Multiple machine learning methods aided virtual screening of Na(V)1.5 inhibitors

Na(v)1.5 sodium channels contribute to the generation of the rapid upstroke of the myocardial action potential and thereby play a central role in the excitability of myocardial cells. At present, the patch clamp method is the gold standard for ion channel inhibitor screening. However, this method has disadvantages such as high technical difficulty, high cost and low speed. In this study, novel machine learning models to screen chemical blockers were developed to overcome the above shortage. The data from the ChEMBL Database were employed to establish the machine learning models. Firstly, six molecular fingerprints together with five machine learning algorithms were used to develop 30 classification models to predict effective inhibitors. A validation and a test set were used to evaluate the performance of the models. Subsequently, the privileged substructures tightly associated with the inhibition of the Na(v)1.5 ion channel were extracted using the bioalerts Python package. In the validation set, the RF-Graph model performed best. Similarly, RF-Graph produced the best result in the test set in which the Prediction Accuracy (Q) was 0.9309 and Matthew's correlation coefficient was 0.8627, further indicating the model had high classification ability. The results of the privileged substructures indicated Sulfa structures and fragments with large Steric hindrance tend to block Na(v)1.5. In the unsupervised learning task of identifying sulfa drugs, MACCS and Graph fingerprints had good results. In summary, effective machine learning models have been constructed which help to screen potential inhibitors of the Na(v)1.5 ion channel and key privileged substructures with high affinity were also extracted.Peer reviewe

Influencing factors of different development stages of green food industry: a system dynamic model

The green food industry is important for China because it bears the additional expectation of promoting the rural economy Heilongjiang Province is a representative example, which is one of the main production bases of green food With this in mind, this paper takes Heilongjiang Province as an example to research the main influencing factor of the green food industry This article uses system dynamics methods to construct research models and uses mathematical models to calculate the industry lifecycle. Different from previous studies, this paper provides the life cycle of the industry and discusses the effects of influencing factors during the different periods The main conclusion includes: The stage before 2011 is the termination period, the stage from 2011 to 2019 is the growth period, and the stage after 2019 is the property period By the final time of the simulation, there are no signs of filtering; The enterprise scale is the main influencing factor that can make positive effects on the output value of the green food industry from the growth period, and others have no objective impact from beginning to end; Undesirably high level of financial investment will execute a negative effect for industrial development in the property period, at least in terms of output value

Iterative Energy-Efficient Stable Matching Approach for Context-Aware Resource Allocation in D2D Communications

Energy efficiency (EE) is critical to fully achieve the huge potentials of device-to-device (D2D) communications with limited battery capacity. In this paper, we consider the two-stage EE optimization problem, which consists of a joint spectrum and power allocation problem in the first stage, and a context-aware D2D peer selection problem in the second stage. We provide a general tractable framework for solving the combinatorial problem, which is NP-hard due to the binary and continuous optimization variables. In each stage, user equipments (UEs) from two finite and disjoint sets are matched in a two-sided stable way based on the mutual preferences. First, the preferences of UEs are defined as the maximum achievable EE. An iterative power allocation algorithm is proposed to optimize EE under a specific match, which is developed by exploiting nonlinear fractional programming and Lagrange dual decomposition. Second, we propose an iterative matching algorithm, which first produces a stable match based on the fixed preferences, and then dynamically updates the preferences according to the latest matching results in each iteration. Finally, the properties of the proposed algorithm, including stability, optimality, complexity, and scalability, are analyzed in detail. Numerical results validate the efficiency and superiority of the proposed algorithm under various simulation scenarios

One-stop stroke management platform reduces workflow times in patients receiving mechanical thrombectomy

