Development and Validation of an Explainable Machine Learning Model for Predicting Myocardial Injury After Noncardiac Surgery in Two Centers in China: Retrospective Study.

Background: Myocardial injury after noncardiac surgery (MINS) is an easily overlooked complication but closely related to postoperative cardiovascular adverse outcomes; therefore, the early diagnosis and prediction are particularly important. Objective: We aimed to develop and validate an explainable machine learning (ML) model for predicting MINS among older patients undergoing noncardiac surgery. Methods: The retrospective cohort study included older patients who had noncardiac surgery from 1 northern center and 1 southern center in China. The data sets from center 1 were divided into a training set and an internal validation set. The data set from center 2 was used as an external validation set. Before modeling, the least absolute shrinkage and selection operator and recursive feature elimination methods were used to reduce dimensions of data and select key features from all variables. Prediction models were developed based on the extracted features using several ML algorithms, including category boosting, random forest, logistic regression, naïve Bayes, light gradient boosting machine, extreme gradient boosting, support vector machine, and decision tree. Prediction performance was assessed by the area under the receiver operating characteristic (AUROC) curve as the main evaluation metric to select the best algorithms. The model performance was verified by internal and external validation data sets with the best algorithm and compared to the Revised Cardiac Risk Index. The Shapley Additive Explanations (SHAP) method was applied to calculate values for each feature, representing the contribution to the predicted risk of complication, and generate personalized explanations. Results: A total of 19,463 eligible patients were included; among those, 12,464 patients in center 1 were included as the training set; 4754 patients in center 1 were included as the internal validation set; and 2245 in center 2 were included as the external validation set. The best-performing model for prediction was the CatBoost algorithm, achieving the highest AUROC of 0.805 (95% CI 0.778-0.831) in the training set, validating with an AUROC of 0.780 in the internal validation set and 0.70 in external validation set. Additionally, CatBoost demonstrated superior performance compared to the Revised Cardiac Risk Index (AUROC 0.636; P<.001). The SHAP values indicated the ranking of the level of importance of each variable, with preoperative serum creatinine concentration, red blood cell distribution width, and age accounting for the top three. The results from the SHAP method can predict events with positive values or nonevents with negative values, providing an explicit explanation of individualized risk predictions. Conclusions: The ML models can provide a personalized and fairly accurate risk prediction of MINS, and the explainable perspective can help identify potentially modifiable sources of risk at the patient level.

Synergistic Effect of Rutile and Brookite TiO2 for Photocatalytic Formic Acid Dehydrogenation.

Solar energy is an ideal clean and inexhaustible energy source. Solar-driven formic acid (FA) dehydrogenation is one of the promising strategies to address safety and cost issues related to the storage, transport, and distribution of hydrogen energy. For FA dehydrogenation, the O-H and C-H cleavages are the key steps, and developing a photocatalyst with the ability to break these two bonds is critical. In this work, both density functional theory (DFT) calculation and experimental results confirmed the positive synergistic effect between brookite and rutile TiO2 for O-H and C-H cleavage in HCOOH. Further, brookite TiO2 is beneficial to the generation of the •OH radical and significantly promotes C-H cleavage in formate. Under optimized conditions, the H2 production efficiency of FA dehydrogenation can reach up to 30.4 µmol·mg-1·h-1, which is the highest value compared with similar reported TiO2-based systems and over 1.7 times the reported highest value of Au0.75Pd0.25/TiO2 photocatalysts. More importantly, after more than 42 days (>500 h) of irradiation, the system still demonstrated high H2 production activity, indicating the potential for practical application. This work provides a valuable strategy to improve both the efficiency and stability of photocatalytic FA dehydrogenation under mild conditions.

Integrated effect of aspect ratio and tree spacing on pedestrian thermal comfort of street canyon.

Increasing heat stress in urban environments due to climate change has a significant adverse impact on human work and daily life. Street canyons as the main component of the underlying surface of the city and the main place of residents' activities, a comprehensive understanding of street morphology and tree planting practices can help to improve thermal comfort. Based on survey data and field experiments, this study designed 30 scenarios and employed ENVI-met model (version 5.0.3) to quantify the effect of street aspect ratio (H/W: H is building height and W is street width) and tree spacing (TS) on pedestrian thermal comfort in two differently oriented streets (north-south and east-west) in Taiyuan, China. Results showed that H/W ratio and TS significantly influenced the street thermal comfort mainly owing to shading. H/W ratio played a pivotal role in reducing mean radiant temperature (Tmrt) and physiological equivalent temperature (PET), and was negatively correlated with Tmrt and PET. Compared to no-tree scenarios, street trees significantly improved thermal comfort (mean reductions of Tmrt and PET were 12.74℃ and 5.66℃, respectively), and PET and Tmrt were significantly negatively correlated with TS. The improvement effect of street trees on Tmrt and PET in east-west oriented street was better than north-south oriented street. H/W = 1.0 and TS = 6 m appeared as the proposed combination to mitigate the summer thermal comfort in the temperate monsoon climate zone. These quantitative results provide new insights into renewal and design strategies for future urban planning.

