Improving drug-drug interactions prediction with interpretability via meta-path-based information fusion.

Drug-drug interactions (DDIs) are compound effects when patients take two or more drugs at the same time, which may weaken the efficacy of drugs or cause unexpected side effects. Thus, accurately predicting DDIs is of great significance for the drug development and the drug safety surveillance. Although many methods have been proposed for the task, the biological knowledge related to DDIs is not fully utilized and the complex semantics among drug-related biological entities are not effectively captured in existing methods, leading to suboptimal performance. Moreover, the lack of interpretability for the predicted results also limits the wide application of existing methods for DDIs prediction. In this study, we propose a novel framework for predicting DDIs with interpretability. Specifically, we construct a heterogeneous information network (HIN) by explicitly utilizing the biological knowledge related to the procedure of inducing DDIs. To capture the complex semantics in HIN, a meta-path-based information fusion mechanism is proposed to learn high-quality representations of drugs. In addition, an attention mechanism is designed to combine semantic information obtained from meta-paths with different lengths to obtain final representations of drugs for DDIs prediction. Comprehensive experiments are conducted on 2410 approved drugs, and the results of predictive performance comparison show that our proposed framework outperforms selected representative baselines on the task of DDIs prediction. The results of ablation study and cold-start scenario indicate that the meta-path-based information fusion mechanism red is beneficial for capturing the complex semantics among drug-related biological entities. Moreover, the results of case study demonstrate that the designed attention mechanism is able to provide partial interpretability for the predicted DDIs. Therefore, the proposed method will be a feasible solution to the task of predicting DDIs.

Causal-ARG: a causality-guided framework for annotating properties of antibiotic resistance genes.

MOTIVATION: The crisis of antibiotic resistance, which causes antibiotics used to treat bacterial infections to become less effective, has emerged as one of the foremost challenges to public health. Identifying the properties of antibiotic resistance genes (ARGs) is an essential way to mitigate this issue. Although numerous methods have been proposed for this task, most of these approaches concentrate solely on predicting antibiotic class, disregarding other important properties of ARGs. In addition, existing methods for simultaneously predicting multiple properties of ARGs fail to account for the causal relationships among these properties, limiting the predictive performance. RESULTS: In this study, we propose a causality-guided framework for annotating properties of ARGs, in which causal inference is utilized for representation learning. More specifically, the hidden biological patterns determining the properties of ARGs are described by a Gaussian Mixture Model, and procedure of causal representation learning is used to derive the hidden features. In addition, a causal graph among different properties is constructed to capture the causal relationships among properties of ARGs, which is integrated into the task of annotating properties of ARGs. The experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework on the task of annotating properties of ARGs. AVAILABILITY AND IMPLEMENTATION: The data and source codes are available in GitHub at https://github.com/David-WZhao/CausalARG.

Camrelizumab plus rivoceranib versus sorafenib as first-line therapy for unresectable hepatocellular carcinoma (CARES-310): a randomised, open-label, international phase 3 study.

