Performance of a novel up-flow electrocatalytic hydrolysis acidification reactor (UEHAR) coupled with anoxic/oxic system for treating coking wastewater.

In this study, the performance of a novel up-flow electrocatalytic hydrolytic acidification reactor (UEHAR) and anoxic/oxic (ANO2/O2) combined system (S2) was compared with that of a traditional anaerobic/anoxic/oxic (ANA/ANO1/O1) system (S1) for treating coking wastewater at different hydraulic retention time (HRT). The effluent non-compliance rates of chemical oxygen demand (COD) of S2 were 45 %, 35 %, 25 % and 55 % lower than S1 with HRT of 94, 76, 65 and 54 h. The removal efficiency of benzene, toluene, ethylbenzene and xylene (BTEX) in S2 was 10.6 ± 2.4 % higher than that in S1. The effluent concentration of volatile phenolic compounds (VPs) in S2 was lower than 0.3 mg/L. The dehydrogenase activity (DHA) and adenosine triphosphate (ATP) of O2 were enhanced by 67.2 ± 26.3 % and 40.6 ± 14.2 % compared with O1, respectively. Moreover, COD was used to reflect the mineralization index of organic matter, and the positive correlation between COD removal rate and microbial activity, VPs, and BTEX was determined. These results indicated that S2 had extraordinary microbial activity, stable pollutant removal ability, and transcendental effluent compliance rate.

Risk factors for intraprocedural hypoxemia in patients with acute cerebral ischemia treated with vascular intervention and its impact on prognosis: A retrospective cohort study.

BACKGROUND: Acute cerebral infarction (ACI) is one of the most common ischemic cerebrovascular diseases in neurology, with high morbidity, mortality, and disability. Early thrombolytic treatment of ACI has significant efficacy, but intraprocedural complications of hypoxemia can significantly reduce the efficacy. This study aims to analyze the risk factors for intraprocedural hypoxemia in patients with ACI, so as to take effective measures in advance to reduce the likelihood of adverse patient outcomes. METHODS: We retrospectively analyzed a total of 238 patients with ACI treated with vascular interventions from May 2017 to May 2022. To assess and collate the patients' characteristics, factors associated with the development of intraprocedural hypoxemia. The independent risk factors for the development of intraprocedural hypoxemia were analyzed by binary logistic regression. RESULTS: A total of 238 patients were included in this study. Of these, intraprocedural hypoxemia occurred in 89 (37.4%). The results showed that old age (odds ratio [OR] = 2.666, P = 0.009), obesity (OR = 3.029, P = 0.003), smoking history (OR = 2.655, P = 0.010), preoperative oxygen saturation (SpO2) (OR = 0.001, P = 0.042), preoperative C-reactive protein (OR = 1.216, P = 0.002), and time from puncture to vascular recanalization (OR = 1.135, P = 0.000) were independent risk factors for intraprocedural hypoxemia in patients. The prognosis of the patients was assessed according to the modified Rankin scale, and the prognosis of the nonhypoxemia group was significantly better than that of the hypoxemia group. Regression analysis showed that intraprocedural hypoxemia (OR = 0.360, P = 0.001), postoperative lower extremity vein thrombosis (OR = 0.187, P = 0.018), hydrocephalus (OR = 0.069, P = 0.015), intracranial hemorrhage (OR = 0.116, P = 0.002), and reocclusion (OR = 0.217, P = 0.036) were independent risk factors for poor prognosis. CONCLUSIONS: Currently, intravascular hypoxemia in patients with ACI has a serious impact on prognosis. Clinical work should attach great importance to the clinical characteristics of patients, identify relevant risk factors, and aggressively take personalized therapeutic actions to improve patients' prognosis.

Association between coronary artery disease and clinical outcome in cancer patients: A propensity score matching analysis.