Comparative Gene Expression Analysis of Salmonella Typhimurium DT104 in Ground Chicken Extract and Brain Heart Infusion Broth.

Salmonella enterica Typhimurium DT104 (S. Typhimurium DT104) is an important foodborne pathogen that is associated with poultry and poultry products. Currently, there is very little information on the underlying molecular mechanisms that allow DT104 to survive and propagate in poultry meat and the poultry processing environment. The current study assessed the global gene expression of DT104 in ground chicken extract (GCE) compared to brain heart infusion (BHI) medium using RNA-Seq technology. DT104 was grown to the early stationary phase (ESP), inoculated into GCE or BHI, and then re-grown to the log phase before RNA was extracted and transcripts were quantified by RNA-Seq. Gene expression for DT104 grown in GCE was then compared to that of DT104 grown in BHI for samples grown to the ESP. Growth in GCE resulted in the up-regulated expression of genes related to translation, carnitine metabolism (23-283-fold change), and cobalamin (vitamin B12) biosynthesis (14-fold change). In particular, the presence of carnitine in chicken meat, and thus, in GCE, which lacks carbohydrates, may allow Salmonella to utilize this compound as a carbon and nitrogen source. This study demonstrates that RNA-Seq data can provide a comprehensive analysis of DT104 gene expression in a food model for poultry products. This study also provides additional evidence for the importance of metabolic adaptation in the ability of S. enterica to successfully adapt to and occupy niches outside of its host and provides potential targets that could be used to develop intervention strategies to control Salmonella in poultry.

Epigenomic identification of vernalization cis-regulatory elements in winter wheat.

BACKGROUND: Winter wheat undergoes vernalization, a process activated by prolonged exposure to low temperatures. During this phase, flowering signals are generated and transported to the apical meristems, stimulating the transition to the inflorescence meristem while inhibiting tiller bud elongation. Although some vernalization genes have been identified, the key cis-regulatory elements and precise mechanisms governing this process in wheat remain largely unknown. RESULTS: In this study, we construct extensive epigenomic and transcriptomic profiling across multiple tissues-leaf, axillary bud, and shoot apex-during the vernalization of winter wheat. Epigenetic modifications play a crucial role in eliciting tissue-specific responses and sub-genome-divergent expressions during vernalization. Notably, we observe that H3K27me3 primarily regulates vernalization-induced genes and has limited influence on vernalization-repressed genes. The integration of these datasets enables the identification of 10,600 putative vernalization-related regulatory elements including distal accessible chromatin regions (ACRs) situated 30Kb upstream of VRN3, contributing to the construction of a comprehensive regulatory network. Furthermore, we discover that TaSPL7/15, integral components of the aging-related flowering pathway, interact with the VRN1 promoter and VRN3 distal regulatory elements. These interactions finely regulate their expressions, consequently impacting the vernalization process and flowering. CONCLUSIONS: Our study offers critical insights into wheat vernalization's epigenomic dynamics and identifies the putative regulatory elements crucial for developing wheat germplasm with varied vernalization characteristics. It also establishes a vernalization-related transcriptional network, and uncovers that TaSPL7/15 from the aging pathway participates in vernalization by directly binding to the VRN1 promoter and VRN3 distal regulatory elements.

Prevalence and Associated Factors of Type 2 Diabetes Mellitus Among Chinese Hakka Individuals Aged 35-65 Years: A Cross-Sectional Study.

Purpose: The prevalence of diabetes in China is increasing, influenced by economic and genetic factors, with varying rates across regions. The Hakka population in Ganzhou city has unique exposures compared to surrounding districts, while limited research reported the epidemiological characteristics of type 2 diabetes mellitus (T2DM) in this population. This study aims to investigate the prevalence and influencing factors of T2DM among the population, thereby establishing a robust foundation for disease prevention and control measures. Patients and Methods: In 2017, a multistage random sampling method selected 3028 individuals from Ganzhou City's permanent resident population. Physical examinations, blood tests, and questionnaire surveys were conducted for data collection, with binary logistic regression analysis used to examine factors affecting T2DM prevalence. Results: A total of 2978 valid samples were included in this study. The average age of the surveyed population was 52.83±7.88 years, comprising 966 males and 2012 females. The prevalence rates of T2DM were 11.8% and 12.9% in males and females, respectively, while the standardized prevalence rate was recorded as 9.1%. Logistic regression analysis revealed that age (Odds Ratio[OR]=1.05, 95% Confidence Interval [CI]:1.03-1.06), hypertension (OR=2.22, 95% CI:1.71-2.93), family history of diabetes (OR= 3.54, 95% CI: 2.58-4.85), overweight (OR=1.73, 95% CI: 1.20-2.48), high total cholesterol (OR=1.17, 95% CI:1.09-1.27), elevated low-density lipoprotein cholesterol (OR=1.19, 95% CI:1.00-1.40) and serum insulin (OR=1.05, 95% CI:1.03-1.06) were identified as significant risk factors for T2DM, Conversely, a higher level of high-density lipoprotein cholesterol (OR=0.55, 95% CI:0.36-0.84) was found to be inversely related to T2DM development. Conclusion: The prevalence of T2DM in Ganzhou city has significantly increased. The effective implementation of comprehensive management strategies aimed at addressing hypertension, overweight, dyslipidemia, and abnormal serum insulin level is essential for promoting overall well-being and efficiently controlling the prevalence of T2DM.