BACKGROUND: Immunotherapy with immune checkpoint inhibitors combined with an anti-angiogenic tyrosine-kinase inhibitor (TKI) has been shown to improve overall survival versus anti-angiogenic therapy alone in advanced solid tumours, but not in hepatocellular carcinoma. Therefore, a clinical study was conducted to compare the efficacy and safety of the anti-PD-1 antibody camrelizumab plus the VEGFR2-targeted TKI rivoceranib (also known as apatinib) versus sorafenib as first-line treatment for unresectable hepatocellular carcinoma. METHODS: This randomised, open-label, international phase 3 trial (CARES-310) was done at 95 study sites across 13 countries and regions worldwide. Patients with unresectable or metastatic hepatocellular carcinoma who had not previously received any systemic treatment were randomly assigned (1:1) to receive either camrelizumab 200 mg intravenously every 2 weeks plus rivoceranib 250 mg orally once daily or sorafenib 400 mg orally twice daily. Randomisation was done via a centralised interactive response system. The primary endpoints were progression-free survival, as assessed by the blinded independent review committee per Response Evaluation Criteria in Solid Tumours version 1.1, and overall survival in the intention-to-treat population. Safety was assessed in all patients who received at least one dose of the study drugs. We report the findings from the prespecified primary analysis for progression-free survival and interim analysis for overall survival. This study is registered with ClinicalTrials.gov (NCT03764293). FINDINGS: Between June 28, 2019, and March 24, 2021, 543 patients were randomly assigned to the camrelizumab-rivoceranib (n=272) or sorafenib (n=271) group. At the primary analysis for progression-free survival (May 10, 2021), median follow-up was 7·8 months (IQR 4·1-10·6). Median progression-free survival was significantly improved with camrelizumab-rivoceranib versus sorafenib (5·6 months [95% CI 5·5-6·3] vs 3·7 months [2·8-3·7]; hazard ratio [HR] 0·52 [95% CI 0·41-0·65]; one-sided p<0·0001). At the interim analysis for overall survival (Feb 8, 2022), median follow-up was 14·5 months (IQR 9·1-18·7). Median overall survival was significantly extended with camrelizumab-rivoceranib versus sorafenib (22·1 months [95% CI 19·1-27·2] vs 15·2 months [13·0-18·5]; HR 0·62 [95% CI 0·49-0·80]; one-sided p<0·0001). The most common grade 3 or 4 treatment-related adverse events were hypertension (102 [38%] of 272 patients in the camrelizumab-rivoceranib group vs 40 [15%] of 269 patients in the sorafenib group), palmar-plantar erythrodysaesthesia syndrome (33 [12%] vs 41 [15%]), increased aspartate aminotransferase (45 [17%] vs 14 [5%]), and increased alanine aminotransferase (35 [13%] vs eight [3%]). Treatment-related serious adverse events were reported in 66 (24%) patients in the camrelizumab-rivoceranib group and 16 (6%) in the sorafenib group. Treatment-related death occurred in two patients: one patient in the camrelizumab-rivoceranib group (ie, multiple organ dysfunction syndrome) and one patient in the sorafenib group (ie, respiratory failure and circulatory collapse). INTERPRETATION: Camrelizumab plus rivoceranib showed a statistically significant and clinically meaningful benefit in progression-free survival and overall survival compared with sorafenib for patients with unresectable hepatocellular carcinoma, presenting as a new and effective first-line treatment option for this population. FUNDING: Jiangsu Hengrui Pharmaceuticals and Elevar Therapeutics.

A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling.

The increasing prevalence of antibiotic resistance has become a global health crisis. For the purpose of safety regulation, it is of high importance to identify antibiotic resistance genes (ARGs) in bacteria. Although culture-based methods can identify ARGs relatively more accurately, the identifying process is time-consuming and specialized knowledge is required. With the rapid development of whole genome sequencing technology, researchers attempt to identify ARGs by computing sequence similarity from public databases. However, these computational methods might fail to detect ARGs due to the low sequence identity to known ARGs. Moreover, existing methods cannot effectively address the issue of multidrug resistance prediction for ARGs, which is a great challenge to clinical treatments. To address the challenges, we propose an end-to-end multi-label learning framework for predicting ARGs. More specifically, the task of ARGs prediction is modeled as a problem of multi-label learning, and a deep neural network-based end-to-end framework is proposed, in which a specific loss function is introduced to employ the advantage of multi-label learning for ARGs prediction. In addition, a dual-view modeling mechanism is employed to make full use of the semantic associations among two views of ARGs, i.e. sequence-based information and structure-based information. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs prediction.

Symptom profiles in patients receiving maintenance hemodialysis and their association with quality of life: a longitudinal study.

PURPOSE: Many patients receiving maintenance hemodialysis experience one or multiple symptoms. Using a latent profile analysis to identify symptom profiles may provide insights for person-centered symptom management strategies. METHODS: This is a longitudinal study based on data from patients receiving maintenance hemodialysis at three hospitals in Shanghai, China. Of the 448 patients who completed the surveys at baseline (T1), 309 completed the 12-month follow-up survey (T2). Symptoms and quality of life were measured by the Chinese version of Kidney Disease Quality of Life 36 Short Form. The optimal classification of symptoms was identified using latent profile analysis. RESULTS: Five symptom profiles were identified: High (9.2%), Fatigue and Gastrointestinal (7.1%), Fatigue and Skin (10.7%), Skin (23.2%), and Low (49.8%). The high-symptom profile and the-fatigue-and-skin-symptom profile were associated with a lower level of physical functioning, a higher burden of kidney disease, and more negative effects of kidney disease than the low symptom profile at T1 and T2. Multivariate regression analysis showed that the high-symptom profile predicted a poorer physical functioning at T2, and the-fatigue-and-skin-symptom profile predicted a poorer physical functioning and higher burden of kidney disease at T2. CONCLUSION: Patients receiving maintenance hemodialysis reported unique symptom experiences which could be classified into different profiles. Patients reporting an overall high level of symptoms or a high level of fatigue and skin symptoms were more likely to have a poorer quality of life.