Objective: The aim of this study was to evaluate the prognostic value of coronary artery disease (CAD) detected by coronary computed tomography angiography (CTA) to predict the risk of all-cause mortality in cancer patients in a propensity score matching (PSM) analysis. Methods: A total of 331 patients who previously had cancer and underwent coronary CTA from January 2015 to December 2019 were included. Multivariate Cox proportional hazards regression analysis and propensity-score matching analysis were performed. The primary endpoint was all-cause of mortality. Results: In total, 125 with CAD and 206 with no CAD during a median follow-up of 3.3 years were included in this study. After PSM, age (HR, 1.040; 95%CI, 1.001-1.081; p = 0.014) and CAD (HR, 2.164; 95%CI, 1.057-4.430; p = 0.035) remained significant factors for all-cause mortality. Conclusion: CAD evaluated by coronary CTA was found to be at higher risk for all-cause mortality in cancer patients. Due to the retrospective design and lack of information on some medical history and treatments, especially immune checkpoint inhibitors, a large-scale prospective study is needed to further determine the prognostic value of coronary CTA in cancer patients.

Reconfigurable Ag/HfO2/NiO/Pt Memristors with Stable Synchronous Synaptic and Neuronal Functions for Renewable Homogeneous Neuromorphic Computing System.

Artificial synapses and bionic neurons offer great potential in highly efficient computing paradigms. However, complex requirements for specific electronic devices in neuromorphic computing have made memristors face the challenge of process simplification and universality. Herein, reconfigurable Ag/HfO2/NiO/Pt memristors are designed for feasible switching between volatile and nonvolatile modes by compliance current controlled Ag filaments, which enables stable and reconfigurable synaptic and neuronal functions. A neuromorphic computing system effectively replicates the biological synaptic weight alteration and continuously accomplishes excitation and reset of artificial neurons, which consist of bionic synapses and artificial neurons based on isotype Ag/HfO2/NiO/Pt memristors. This reconfigurable electrical performance of the Ag/HfO2/NiO/Pt memristors takes advantage of simplified hardware design and delivers integrated circuits with high density, which exhibits great potency for future neural networks.

Bi-2-(2-phenethyl)chromone derivatives from agarwood of Aquilaria filaria in the Philippines.

Twelve undescribed 2-(2-phenethyl)chromone dimers (1-12) were isolated from EtOAc extract of agarwood originating from Aquilaria filaria in the Philippines, guided by a UHPLC-MS analysis. Their structures were elucidated by 1D NMR, 2D NMR, and HR-ESI-MS spectra. The absolute configuration of 2-(2-phenylethyl)chromone dimers was determined by single-crystal X-ray diffraction analysis and comparison of the experimental and calculated ECD spectra. Compounds 1, 2, 5 and 9-12 exhibited potent to moderate anti-inflammatory activity with IC50 values in the range of 22.43 ± 0.86 to 53.88 ± 4.06 µM.

Quasi van der Waals Epitaxy of Single Crystalline GaN on Amorphous SiO2/Si(100) for Monolithic Optoelectronic Integration.

The realization of high quality (0001) GaN on Si(100) is paramount importance for the monolithic integration of Si-based integrated circuits and GaN-enabled optoelectronic devices. Nevertheless, thorny issues including large thermal mismatch and distinct crystal symmetries typically bring about uncontrollable polycrystalline GaN formation with considerable surface roughness on standard Si(100). Here a breakthrough of high-quality single-crystalline GaN film on polycrystalline SiO2/Si(100) is presented by quasi van der Waals epitaxy and fabricate the monolithically integrated photonic chips. The in-plane orientation of epilayer is aligned throughout a slip and rotation of high density AlN nuclei due to weak interfacial forces, while the out-of-plane orientation of GaN can be guided by multi-step growth on transfer-free graphene. For the first time, the monolithic integration of light-emitting diode (LED) and photodetector (PD) devices are accomplished on CMOS-compatible SiO2/Si(100). Remarkably, the self-powered PD affords a rapid response below 250 µs under adjacent LED radiation, demonstrating the responsivity and detectivity of 2.01 × 105 A/W and 4.64 × 1013 Jones, respectively. This work breaks a bottleneck of synthesizing large area single-crystal GaN on Si(100), which is anticipated to motivate the disruptive developments in Si-integrated optoelectronic devices.

Association of HBV serological markers with host antiviral immune response relevant hepatic inflammatory damage in chronic HBV infection.