Construction of Near-Infrared Probes with Remarkable Large Stokes Shift Based on a Novel Purine Platform for the Visualization of mtG4 Upregulation during Mitochondrial Disorder in Somatic Cells and Human Sperms.

G-quadruplex structures within the nuclear genome (nG4) is an important regulatory factor, while the function of G4 in the mitochondrial genome (mtG4) still needs to be explored, especially in human sperms. To gain a better understanding of the relationship between mtG4 and mitochondrial function, it is crucial to develop excellent probes that can selectively visualize and track mtG4 in both somatic cells and sperms. Herein, based on our previous research on purine frameworks, we attempted for the first time to extend the conjugated structure from the C-8 site of purine skeleton and discovered that the purine derivative modified by the C-8 aldehyde group is an ideal platform for constructing near-infrared probes with extremely large Stokes shift (>220 nm). Compared with the compound substituted with methylpyridine (PAP), the molecule substituted with methylthiazole orange (PATO) showed better G4 recognition ability, including longer emission (∼720 nm), more significant fluorescent enhancement (∼67-fold), lower background, and excellent photostability. PATO exhibited a sensitive response to mtG4 variation in both somatic cells and human sperms. Most importantly, PATO helped us to discover that mtG4 was significantly increased in cells with mitochondrial respiratory chain damage caused by complex I inhibitors (6-OHDA and rotenone), as well as in human sperms that suffer from oxidative stress. Altogether, our study not only provides a novel ideal molecular platform for constructing high-performance probes but also develops an effective tool for studying the relationship between mtG4 and mitochondrial function in both somatic cells and human sperms.

[Retracted] MicroRNA­106a­5p promotes the proliferation, autophagy and migration of lung adenocarcinoma cells by targeting LKB1/AMPK.

[This retracts the article DOI: 10.3892/etm.2021.10857.].

Monitoring Cattle Ruminating Behavior Based on an Improved Keypoint Detection Model.

Cattle rumination behavior is strongly correlated with its health. Current methods often rely on manual observation or wearable devices to monitor ruminating behavior. However, the manual monitoring of cattle rumination is labor-intensive, and wearable devices often harm animals. Therefore, this study proposes a non-contact method for monitoring cattle rumination behavior, utilizing an improved YOLOv8-pose keypoint detection algorithm combined with multi-condition threshold peak detection to automatically identify chewing counts. First, we tracked and recorded the cattle's rumination behavior to build a dataset. Next, we used the improved model to capture keypoint information on the cattle. By constructing the rumination motion curve from the keypoint information and applying multi-condition threshold peak detection, we counted the chewing instances. Finally, we designed a comprehensive cattle rumination detection framework to track various rumination indicators, including chewing counts, rumination duration, and chewing frequency. In keypoint detection, our modified YOLOv8-pose achieved a 96% mAP, an improvement of 2.8%, with precision and recall increasing by 4.5% and 4.2%, enabling the more accurate capture of keypoint information. For rumination analysis, we tested ten video clips and compared the results with actual data. The experimental results showed an average chewing count error of 5.6% and a standard error of 2.23%, verifying the feasibility and effectiveness of using keypoint detection technology to analyze cattle rumination behavior. These physiological indicators of rumination behavior allow for the quicker detection of abnormalities in cattle's rumination activities, helping managers make informed decisions. Ultimately, the proposed method not only accurately monitors cattle rumination behavior but also provides technical support for precision management in animal husbandry, promoting the development of modern livestock farming.

Druggability Analysis of Protein Targets for Drug Discovery to Combat Listeria monocytogenes.