Mood assessments of family caregivers of patients with severe brain injury in China.

OBJECTIVES: Long-term care of severe brain injury patients places a significant mental burden on family caregivers, yet few studies have reported the situation in China. We aimed to describe the mood states of family caregivers of patients with severe brain injury and examine the influencing factors that affect caregivers' moods. METHODS: Cross-sectional survey was used to assess the mood profiles of Chinese family caregivers between February 2019 and February 2020. Demographic data of caregivers and patients, the Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder scale (GAD-7) were used to assess the level of depressive and anxiety symptoms. The quality of life score was also assessed by a visual analog scale, and the Coma Recovery Scale-Revised was used to assess the patient's consciousness. RESULT: One hundred and one patients with severe brain injury (57 unresponsive wakefulness syndrome, UWS) between the age of 14 and 70 and their main family caregivers were enrolled in the study. Most caregivers displayed depressive (n = 62) and anxiety symptoms (n = 65), with 17 and 20 of these family caregivers reporting (moderately) severe depressive symptom and severe anxiety symptom, respectively. The caregiver's depressive symptom level significantly decreased as the patient's injury lasted longer (r = - 0.208, P = 0.037). Moreover, the age of the patient negatively related to the levels of depressive (r = - 0.310, P = 0.002) and anxiety symptoms (r = - 0.289, P = 0.003) in caregivers. There was a significant positive correlation between anxiety and depressive symptoms scores in family caregivers (r = 0.838, P < 0.001). The higher the level of anxiety (r = - 0.273, P = 0.006) and depressive symptoms (r = - 0.265, P = 0.007), the worse the quality of life. CONCLUSION: Many family caregivers of patients with severe brain injury experience various levels of anxiety and depressive symptoms in China. Tailor-made psychological help seems imperative. Researchers and doctors can provide information about patient's conditions to assist family members in discussing rehabilitation options for patients in different states of consciousness will help to ease anxiety of family caregivers.

Perception of diagnosis by family caregivers in severe brain injury patients in China.

OBJECTIVES: Surrogate decision-making by family caregivers for patients with severe brain injury is influenced by the availability and understanding of relevant information and expectations for future rehabilitation. We aimed to compare the consistency of family caregivers' perceptions with clinical diagnoses and to inform their expectation of prognosis in the future. METHODS: The Coma Recovery Scale-Revised was used to assess the diagnosis of inpatients with severe brain injury between February 2019 and February 2020. A main family caregiver was included per patient. The family caregiver's perception of the patient's consciousness and expectations of future recovery were collected through questionnaires and compared consistently with the clinical diagnosis. RESULTS: The final sample included 101 main family caregivers of patients (57 UWS, unresponsive wakefulness syndrome, 37 MCS, minimally conscious state, 7 EMCS, emergence from MCS) with severe brain injury. Only 57 family caregivers correctly assessed the level of consciousness as indicated by the CRS-R, showing weak consistency (Kappa = 0.217, P = 0.002). Family caregivers' demographic characteristics and CRS-R diagnosis influenced the consistency between perception and clinical diagnosis. Family caregivers who provided hands-on care to patients showed higher levels of consistent perception (AOR = 12.24, 95% CI = 2.06-73.00, P = 0.006). Compared to UWS, the family caregivers of MCS patients were more likely to have a correct perception (OR = 7.68, 95% CI = 1.34-44.06). Family caregivers had positive expectations for patients' recovery in terms of both communication and returning to normal life. CONCLUSION: Nearly half of family caregivers have inadequate understanding of their relative's level of consciousness, and most of them report overly optimistic expectations that do not align with clinical diagnosis. Providing more medical information to family caregivers to support their surrogate decision-making process is essential.

Novel decision tree models predict the overall survival of patients with submandibular gland cancer.