Background and purposeClinical outcome in patients who received thrombectomy treatment is time-dependent. The purpose of this study was to evaluate the efficacy of the one-stop stroke management (OSSM) platform in reducing in-hospital workflow times in patients receiving thrombectomy compared with the traditional model.MethodsThe data of patients who received thrombectomy treatment through the OSSM platform and traditional protocol transshipment pathway were retrospectively analyzed and compared. The treatment-related time interval and the clinical outcome of the two groups were also assessed and compared. The primary efficacy endpoint was the time from door to groin puncture (DPT).ResultsThere were 196 patients in the OSSM group and 210 patients in the control group, in which they were treated by the traditional approach. The mean DPT was significantly shorter in the OSSM group than in the control group (76 vs. 122 min; P < 0.001). The percentages of good clinical outcomes at the 90-day time point of the two groups were comparable (P = 0.110). A total of 121 patients in the OSSM group and 124 patients in the control group arrived at the hospital within 360 min from symptom onset. The mean DPT and time from symptom onset to recanalization (ORT) were significantly shorter in the OSSM group than in the control group. Finally, a higher rate of good functional outcomes was achieved in the OSSM group than in the control group (53.71 vs. 40.32%; P = 0.036).ConclusionCompared to the traditional transfer model, the OSSM transfer model significantly reduced the in-hospital delay in patients with acute stroke receiving thrombectomy treatment. This novel model significantly improved the clinical outcomes of patients presenting within the first 6 h after symptom onset

Reduced NOV expression correlates with disease progression in colorectal cancer and is associated with survival, invasion and chemoresistance of cancer cells

Aberrant expression of nephroblastoma overexpressed (NOV) has been evident in certain malignancies. In the current study, we aim to investigate the role played by NOV in colorectal cancer (CRC). NOV expression was determined in a cohort of 359 CRC tissues and 174 normal colorectal tissues. Its impact on CRC cells was investigated using in vitro NOV knockdown and overexpression models. NOV transcripts were reduced in the CRC tumours compared with the paired adjacent normal colorectal tissues (p < 0.01) and was associated with distant metastases. NOV knockdown resulted in increased cell proliferation and invasion of RKO cells, whilst an opposite effect was seen in the HT115 NOV over expressing cells. A positive association between Caspase-3/-8 and NOV was seen in NOV knockdown and overexpression cell lines which contributed to the survival of serum deprived CRC cells. Further investigation showed that NOV regulated proliferation, survival and invasion through the JNK pathway. NOV knockdown in RKO cells reduced the responsiveness to 5-Fluorouracil treatment, whilst overexpression in HT115 cells exhibited a contrasting effect. Taken together, NOV is reduced in CRC tumours and this is associated with disease progression. NOV inhibits the proliferation and invasion of CRC cells in vitro. Inhibition of proliferation is mediated by a regulation of Caspase-3/-8, via the JNK pathway, which has potential for predicting and preventing chemoresistance

Aspergillus Myosin-V Supports Polarized Growth in the Absence of Microtubule-Based Transport

In the filamentous fungus Aspergillus nidulans, both microtubules and actin filaments are important for polarized growth at the hyphal tip. Less clear is how different microtubule-based and actin-based motors work together to support this growth. Here we examined the role of myosin-V (MYOV) in hyphal growth. MYOV-depleted cells form elongated hyphae, but the rate of hyphal elongation is significantly reduced. In addition, although wild type cells without microtubules still undergo polarized growth, microtubule disassembly abolishes polarized growth in MYOV-depleted cells. Thus, MYOV is essential for polarized growth in the absence of microtubules. Moreover, while a triple kinesin null mutant lacking kinesin-1 (KINA) and two kinesin-3s (UNCA and UNCB) undergoes hyphal elongation and forms a colony, depleting MYOV in this triple mutant results in lethality due to a severe defect in polarized growth. These results argue that MYOV, through its ability to transport secretory cargo, can support a significant amount of polarized hyphal tip growth in the absence of any microtubule-based transport. Finally, our genetic analyses also indicate that KINA (kinesin-1) rather than UNCA (kinesin-3) is the major kinesin motor that supports polarized growth in the absence of MYOV

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake