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Drug combination therapy, involving the use of two or more drugs, has been widely employed to treat complex diseases such as cancer. It enhances therapeutic efficacy, reduces drug resistance, and minimizes side effects. However, traditional methods to identify effective drug combinations are time-consuming, costly, and less efficient than computational methods. Therefore, developing computational approaches to predict drug combinations has become increasingly important.In this paper, we developed the Random Walk with Restart for Drug Combination (RWRDC) model to predict effective drug combinations for cancer therapy. The RWRDC model offers a quantitative mathematical method for predicting the potential effective drug combinations. Cross-validation results indicate that the RWRDC model outperforms other predictive models, particularly in breast, colorectal, and lung cancer predictions across various performance metrics. We have theoretically proven the convergence of its algorithm and provided an explanation for the algorithm's rationality. A targeted case study on breast cancer further highlights the capability of RWRDC to identify effective drug combinations. These findings highlight our model as a novel and effective tool for discovering potential effective drug combinations, offering new possibilities in therapy. Additionally, the graph-based framework of RWRDC holds potential for predicting drug combinations in other complex diseases, expanding its utility in the medical field.

IntroductionCaring for people with Alzheimer’s disease (AD) is burdensome, especially when family members act as caregivers. This multicenter survey first aimed to investigate caregivers’ mental states as well as its influencing factors in caring for people with different severities of AD in China.MethodsPeople with AD and their caregivers from 30 provincial regions in mainland China were enrolled from October 2020 to December 2020 to be surveyed for caregivers’ mental states and living conditions, as well as caregivers’ attitudes toward treatment and caring. Logistic regression was used to explore the factors that influence the positive and negative states of caregivers who care for people with different stages of AD.ResultsA total of 1,966 valid questionnaires were analyzed (mild AD: 795, moderate AD: 521, severe AD: 650). A total of 73.6% of caregivers maintained normal states (mild group: 71.9%, moderate group: 73.9%, severe group: 75.2%; X2 = 2.023, p = 0.364), and the proportions of caregivers with positive and negative states were 26.3% (mild group: 38.4%, moderate group: 24.6%, severe group: 13.1%; X2 = 119.000, p

There is compelling evidence that synergistic drug combinations have become promising strategies for combating complex diseases, and they have evident predominance comparing to traditional one drug - one disease approaches. In this paper, we develop a computational method, namely SyFFM, that takes pharmacological data into consideration and applies field-aware factorization machines to analyze and predict potential synergistic drug combinations. Firstly, features of drug pairs are constructed based on associations between drugs and target, and enzymes, and indication areas. Then, the synergistic scores of drug combinations are obtained by implementing field-aware factorization machines on latent vector space of these features. Finally, synergistic combinations can be predicted by introducing a threshold. We applied SyFFM to predict pairwise synergistic combinations and three-drug synergistic combinations, and the performance is good in terms of cross-validation. Besides, more than 90% combinations of the top ranked predictions are proved by literature and the analysis of parameters in model shows that our method can help to investigate and explain synergistic mechanisms underlying combinatorial therapy. Keywords: Synergistic drug combinations, Factorization machines, Computational methods

Alzheimer's disease (AD) is a neurodegenerative disease that commonly affects the elderly; early diagnosis and timely treatment are very important to delay the course of the disease. In the past, most brain regions related to AD were identified based on imaging methods, and only some atrophic brain regions could be identified. In this work, the authors used mathematical models to identify the potential brain regions related to AD. In this study, 20 patients with AD and 13 healthy controls (non-AD) were recruited by the neurology outpatient department or the neurology ward of Peking University First Hospital from September 2017 to March 2019. First, diffusion tensor imaging (DTI) was used to construct the brain structural network. Next, the authors set a new local feature index 2hop-connectivity to measure the correlation between different regions. Compared with the traditional graph theory index, 2hop-connectivity exploits the higher-order information of the graph structure. And for this purpose, the authors proposed a novel algorithm called 2hopRWR to measure 2hop-connectivity. Then, a new index global feature score (GFS) based on a global feature was proposed by combing five local features, namely degree centrality, betweenness centrality, closeness centrality, the number of maximal cliques, and 2hop-connectivity, to judge which brain regions are related to AD. As a result, the top ten brain regions identified using the GFS scoring difference between the AD and the non-AD groups were associated to AD by literature verification. The results of the literature validation comparing GFS with the local features showed that GFS was superior to individual local features. Finally, the results of the canonical correlation analysis showed that the GFS was significantly correlated with the scores of the Mini-Mental State Examination (MMSE) scale and the Montreal Cognitive Assessment (MoCA) scale. Therefore, the authors believe the GFS can also be used as a new biomarker to assist in diagnosis and objective monitoring of disease progression. Besides, the method proposed in this paper can be used as a differential network analysis method for network analysis in other domains.