BACKGROUND: While the accurate prediction of the overall survival (OS) in patients with submandibular gland cancer (SGC) is paramount for informed therapeutic planning, the development of reliable survival prediction models has been hindered by the rarity of SGC cases. The purpose of this study is to identify key prognostic factors for OS in SGC patients using a large database and construct decision tree models to aid the prediction of survival probabilities in 12, 24, 60 and 120 months. MATERIALS AND METHODS: We performed a retrospective cohort study using the Surveillance, Epidemiology and End Result (SEER) program. Demographic and peri-operative predictor variables were identified. The outcome variables overall survival at 12-, 24-, 60, and 120 months. The C5.0 algorithm was utilized to establish the dichotomous decision tree models, with the depth of tree limited within 4 layers. To evaluate the performances of the novel models, the receiver operator characteristic (ROC) curves were generated, and the metrics such as accuracy rate, and area under ROC curve (AUC) were calculated. RESULTS: A total of 1,705, 1,666, 1,543, and 1,413 SGC patients with a follow up of 12, 24, 60 and 120 months and exact survival status were identified from the SEER database. Predictor variables of age, sex, surgery, radiation, chemotherapy, tumor histology, summary stage, metastasis to distant lymph node, and marital status exerted substantial influence on overall survival. Decision tree models were then developed, incorporating these vital prognostic indicators. Favorable consistency was presented between the predicted and actual survival statuses. For the training dataset, the accuracy rates for the 12-, 24-, 60- and 120-month survival models were 0.866, 0.767, 0.737 and 0.797. Correspondingly, the AUC values were 0.841, 0.756, 0.725, and 0.774 for the same time points. CONCLUSIONS: Based on the most important predictor variables identified using the large, SEER database, decision tree models were established that predict OS of SGC patients. The models offer a more exhaustive evaluation of mortality risk and may lead to more personalized treatment strategies.

Perioperative Toripalimab Plus Chemotherapy for Patients With Resectable Non-Small Cell Lung Cancer: The Neotorch Randomized Clinical Trial.

Importance: Adjuvant and neoadjuvant immunotherapy have improved clinical outcomes for patients with early-stage non-small cell lung cancer (NSCLC). However, the optimal combination of checkpoint inhibition with chemotherapy remains unknown. Objective: To determine whether toripalimab in combination with platinum-based chemotherapy will improve event-free survival and major pathological response in patients with stage II or III resectable NSCLC compared with chemotherapy alone. Design, Setting, and Participants: This randomized clinical trial enrolled patients with stage II or III resectable NSCLC (without EGFR or ALK alterations for nonsquamous NSCLC) from March 12, 2020, to June 19, 2023, at 50 participating hospitals in China. The data cutoff date for this interim analysis was November 30, 2022. Interventions: Patients were randomized in a 1:1 ratio to receive 240 mg of toripalimab or placebo once every 3 weeks combined with platinum-based chemotherapy for 3 cycles before surgery and 1 cycle after surgery, followed by toripalimab only (240 mg) or placebo once every 3 weeks for up to 13 cycles. Main Outcomes and Measures: The primary outcomes were event-free survival (assessed by the investigators) and the major pathological response rate (assessed by blinded, independent pathological review). The secondary outcomes included the pathological complete response rate (assessed by blinded, independent pathological review) and adverse events. Results: Of the 501 patients randomized, 404 had stage III NSCLC (202 in the toripalimab + chemotherapy group and 202 in the placebo + chemotherapy group) and 97 had stage II NSCLC and were excluded from this interim analysis. The median age was 62 years (IQR, 56-65 years), 92% of patients were male, and the median follow-up was 18.3 months (IQR, 12.7-22.5 months). For the primary outcome of event-free survival, the median length was not estimable (95% CI, 24.4 months-not estimable) in the toripalimab group compared with 15.1 months (95% CI, 10.6-21.9 months) in the placebo group (hazard ratio, 0.40 [95% CI, 0.28-0.57], P < .001). The major pathological response rate (another primary outcome) was 48.5% (95% CI, 41.4%-55.6%) in the toripalimab group compared with 8.4% (95% CI, 5.0%-13.1%) in the placebo group (between-group difference, 40.2% [95% CI, 32.2%-48.1%], P < .001). The pathological complete response rate (secondary outcome) was 24.8% (95% CI, 19.0%-31.3%) in the toripalimab group compared with 1.0% (95% CI, 0.1%-3.5%) in the placebo group (between-group difference, 23.7% [95% CI, 17.6%-29.8%]). The incidence of immune-related adverse events occurred more frequently in the toripalimab group. No unexpected treatment-related toxic effects were identified. The incidence of grade 3 or higher adverse events, fatal adverse events, and adverse events leading to discontinuation of treatment were comparable between the groups. Conclusions and Relevance: The addition of toripalimab to perioperative chemotherapy led to a significant improvement in event-free survival for patients with resectable stage III NSCLC and this treatment strategy had a manageable safety profile. Trial Registration: ClinicalTrials.gov Identifier: NCT04158440.

The applicability of the Second Revision of the International Staging System for patients with multiple myeloma receiving immunomodulatory drugs or proteasome inhibitor-based regimens as induction treatment: A real-world analysis.