The natural progression of chronic hepatitis B virus (HBV) infection is dynamic, but the longitudinal landscape of HBV serological markers with host antiviral immune response relevant hepatic inflammatory damage remains undetermined. To this issue, we studied the association of HBV serological markers with the severity of hepatic inflammatory damage and enumerated HBV-specific T cells using the cultured enzyme-linked immune absorbent spot (ELISpot). Five hundred and twenty-four treatment-naïve chronic HBV infection patients were enrolled. The Spearman correlation analysis revealed that in hepatitis B e antigen (HBeAg)-positive patients, all HBV virologic indicators negatively correlated with liver inflammatory damage and fibrosis (p < 0.01). Stronger correlations were accessed in the subgroup of HBeAg-positive patients with HBV DNA > 2 × 106 IU/mL (p < 0.01), whereas negative correlations disappeared in patients with HBV DNA ≤ 2 × 106 IU/mL. Surprisingly, in HBeAg-negative patients, the HBV DNA level was positively correlated with the hepatic inflammatory damage (p < 0.01). The relationship between type Ⅱ interferon genes expression and HBV DNA levels also revealed a direct shift from the initial negative to positive in HBeAg-positive patients with HBV DNA declined below 2 × 106 IU/mL. The number of HBV-specific T cells were identified by interferon γ ELISpot assays and showed a significant increase from HBeAg-positive to HBeAg-negative group. The host's anti-HBV immunity remains effective in HBeAg-positive patients with HBV DNA levels exceeding 2 × 106 IU/mL, as it efficiently eliminates infected hepatocytes and inhibits HBV replication. However, albeit the increasing number of HBV-specific T cells, the host antiviral immune response shifts towards dysfunctional when the HBV DNA load drops below this threshold, which causes more pathological damage and disease progression.

Efficient Spiking Neural Networks with Biologically Similar Lithium-Ion Memristor Neurons.

Benefiting from the brain-inspired event-driven feature and asynchronous sparse coding approach, spiking neural networks (SNNs) are becoming a potentially energy-efficient replacement for conventional artificial neural networks. However, neuromorphic devices used to construct SNNs persistently result in considerable energy consumption owing to the absence of sufficient biological parallels. Drawing inspiration from the transport nature of Na+ and K+ in synapses, here, a Li-based memristor (LixAlOy) was proposed to emulate the biological synapse, leveraging the similarity of Li as a homologous main group element to Na and K. The Li-based memristor exhibits ∼8 ns ultrafast operating speed, 1.91 and 0.72 linearity conductance modulation, and reproducible switching behavior, enabled by lithium vacancies forming a conductive filament mechanism. Moreover, these memristors are capable of simulating fundamental behaviors of a biological synapse, including long-term potentiation and long-term depression behaviors. Most importantly, a threshold-tunable leaky integrate-and-fire (TT-LIF) neuron is built using LixAlOy memristors, successfully integrating synaptic signals from both temporal and spatial levels and achieving an optimal threshold of SNNs. A computationally efficient TT-LIF-based SNN algorithm is also implemented for image recognition schemes, featuring a high recognition rate of 90.1% and an ultralow firing rate of 0.335%, which is 4 times lower than those of other memristor-based SNNs. Our studies reveal the ion dynamics mechanism of the LixAlOy memristor and confirm its potential in rapid switching and the construction of SNNs.

Analysis of Microbial Diversity and Community Structure of Rhizosphere Soil of Three Astragalus Species Grown in Special High-Cold Environment of Northwestern Yunnan, China.

Astragalus is a medicinal plant with obvious rhizosphere effects. At present, there are many Astragalus plants with high application value but low recognition and resource reserves in the northwestern area of Yunnan province, China. In this study, metagenomics was used to analyze the microbial diversity and community structure of rhizosphere soil of A. forrestii, A. acaulis, and A. ernestii plants grown in a special high-cold environment of northwestern Yunnan, China, at different altitudes ranging from 3225 to 4353 m. These microbes were taxonomically annotated to obtain 24 phyla and 501 genera for A. forrestii, 30 phyla and 504 genera for A. acaulis, as well as 39 phyla and 533 genera for A. ernestii. Overall, the dominant bacterial phyla included Proteobacteria, Actinobacteria, and Acidobacteria, while the dominant fungal ones were Ascomycota and Basidiomycota. At the genus level, Bradyrhizobium, Afipia, and Paraburkholderia were the most prevalent bacteria, and Hyaloscypha, Pseudogymnoascus, and Russula were the dominant fungal genera. Some of them are considered biocontrol microbes that could sustain the growth and health of host Astragalus plants. Redundancy analysis revealed that pH, TN, and SOM had a significant impact on the microbial community structures (p < 0.05). Finally, triterpene, flavonoid, polysaccharide, and amino acid metabolisms accounted for a high proportion of the enriched KEGG pathways, which possibly contributed to the synthesis of bioactive constituents in the Astragalus plants.

Gut microbiota and cognitive performance: A bidirectional two-sample Mendelian randomization.

PURPOSE: Previous studies have suggested a potential association between gut microbiota and neurological and psychiatric disorders. However, the causal relationship between gut microbiota and cognitive performance remains uncertain. METHODS: A two-sample Mendelian randomization (MR) study used SNPs linked to gut microbiota (n = 18,340) and cognitive performance (n = 257,841) from recent GWAS data. Inverse-variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode were employed. Heterogeneity was assessed via Cochran's Q test for IVW. Results were shown with funnel plots. Outliers were detected through leave-one-out method. MR-PRESSO and MR-Egger intercept tests were conducted to address horizontal pleiotropy influence. LIMITATIONS: Limited to European populations, generic level, and potential confounding factors. RESULTS: IVW analysis revealed detrimental effects on cognitive perfmance associated with the presence of genus Blautia (P = 0.013, 0.966[0.940-0.993]), Catenibacterium (P = 0.035, 0.977[0.956-0.998]), Oxalobacter (P = 0.043, 0.979[0.960-0.999]). Roseburia (P < 0.001, 0.935[0.906-0.965]), in particular, remained strongly negatively associated with cognitive performance after Bonferroni correction. Conversely, families including Bacteroidaceae (P = 0.043, 1.040[1.001-1.081]), Rikenellaceae (P = 0.047, 1.026[1.000-1.053]), along with genera including Paraprevotella (P = 0.044, 1.020[1.001-1.039]), Ruminococcus torques group (P = 0.016, 1.062[1.011-1.115]), Bacteroides (P = 0.043, 1.040[1.001-1.081]), Dialister (P = 0.027, 1.039[1.004-1.074]), Paraprevotella (P = 0.044, 1.020[1.001-1.039]) and Ruminococcaceae UCG003 (P = 0.007, 1.040[1.011-1.070]) had a protective effect on cognitive performance. CONCLUSIONS: Our results suggest that interventions targeting specific gut microbiota may offer a promising avenue for improving cognitive function in diseased populations. The practical application of these findings has the potential to enhance cognitive performance, thereby improving overall quality of life.

Methyl jasmonate regulation of pectin polysaccharides in Cosmos bipinnatus roots: A mechanistic insight into alleviating cadmium toxicity.

Methyl jasmonate (MeJA), a crucial phytohormone, which plays an important role in resistance to Cadmium (Cd) stress. The cell wall (CW) of root system is the main location of Cd and plays a key role in resistance to Cd toxicity. However, the mechanism effect of MeJA on the CW composition and Cd accumulation remain unclear. In this study, the contribution of MeJA in regulating CW structure, pectin composition and Cd accumulation was investigated in Cosmos bipinnatus. Phenotypic results affirm MeJA's significant role in reducing Cd-induced toxicity in C. bipinnatus. Notably, MeJA exerts a dual impact, reducing Cd uptake in roots while increasing Cd accumulation in the CW, particularly bound to pectin. The molecular structure of pectin, mainly uronic acid (UA), correlates positively with Cd content, consistent in HC1 and cellulose, emphasizing UA as pivotal for Cd binding. Furthermore, MeJA modulates pectin methylesterase (PME) activity under Cd stress, influencing pectin's molecular structure and homogalacturonan (HG) content affecting Cd-binding capacity. Chelate-soluble pectin (CSP) within soluble pectins accumulates a substantial Cd proportion, with MeJA regulating both UA content and the minor component 3-deoxy-oct-2-ulosonic acid (Kdo) in CSP. The study delves into the intricate regulation of pectin monosaccharide composition under Cd stress, revealing insights into the CW's physical defense and Cd binding. In summary, this research provides novel insights into MeJA-specific mechanisms alleviating Cd toxicity in C. bipinnatus, shedding light on complex interactions between MeJA, and Cd accumulation in CW pectin polysaccharide.