It has been demonstrated that long non-coding RNAs (lncRNAs) play important roles in a variety of biological processes associated with human diseases. However, the identification of lncRNA–disease associations by experimental methods is time-consuming and labor-intensive. Computational methods provide an effective strategy to predict more potential lncRNA–disease associations to some degree. Based on the hypothesis that phenotypically similar diseases are often associated with functionally similar lncRNAs and vice versa, we developed an improved diffusion model to predict potential lncRNA–disease associations (IDLDA). As a result, our model performed well in the global and local cross-validations, which indicated that IDLDA had a great performance in predicting novel associations. Case studies of colon cancer, breast cancer, and gastric cancer were also implemented, all lncRNAs which ranked top 10 in both databases were verified by databases and related literature. The results showed that IDLDA might play a key role in biomedical research.

Let , and be integers, and let be an -connected graph. If there exists a subgraph of such that the distance between and is at most for any , then we say that -dominates . A subset of is said to be -stable if the distance between each pair of distinct vertices in is at least . In this paper, we prove that if does not have a -stable set of order at least , then has an -dominating tree which has at most leaves.

Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations.

Abstract Background Women with pre-existing cardiac conditions who undergo assisted reproductive technologies (ART) are believed to be at a heightened risk of cardiovascular events during both the treatment and pregnancy phases. An unresolved question within this context pertains to whether the ART procedure itself constitutes a risk factor for individuals with bioprosthetic heart valves (BHV). Additionally, there is ongoing controversy regarding whether pregnancies expedite the process of structural valve degeneration (SVD) in BHV. The purpose of this study is to present the developmental process of BHV calcification, which is considered the primary cause of SVD, during a pregnancy resulting from in vitro fertilization and embryo transfer (IVF-ET), an ART modality, and to elucidate the underlying mechanisms. Case presentation At 7 + 3 weeks of gestation in a twin pregnancy resulting from IVF-ET, a 27-year-old woman with a bioprosthetic mitral valve manifesting severe mitral stenosis and moderate pulmonary arterial hypertension, was suspected of SVD. Despite undergoing fetal reduction, she experienced progressive calcification of the bioprosthetic valve, increasing pulmonary arterial pressure and ultimately deteriorated into heart failure. An elective cesarean section and redo valve replacement was subsequently administered to improve her cardiovascular condition. As a result, a healthy young boy was delivered and the dysfunctional BHV was replaced with a mechanical valve. She did not report any discomfort during the 3-month follow-up. Conclusion The progressive calcification of the BHV was observed during IVF pregnancy, indicating a potential connection between fertility therapy, pregnancy and calcification of BHV. Pregnant women with pre-implanted BHV should be treated with caution, as any medical interventions during ART and pregnancy can have a significant impact on both maternal and fetal outcomes. Thus, involving a multidisciplinary team in decision-making early on, starting from the treatment of the original heart disease, throughout the entire process of ART and pregnancy, is crucial.

Abstract The Nilaparvata muiri (Hemiptera: Delphacidae) is a sibling species of a destructive rice insect pest, the brown planthopper (BPH), Nilaparvata lugens. Here, we generated a high-quality chromosome-level genome assembly of N. muiri using a combination of the PacBio HiFi sequencing, Illumina short-read sequencing and Hi-C scaffolding technologies. The genome assembly (524.9 Mb) is anchored to 15 pseudochromosomes, with a scaffold N50 of 43.3 Mb and 99.1% BUSCO completeness. It contains 188.1 Mb repeat sequences and 13204 protein-coding genes. As a closely related species within the same genus as the significant pest, N. lugens, the chromosome-level genome assembly of N. muiri will provide important support for the better analysis of pathogenicity mechanisms of N. lugens based on comparative genomics.