The Second Revision of the International Staging System (R2-ISS) was recently introduced to improve risk stratification over that provided by the extensively applied standard revised International Staging System (R-ISS). In addition to the variables included in the R-ISS, the R2-ISS incorporates chromosome 1q gain/amplification and divides the patients into 4 groups with different survival outcomes, better stratifying patients within the R-ISS intermediate-risk. The new model was developed based on a great quantity of data from patients participating in uniform clinical trials and has not been validated in real-world clinical practice. Therefore, we retrospectively analyzed the prognostic value of the R2-ISS in 474 consecutive patients with multiple myeloma receiving immunomodulatory drugs or proteasome inhibitor-based regimens as their first-line treatment. According to the R2-ISS, 41 (8.6%), 76 (16%), 275 (58%), and 82 (17.3%) patients were identified as R2-ISS I, R2-ISS II, R2-ISS III, and R2-ISS IV, respectively. The median progression-free survival (PFS) was 48 (95% CI: 38-58), 35 (95% CI: 23-47), 24 (95% CI: 21-27), and 12 (95% CI: 7-17) months, and the estimated median overall survival (OS) was 110 (95% CI: 42-178), 88 (95% CI: 75-101), 50 (95% CI: 43-57), and 26 (95% CI: 19-33) months (p < 0.001) in the 4 groups, respectively. The R2-ISS could also classify groups with distinct survival among patients with renal impairment or classified as R-ISS II. Adjusted by age, sex, treatment approaches and transplantation status, the R2-ISS was an independent prognostic factor associated with OS with a hazard ratio of 7.055 (95% CI: 3.626-13.726) (p < 0.001) for R2-ISS IV versus R2-ISS I and 2.707 (95% CI: 1.436-5.103) (p = 0.002) for R2-ISS III versus R2-ISS I. In conclusion, our results suggest that the R2-ISS is a simple and robust risk stratification tool for patients with multiple myeloma treated with novel drugs and could be used in everyday clinical practice.

Mediating effect of gait speed on the relationship between ankle-brachial index and mild cognitive impairment in hemodialysis patients.

BACKGROUND: Patients undergoing hemodialysis are highly predisposed to arterial disease, poor physical performance, and cognitive impairment. However, the connection between them is not yet known. We aimed to investigate the mediating effect of physical performance on the relationship between arterial stiffness and mild cognitive impairment (MCI). METHODS: We conducted a multicenter cross-sectional study. The final analyzed hemodialysis patients comprised 616 subjects (men 391, women 225) from seven dialysis units in Shanghai, China. MCI was assessed by Mini-Mental State Examination (MMSE) and the Instrumental Activities of Daily Living (IADL) scale. Arterial function was measured by ankle-brachial index (ABI) and branchial-ankle pulse-wave velocity (baPWV). Physical function was assessed by the Short Physical Performance Battery (SPPB). Logistic regression and mediation model were used to analysis. RESULTS: The mean age of the final analysis sample (n = 616) was 59.0 ± 12.0 years. Hemodialysis patients with MCI were more likely to have lower ABI (p < 0.001) and higher baPWV (p < 0.01). After adjusting for covariates, lower ABI (abnormal ≤0.9 and borderline 0.91-0.99) were positively associated with MCI (OR = 4.43, 95% CI = 1.89-10.39; OR = 4.83, 95% CI = 1.61-14.46). SPPB total score and its components standing balance, gait speed score were negatively associated with MCI. In the mediational model, gait speed played a mediating role (indirect effect ab = -0.21; 95% CI = -0.58 to -0.03) in the association of ABI (≤0.9) and MMSE, while standing balance and chair stands did not. CONCLUSIONS: Lower gait speed mediates a positive association between ABI and MCI in hemodialysis patients. Suitable interventions for physical performance, especially gait speed, may reduce the risk of MCI in hemodialysis patients.

MTGNN: Multi-Task Graph Neural Network based few-shot learning for disease similarity measurement.

Similar diseases are usually caused by molecular origins or similar phenotypes. Confirming the relationship between diseases can help researchers gain a deep insight of the pathogenic mechanisms of emerging complex diseases, and improve the corresponding diagnoses and treatment. Therefore, similar diseases are considerably important in biology and pathology. However, the insufficient number of labelled similar disease pairs cannot support the optimal training of the models. In this paper, we propose a Multi-Task Graph Neural Network (MTGNN) framework to measure disease similarity by few-shot learning. To tackle the problem of insufficient number of labelled similar disease pairs, we design the multi-task optimization strategy to train the graph neural network for disease similarity task (lack of labelled training data) by introducing link prediction task (sufficient labelled training data). The similarity between diseases can then be obtained by measuring the distance between disease embeddings in high-dimensional space learning from the double tasks. The experiment results evaluate the performance of MTGNN and illustrate its advantages over previous methods on few labeled training dataset.