Adult-onset combined methylmalonic acidemia and hyperhomocysteinemia, cblC type with aortic dissection and acute kidney injury: a case report.

BACKGROUND: Combined methylmalonic acidemia (MMA) and hyperhomocysteinemia, cobalamin C (cblC) type, also named cblC deficiency is a rare autosomal recessive genetic metabolic disease. It progressively causes neurological, hematologic, renal and other system dysfunction. The clinical manifestations are relatively different due to the onset time of disease. CASE PRESENTATION: This report describes a rare case of a 26 year old man with cblC deficiency who developed life-threatening aortic dissection and acute kidney injury (AKI) and showed neuropsychiatric symptoms with elevated serum homocysteine and methylmalonic aciduria. After emergent operation and intramuscular cobalamin supplementation therapy, the male recovered from aortic dissection, neurological disorder and AKI. Finally, two previously published compound heterozygous variants, c.482G > A (p.R161Q) and c.658_660del (p.K220del) in the MMACHC gene were detected in this patient and he was confirmed to have cblC deficiency. CONCLUSIONS: Poor cognizance of presenting symptoms and biochemical features of adult onset cblC disease may cause delayed diagnosis and management. This case is the first to depict a case of adult-onset cblC deficiency with aortic dissection. This clinical finding may contribute to the diagnosis of cblC deficiency.

Dibenzofurans from nature: Biosynthesis, structural diversity, sources, and bioactivities.

Dibenzofurans are a small class of natural products with versatile biological activities that used to be thought to come mainly from lichens and ascomycetes. In fact, they are also distributed widely in higher plants, especially in the families Rosaceae and Myrtaceae. Dibenzofurans and derivatives from lichens and ascomycetes have been well reviewed, but dibenzofurans from all biological sources in nature have not been reviewed. In this review, dibenzofurans from all natural sources have been comprehensively reviewed, and a total of 211 dibenzofurans isolated and identified from organisms between 1843 and March 2023 are categorized and discussed, including their biosynthesis, structural diversity, sources, and bioactivities.

Association of sleep quality on the night of operative day with postoperative delirium in elderly patients: A prospective cohort study.

BACKGROUND: Sleep disturbances in the peri-operative period have been associated with adverse outcomes, including postoperative delirium (POD). However, research on sleep quality during the immediate postoperative period is limited. OBJECTIVES: This study aimed to investigate the association between sleep quality on the night of the operative day assessed using the Sleep Quality Numeric Rating Scale (SQ-NRS), and the incidence of POD in a large cohort of surgical patients. DESIGN: A prospective cohort study. SETTING: A tertiary hospital in China. PATIENTS: This study enrolled patients aged 65 years or older undergoing elective surgery under general anaesthesia. The participants were categorised into the sleep disturbance and no sleep disturbance groups according to their operative night SQ-NRS. MAIN OUTCOME MEASURES: The primary outcome was delirium incidence, whereas the secondary outcomes included acute kidney injury, stroke, pulmonary infection, cardiovascular complications and all-cause mortality within 1 year postoperatively. RESULTS: In total, 3072 patients were included in the analysis of this study. Among them, 791 (25.72%) experienced sleep disturbances on the night of operative day. Patients in the sleep disturbance group had a significantly higher risk of developing POD (adjusted OR 1.43, 95% CI 1.11 to 1.82, P  = 0.005). Subgroup analysis revealed that age 65-75 years; male sex; ASA III and IV; haemoglobin more than 12 g l -1 ; intra-operative hypotension; surgical duration more than 120 min; and education 9 years or less were significantly associated with POD. No interaction was observed between the subgroups. No significant differences were observed in the secondary outcomes, such as acute kidney injury, stroke, pulmonary infection, cardiovascular complications and all-cause mortality within 1 year postoperatively. CONCLUSIONS: The poor subjective sleep quality on the night of operative day was independently associated with increased POD risk, especially in certain subpopulations. Optimising peri-operative sleep may reduce POD. Further research should investigate potential mechanisms and causal relationships. TRIAL REGISTRY: chictr.org.cn: ChiCTR1900028545.

