantly improve prurigo nodularis. Based on the results, we would like to draw a conclusion that abrocitinib as an inhibitor of Jak is a promising choice for the treatment of prurigo nodularis.

A Comprehensive Survey on Deep Graph Representation Learning.

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future.