Comprehensive Characterization of the Genomic Landscape in Chinese Pulmonary Neuroendocrine Tumors Reveals Prognostic and Therapeutic Markers (CSWOG-1901).

BACKGROUND: Pulmonary neuroendocrine tumors (pNETs) include typical carcinoid (TC), atypical carcinoid (AC), large cell neuroendocrine carcinoma (LCNEC), and small cell lung carcinoma (SCLC). The optimal treatment strategy for each subtype remains elusive, partly due to the lack of comprehensive understanding of their molecular features. We aimed to explore differential genomic signatures in pNET subtypes and identify potential prognostic and therapeutic biomarkers. METHODS: We investigated genomic profiles of 57 LCNECs, 49 SCLCs, 18 TCs, and 24 ACs by sequencing tumor tissues with a 520-gene panel and explored the associations between genomic features and prognosis. RESULTS: Both LCNEC and SCLC displayed higher mutation rates for TP53, PRKDC, SPTA1, NOTCH1, NOTCH2, and PTPRD than TC and AC. Small cell lung carcinoma harbored more frequent co-alterations in TP53-RB1, alterations in PIK3CA and SOX2, and mutations in HIF-1, VEGF and Notch pathways. Large cell neuroendocrine carcinoma (12.7 mutations/Mb) and SCLC (11.9 mutations/Mb) showed higher tumor mutational burdens than TC (2.4 mutations/Mb) and AC (7.1 mutations/Mb). 26.3% of LCNECs and 20.8% of ACs harbored alterations in classical non-small cell lung cancer driver genes. The presence of alterations in the homologous recombination pathway predicted longer progression-free survival in advanced LCNEC patients with systemic therapy (P = .005) and longer overall survival (OS) in SCLC patients with resection (P = .011). The presence of alterations in VEGF (P = .048) and estrogen (P = .018) signaling pathways both correlated with better OS in patients with resected SCLC. CONCLUSION: We performed a comprehensive genomic investigation on 4 pNET subtypes in the Chinese population. Our data revealed distinctive genomic signatures in subtypes and provided new insights into the prognostic and therapeutic stratification of pNETs.

Differential photoregulation of the nuclear and cytoplasmic CRY1 in Arabidopsis.

Arabidopsis cryptochrome 1 (CRY1) is a blue light receptor distributed in the nucleus and cytoplasm. The nuclear CRY1, but not cytoplasmic CRY1, mediates blue light inhibition of hypocotyl elongation. However, the photobiochemical mechanisms distinguishing the CRY1 protein in the two subcellular compartments remains unclear. Here we show that the nuclear CRY1, but not the cytoplasmic CRY1, is regulated by phosphorylation, polyubiquitination and 26S proteasome-dependent proteolysis in response to blue light. The blue light-dependent CRY1 degradation is observed only under high fluences of blue light. The nuclear specificity and high fluence dependency of CRY1 explain why this photochemical regulatory mechanism of CRY1 was not observed previously and it further supports the hypothesis that CRY1 is a high light receptor regulating photomorphogenesis. We further show that the nuclear CRY1, but not cytoplasmic CRY1, undergoes blue light-dependent phosphorylation by photoregulatory protein kinase 1 (PPK1) followed by polyubiquitination by the E3 ubiquitin ligase Cul4COP1/SPAs , resulting in the blue light-dependent proteolysis. Both phosphorylation and ubiquitination of nuclear CRY1 are inhibited by blue-light inhibitor of cryptochromes 1 (BIC1), demonstrating the involvement of photo-oligomerization of the nuclear CRY1. These finding reveals a photochemical mechanism that differentially regulates the physiological activity of the CRY1 photoreceptor in distinct subcellular compartments.

Identification of the hub susceptibility genes and related common transcription factors in the skeletal muscle of Type 2 Diabetes Mellitus.