MMDTA: A Multimodal Deep Model for Drug-Target Affinity with a Hybrid Fusion Strategy.

The prediction of the drug-target affinity (DTA) plays an important role in evaluating molecular druggability. Although deep learning-based models for DTA prediction have been extensively attempted, there are rare reports on multimodal models that leverage various fusion strategies to exploit heterogeneous information from multiple different modalities of drugs and targets. In this study, we proposed a multimodal deep model named MMDTA, which integrated the heterogeneous information from various modalities of drugs and targets using a hybrid fusion strategy to enhance DTA prediction. To achieve this, MMDTA first employed convolutional neural networks (CNNs) and graph convolutional networks (GCNs) to extract diverse heterogeneous information from the sequences and structures of drugs and targets. It then utilized a hybrid fusion strategy to combine and complement the extracted heterogeneous information, resulting in the fused modal information for predicting drug-target affinity through the fully connected (FC) layers. Experimental results demonstrated that MMDTA outperformed the competitive state-of-the-art deep learning models on the widely used benchmark data sets, particularly with a significantly improved key evaluation metric, Root Mean Square Error (RMSE). Furthermore, MMDTA exhibited excellent generalization and practical application performance on multiple different data sets. These findings highlighted MMDTA's accuracy and reliability in predicting the drug-target binding affinity. For researchers interested in the source data and code, they are accessible at http://github.com/dldxzx/MMDTA.

Risk factors prediction of 6-month mortality after noncardiac surgery of older patients in China: a multicentre retrospective cohort study.

BACKGROUND: Identifying the risk factors associated with perioperative mortality is crucial, particularly in older patients. Predicting 6-month mortality risk in older patients based on large datasets can assist patients and surgeons in perioperative clinical decision-making. This study aimed to develop a risk prediction model of mortality within 6 months after noncardiac surgery using the clinical data from 11 894 older patients in China. MATERIALS AND METHODS: A multicentre, retrospective cohort study was conducted in 20 tertiary hospitals. The authors retrospectively included 11 894 patients (aged ≥65 years) who underwent noncardiac surgery between April 2020 and April 2022. The least absolute shrinkage and selection operator model based on linear regression was used to analyse and select risk factors, and various machine learning methods were used to build predictive models of 6-month mortality. RESULTS: The authors predicted 12 preoperative risk factors associated with 6-month mortality in older patients after noncardiac surgery. Including laboratory-associated risk factors such as mononuclear cell ratio and total blood cholesterol level, etc. Also including medical history associated risk factors such as stroke, history of chronic diseases, etc. By using a random forest model, the authors constructed a predictive model with a satisfactory accuracy (area under the receiver operating characteristic curve=0.97). CONCLUSION: The authors identified 12 preoperative risk factors associated with 6-month mortality in noncardiac surgery older patients. These preoperative risk factors may provide evidence for a comprehensive preoperative anaesthesia assessment as well as necessary information for clinical decision-making by anaesthesiologists.

GHGPR-PPIS: A graph convolutional network for identifying protein-protein interaction site using heat kernel with Generalized PageRank techniques and edge self-attention feature processing block.

Accurately pinpointing protein-protein interaction site (PPIS) on the molecular level is of utmost significance for annotating protein function and comprehending the mechanisms underpinning various diseases. While numerous computational methods for predicting PPIS have emerged, they have indeed mitigated the labor and time constraints associated with traditional experimental methods. However, the predictive accuracy of these methods has yet to reach the desired threshold. In this context, we proposed a groundbreaking graph-based computational model called GHGPR-PPIS. This innovative model leveraged a graph convolutional network using heat kernel (GraphHeat) in conjunction with Generalized PageRank techniques (GHGPR) to predict PPIS. Additionally, building upon the GHGPR framework, we devised an edge self-attention feature processing block, further augmenting the performance of the model. Experimental findings conclusively demonstrated that GHGPR-PPIS surpassed all competing state-of-the-art models when evaluated on the benchmark test set. Impressively, on two distinct independent test sets and a specific protein chain, GHGPR-PPIS consistently demonstrated superior generalization performance and practical applicability compared to the comparative model, AGAT-PPIS. Lastly, leveraging the t-SNE dimensionality reduction algorithm and clustering visualization technique, we delved into an interpretability analysis of the effectiveness of GHGPR-PPIS by meticulously comparing the outputs from different stages of the model.