Abstract Background Delirium, marked by acute cognitive decline, poses a life-threatening issue among older individuals, especially after cardiac surgery, with prevalence ranging from 15 to 80%. Postoperative delirium is linked to increased morbidity and mortality. Although clinical trials suggest preventability, there is limited research on intranasal insulin (INI) for cardiac surgery-related delirium. INI has shown promise in managing cognitive disorders. It rapidly elevates brain hormone levels, enhancing memory even in non-impaired individuals. While effective in preventing delirium in gastrointestinal surgery, its impact after cardiac surgery remains understudied, especially for middle-aged patients. Method This is a prospective randomized, double-blind, single-center controlled trial. A total of 76 eligible participants scheduled for elective on-pump cardiac surgery will be enrolled and randomly assigned in a 1:1 ratio to either receive Intranasally administered insulin (INI) or intranasally administered normal saline. The primary outcome of our study is the incidence of postoperative delirium (POD). Secondary outcomes include duration of ICU, postoperative hospital length of stay, all in-hospital mortality, the change in MMSE scores pre- and post-operation, and incidence of postoperative cognitive dysfunction at 1 month, 3 months, and 6 months after operation. Moreover, we will subjectively and objectively evaluate perioperative sleep quality to investigate the potential impact of nasal insulin on the development of delirium by influencing sleep regulation. Discussion Our study will aim to assess the impact of intranasal administration of insulin on the incidence of postoperative delirium in middle-aged patients undergoing on-pump elective cardiac surgery. If intranasal insulin proves to be more effective, it may be considered as a viable alternative for preventing postoperative delirium. Trial registration ChiCTR ChiCTR2400081444. Registered on March 1, 2024.

Abstract Background Compared to traditional thoracotomy, transapical transcatheter aortic valve implantation (TAVI) surgery offers reduced trauma and faster recovery, fostering the adoption of enhanced recovery after surgery (ERAS) protocols in cardiac surgery. Despite these advancements, postoperative pain management has received insufficient attention. The potential effects of multi-mode analgesia, including ultrasound-guided serratus anterior plane block (SAPB), on postoperative pain and early quality of recovery have not been widely studied, lacking comprehensive prospective evidence. Therefore, this study aims to investigate the impact of SAPB combined with general anesthesia on early recovery quality and analgesic efficacy in transapical TAVI patients. Methods This prospective, randomized controlled study will enroll 70 patients undergoing transapical TAVI, randomly allocated to either the SAPB group or the control group. The primary outcome, assessed using Quality of Recovery-40 (QOR-40) scale, focuses on the quality of recovery at 24 h and 48 h postoperatively. Secondary outcomes include the visual analog scale (VAS) pain scores at rest and during coughing at 6 h, 12 h, 24 h, and 48 h after surgery, frequency of patient-controlled analgesia (PCA) utilization at 24 h and 48 h, opioid consumption at 24 h and 48 h, time and frequency of rescue analgesia and severe pain at 24 h and 48 h, incidence of nausea and vomiting at 48 h after surgery, and dosage of antiemetic drugs. Discussion The purpose of our study is to evaluate the effects of ultrasound-guided SAPB combined with general anesthesia on postoperative early quality of recovery and analgesia in transapical TAVI patients. The results obtained may provide valuable insight for the implementation of multi-mode analgesia and enhanced ERAS in this specific patient population. Trial registration China Clinical Trial Register ChiCTR2300068584. Registered on 24 February 2023.

The purpose of this research is financing options for high-tech enterprises in China during their early-stage. This study develops a three-phase model to analyze the financing choices of Chinese high-tech firms between banking and venture capital while considering information asymmetry. The findings reveal that opaque information disclosure favors venture capital financing, while transparent disclosure leads to bank loans for equally qualified high-tech enterprises. Data analysis further confirms that both bank loans and venture capital are viable options for early-stage Chinese high-tech enterprises. These findings offer valuable guidance for high-tech firms and investors navigating China’s financial landscape. Moreover, policy management departments can leverage these insights to make informed decisions and facilitate the financing of high-tech enterprises. JEL Classification : C70, C23, D22, G24.

Abstract Background Postpartum depression (PPD) is a major public health problem worldwide. Previous studies have shown that postpartum negative life events and neuroticism are both important risk factors for PPD. However, few studies have considered the role of protective factors in the influence of postpartum negative life events and neuroticism on PPD. Based on the diathesis–stress model and Acceptance and Commitment Therapy (ACT), a moderated mediating model was established to examine the mediating role of neuroticism between postpartum negative life events and PPD, as well as the moderating role of psychological flexibility in this mediating effect. Methods A sample of 776 parturients from three different Grade A hospitals in China were assessed using the Edinburgh Postpartum Depression Scale, the Postpartum Negative Life Events Scale, the Neuroticism Subscale of the Big Five Personality Scale, and the Acceptance and Action Questionnaire– II. Results PPD, postpartum negative life events, neuroticism, and experiential avoidance were significantly positively correlated with one another. Neuroticism partially mediated the relationship between postpartum negative life events and PPD. In this mediation model, the direct path and the second half of the mediation path were moderated by psychological flexibility. Specifically, the links between postpartum negative life events and PPD, as well as between neuroticism and PPD, were stronger when psychological flexibility was low, but weaker when psychological flexibility was high. Conclusions The results show that psychological flexibility plays an important role in buffering the negative effects of postpartum negative life events and neuroticism on PPD. These findings provide implications for the prevention and intervention of PPD using an ACT approach.