BACKGROUND: Type 2 diabetes mellitus (T2DM) and its related complications contribute to the high morbidity and mortality in worldwide. Skeletal muscle insulin resistance plays a critical role in the onset of T2DM due to the decreasing in the insulin-stimulated glucose uptake. T2DM is associated not only with the inherited factors but also with the noninherited factors. However, the susceptibility genes related with the two factors and the transcription factors (TF) regulating the susceptibility genes in skeletal muscle, which aggravate the development of T2DM were still ill-defined. METHODS: In the present study, the expression profiles by the array of GSE25462 were retrieved from the GEO database. GEO2R was performed to validate the susceptibility differentially expressed genes (SDEG) in skeletal muscle of T2DM. Gene Ontology (GO) analysis and The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted via The Database for Annotation, Visualization, and Integrated Discovery (DAVID). A Protein-Protein Interaction (PPI) network was performed with the STRING. RESULTS: With the performance of GEO2R, 229 SDEGs in skeletal muscle of T2DM were identified. The biological processes (BP) of SDEGs was enriched in the cellular response to UV-B most significantly. KEGG pathway analysis revealed that the SDEGs were most significantly enriched in glycosaminoglycan degradation. 5 hub susceptibility genes (GPR84, CALCB, GCG, PTGDR, GNG8) in the skeletal muscle of T2DM were identified. Eventually, the common transcription factors regulating the hub susceptibility genes were identified by means of the online tool PROMO. CONCLUSIONS: Five hub susceptibility genes (GPR84, CALCB, GCG, PTGDR, GNG8) in the skeletal muscle of T2DM and the common transcription factors were identified. The outputs would provide new clues on the novel potential targets and the therapeutic strategies for treating T2DM and its related diseases.

Temporal and chirp effects of laser pulses on the spectral line shape in sum-frequency generation vibrational spectroscopy.

Assignment and interpretation of the sum-frequency generation vibrational spectra (SFG-VS) depend on the ability to measure and understand the factors affecting the SFG-VS spectral line shape accurately and reliably. In the past, the formulation of the polarization selection rules for SFG-VS and the development of the sub-wavenumber high-resolution broadband SFG-VS (HR-BB-SFG-VS) have provided solutions for many of these needs. However, despite these advantages, HR-BB-SFG-VS have not been widely adopted. The majority of SFG measurements so far still relies on the picosecond (ps) scanning SFG-VS or the conventional broadband SFG-VS (BB-SFG-VS) with the spectral resolution around (mostly above) 10 cm-1, which also results in less ideal spectral line shape in the SFG spectra due to the temporal and chirp effects of the laser pulses used in experiment. In this study, the temporal and the chirp effects of laser pulses with different profiles in the SFG experiment on the measured SFG-VS spectral line shape are examined through spectral simulation. In addition, the experimental data of a classical model system, i.e., octadecyltrichlorosilane monolayer on glass, obtained from the ps scanning SFG-VS, the BB-SFG-VS, and the HR-BB-SFG-VS measurements are directly compared and examined. These results show that temporal and chirp effects are often significant in the conventional BB-SFG-VS, resulting in line shape distortions and peak position shifts besides spectral broadening. Such temporal and chirp effects are less significant in the ps scanning SFG-VS. For the HR-BB-SFG-VS, spectral broadening and temporal and chirp effects are insignificant, making HR-BB-SFG-VS the choice for accurate and reliable measurement and analysis of SFG-VS.

Identification of a TGF-ß signaling-related gene signature for prediction of immunotherapy and targeted therapy for lung adenocarcinoma.

BACKGROUND: Transforming growth factor (TGF)-ß signaling functions importantly in regulating tumor microenvironment (TME). This study developed a prognostic gene signature based on TGF-ß signaling-related genes for predicting clinical outcome of patients with lung adenocarcinoma (LUAD). METHODS: TGF-ß signaling-related genes came from The Molecular Signature Database (MSigDB). LUAD prognosis-related genes were screened from all the genes involved in TGF-ß signaling using least absolute shrinkage and selection operator (LASSO) Cox regression analysis and then used to establish a risk score model for LUAD. ESTIMATE and CIBERSORT analyzed infiltration of immune cells in TME. Immunotherapy response was analyzed by the TIDE algorithm. RESULTS: A LUAD prognostic 5-gene signature was developed based on 54 TGF-ß signaling-related genes. Prognosis of high-risk patients was significantly worse than low-risk patients. Both internal validation and external dataset validation confirmed a high precision of the risk model in predicting the clinical outcomes of LUAD patients. Multivariate Cox analysis demonstrated the model independence in OS prediction of LUAD. The risk model was significantly related to the infiltration of 9 kinds of immune cells, matrix, and immune components in TME. Low-risk patients tended to respond more actively to anti-PD-1 treatment, while high-risk patients were more sensitive to chemotherapy and targeted therapy. CONCLUSIONS: The 5-gene signature based on TGF-ß signaling-related genes showed potential for LUAD management.