Preoperative prognostic nutritional index predicts postoperative delirium in aged patients after surgery: A matched cohort study.

OBJECTIVE: Prognostic nutritional index (PNI) is an indicator to evaluate the nutritional immune status of patients. This study aimed to assess whether preoperative PNI could predict the occurrence of postoperative POD in aged patients undergoing non-neurosurgery and non-cardiac surgery. METHOD: The aged patients undergoing non-neurosurgery and non-cardiac surgery between January 2014 and August 2019 were included in the retrospective cohort study. The correlation between POD and PNI was investigated by univariate and multivariable logistic regression analysis, propensity score matching (PSM), inverse probability of treatment weighting (IPTW), and subgroup analysis. RESULTS: In the cohort (n = 29,814), the cutoff value of PNI was 46.01 determined by the receiver operating characteristic (ROC) curve. In univariate and three multivariable regression analysis, the ORs of PNI ≤ 46.01 was 2.573(95% CI:2.261-2.929, P < 0.001),1.802 (95% CI:1.567-2.071, P < 0.001),1.463(95% CI:1.246-1.718, P < 0.001),1.370(95% CI:1.165-1.611, P < 0.001). In the PSM model and IPTW model, the ORs of PNI ≤ 46.01 were 1.424(95% CI:1.172-1.734, P < 0.001) and 1.356(95% CI:1.223-1.505, P < 0.001). CONCLUSION: The PNI was found to have a predictive value for POD in patients undergoing non-neurosurgery and non-cardiac surgery. Improving preoperative nutritional status may be beneficial in preventing POD for aged patients.

Potential Plausible Role of Stem Cell for Treating Depressive Disorder: a Retrospective Review.

Depression poses a significant threat to global physical and mental health, impacting around 3.8% of the population with a rising incidence. Current treatment options primarily involve medication and psychological support, yet their effectiveness remains limited, contributing to high relapse rates. There is an urgent need for innovative and more efficacious treatment modalities. Stem cell therapy, a promising avenue in regenerative medicine for a spectrum of neurodegenerative conditions, has recently garnered attention for its potential application in depression. While much of this work remains preclinical, it has demonstrated considerable promise. Identified mechanisms underlying the antidepressant effects of stem cell therapy encompass the stimulation of neurotrophic factors, immune function modulation, and augmented monoamine levels. Nonetheless, these pathways and other undiscovered mechanisms necessitate further investigation. Depression fundamentally manifests as a neurodegenerative disorder. Given stem cell therapy's success in addressing a range of neurodegenerative pathologies, it opens the door to explore its application in depression treatment. This exploration may include repairing damaged nerves directly or indirectly and inhibiting neurotoxicity. Nevertheless, significant challenges must be overcome before stem cell therapies can be applied clinically. Successful resolution of these issues will ultimately determine the feasibility of incorporating stem cell therapies into the clinical landscape. This narrative review provides insights into the progress of research, potential avenues for exploration, and the prevailing challenges in the implementation of stem cell therapy for treatment of depression.

Fusing Sequence and Structural Knowledge by Heterogeneous Models to Accurately and Interpretively Predict Drug-Target Affinity.

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence features of drug SMILES, targets, and pockets and their corresponding graph structural features using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model's performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, such as DeepDTA, GraphDTA, and DeepDTAF, S2DTA's performance became more evident. It exhibited a 25.2% reduction in mean absolute error (MAE) and a 20.1% decrease in root mean square error (RMSE). Additionally, S2DTA showed some improvements in other crucial metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2, with these metrics experiencing increases of 19.6%, 17.5%, 8.1%, and 49.4%, respectively. Finally, we conducted an interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism. The analysis results supported that S2DTA was an effective and accurate tool for predicting DTA.