Treatment of Pasteurella multocida infection with dressings containing honey and antibacterials: a case report.

Infections secondary to Pasteurella multocida frequently occur in patients who have been exposed to domestic pets. Human infections caused by Pasteurella multocida vary in severity, and clinical features include localised cellulitis, osteomyelitis, systemic bacteraemia, meningitis and pneumonia. No vaccine has been developed against Pasteurella multocida; it is treated with antibacterial agents and, in most cases, surgical intervention. This article discusses the authors' experience in treating a woman with severe cellulitis and osteomyelitis on her hand caused by Pasteurella multocida. She refused surgical intervention and was successfully treated with honey-containing dressings and antibiotics after failure to heal following conservative treatment using conventional wound dressings combined with antibiotics.

Mental Health and Psychological Responses During the Coronavirus Disease 2019 Epidemic: A Comparison Between Wuhan and Other Areas in China.

OBJECTIVE: This study aimed to compare the mental health and psychological responses in Wuhan, a severely affected area, and other areas of China during the coronavirus disease 2019 (COVID-19) epidemic. METHODS: This cross-sectional study was conducted on February 10-20, 2020. A set of online questionnaires was used to measure mental health and responses. A total of 1397 participants from Wuhan (age, 36.4 ± 10.7 years; male, 36.1%) and 2794 age- and sex-matched participants from other areas of China (age, 35.9 ± 9.9 years; male, 39.0%) were recruited. RESULTS: Compared with their counterparts, participants from Wuhan had a significantly higher prevalence of any mental health problems (46.6% versus 32.2%; adjusted odds ratio [OR] = 1.89, 95% confidence interval [CI] = 1.65-2.17), anxiety (15.2% versus 6.2%; adjusted OR = 2.65, 95% CI = 2.14-3.29), depression (18.3% versus 9.7%; adjusted OR = 2.11, 95% CI = 1.74-2.54), suicidal ideation (10.5% versus 7.1%; adjusted OR = 1.60, 95% CI = 1.28-2.02), and insomnia (38.6% versus 27.6%; adjusted OR = 1.70, 95% CI = 1.48-1.96). Participants from Wuhan had a slightly higher rate of help-seeking behavior (7.1% versus 4.2%; adjusted OR = 1.76, 95% CI = 1.12-2.77) but similar rate of treatment (3.5% versus 2.7%; adjusted OR = 1.23, 95% CI = 0.68-2.24) for mental problems than did their counterparts. In addition, compared with their counterparts, participants from Wuhan gave higher proportions of responses regarding "fearful" (52% versus 36%, p < .001), "discrimination against COVID-19 cases" (64% versus 58%, p = .006), "strictly comply with preventive behaviors" (98.7% versus 96%, p = .003), and "fewer living and medical supplies" (<2 weeks: 62% versus 57%, p = .015). CONCLUSIONS: The COVID-19 epidemic has raised enormous challenges regarding public mental health and psychological responses, especially in the highly affected Wuhan area. The present findings provide important information for developing appropriate strategies for the prevention and management of mental health problems during COVID-19 and other epidemics.

A novel graph attention adversarial network for predicting disease-related associations.

Identifying complex human diseases at molecular level is very helpful, especially in diseases diagnosis, therapy, prognosis and monitoring. Accumulating evidences demonstrated that RNAs are playing important roles in identifying various complex human diseases. However, the amount of verified disease-related RNAs is still little while many of their biological experiments are very time-consuming and labor-intensive. Therefore, researchers have instead been seeking to develop effective computational algorithms to predict associations between diseases and RNAs. In this paper, we propose a novel model called Graph Attention Adversarial Network (GAAN) for the potential disease-RNA association prediction. To our best knowledge, we are among the pioneers to integrate successfully both the state-of-the-art graph convolutional networks (GCNs) and attention mechanism in our model for the prediction of disease-RNA associations. Comparing to other disease-RNA association prediction methods, GAAN is novel in conducting the computations from the aspect of global structure of disease-RNA network with graph embedding while integrating features of local neighborhoods with the attention mechanism. Moreover, GAAN uses adversarial regularization to further discover feature representation distribution of the latent nodes in disease-RNA networks. GAAN also benefits from the efficiency of deep model for the computation of big associations networks. To evaluate the performance of GAAN, we conduct experiments on networks of diseases associating with two different RNAs: MicroRNAs (miRNAs) and Long non-coding RNAs (lncRNAs). Comparisons of GAAN with several popular baseline methods on disease-RNA networks show that our novel model outperforms others by a wide margin in predicting potential disease-RNAs associations.