Extensive research has been conducted to identify key proteins governing stress responses, virulence, and antimicrobial resistance, as well as to elucidate their interactions within Listeria monocytogenes. While these proteins hold promise as potential targets for novel strategies to control L. monocytogenes, given their critical roles in regulating the pathogen's metabolism, additional analysis is needed to further assess their druggability-the chance of being effectively bound by small-molecule inhibitors. In this work, 535 binding pockets of 46 protein targets for known drugs (mainly antimicrobials) were first analyzed to extract 13 structural features (e.g., hydrophobicity) in a ligand-protein docking platform called Molsoft ICM Pro. The extracted features were used as inputs to develop a logistic regression model to assess the druggability of protein binding pockets, with a value of one if ligands can bind to the protein pocket. The developed druggability model was then used to evaluate 23 key proteins from L. monocytogenes that have been identified in the literature. The following proteins are predicted to be high-potential druggable targets: GroEL, FliH/FliI complex, FliG, FlhB, FlgL, FlgK, InlA, MogR, and PrfA. These findings serve as an initial point for future research to identify specific compounds that can inhibit druggable target proteins and to design experimental work to confirm their effectiveness as drug targets.

Effect of radio frequency roasting on the lipid profile of peanut oil and the mechanism of lipids transformation: Revealed by untargeted lipidomics approach.

Radio frequency (RF) heating has been proved an alternative roasting method for peanuts, which could effectively degrade aflatoxins and possesses the advantages of greater heating efficiency and penetration depth. This study aimed to investigate the influences of RF roasting on the lipid profile of peanut oil under 150 °C target temperature with varied peanut moisture contents (8.29 % and 20 %) and holding times (0, 7.5, and 15 min), using ultra-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS/MS)-based lipidomics. In total, 2587 lipid species from 35 subclasses were identified. After roasting, the contents of sterol lipid (ST) and subclasses of glycerophospholipids (GPs) and glycoglycerolipids increased significantly, while fatty acid (FA), Oxidized (Ox-) FA, cholesterol (CE), and all subclasses of glycerolipids (GLs) decreased, and 1084 differential lipids were screened. The highest ST and lowest CE contents in peanut oil were achieved by medium roasting (7.5 min). The raise in moisture content of peanut simply affected a few GPs subclasses adversely. Compared with hot air (HA) roasting, RF decelerated lipid oxidation, showing higher levels of diacylglycerol, triacylglycerol and FA, with no additional negative impact and only 69 exclusive differential lipids. During RF roasting, hydrolysis and oxidation of fatty acyl chains into secondary oxides were the central behaviors of lipids transformation. This study could provide insights into the lipid changes and transformation mechanism of peanut oil by RF roasting processing.

Insight into the photodynamic mechanism and protein binding of a nitrosyl iron-sulfur [Fe2S2(NO)4]2- cluster.

Iron-sulfur cluster conversion and nitrosyl modification are involved in regulating their functions and play critical roles in signaling for biological systems. Hereby, the photo-induced dynamic process of (Me4N)2[Fe2S2(NO)4] was monitored using time-resolved electron paramagnetic resonance (EPR) spectra, MS spectra and cellular imaging methods. Photo-irradiation and the solvent affect the reaction rates and products. Spectroscopic and kinetic studies have shown that the process involves at least three intermediates: spin-trapped NO free radical species with a gav at 2.040, and two other iron nitrosyl species, dinitrosyl iron units (DNICs) and mononitrosyl iron units (MNICs) with gav values at 2.031 and 2.024, respectively. Moreover, the [Fe2S2(NO)4]2- cluster could bind with ferritin and decompose gradually, and a binding state of dinitrosyl iron coordinated with Cys102 of the recombinant human heavy chain ferritin (rHuHF) was finally formed. This study provides insight into the photodynamic mechanism of nitrosyl iron - sulfur clusters to improve the understanding of physiological activity.

Efficient Near-Infrared Fluorophores Based on Cyanostyrene Derivatives for Two-Photon Fluorescence Bioimaging.

Organic fluorescent materials with red/near-infrared (NIR) emission are highly promising for use in biotechnology due to their exceptional advantages. However, traditional red/NIR fluorophores often exhibit weak emission at high concentrations or in an aggregated state due to the aggregate-caused quenching effect, which severely limits their applicability in biological imaging. To address this challenge, we developed a series of cyanostyrene derivatives with aggregation-induced emission characteristics, including 2,3-Bis-(4-styryl-phenyl)-but-2-enedinitrile (DPB), 2,3-Bis-{4-[2-(4-methoxy- phenyl)-vinyl]-phenyl}-but-2-enedinitrile (DOB), 2,3-Bis-{4-[2-(4-diphenylamino- phenyl)-vinyl]-phenyl}-but-2-enedinitrile (DTB), and 2,3-Bis-[4-(2-{4-[phenyl- (4-triphenylvinyl-phenyl)-amino]-phenyl}-vinyl)- phenyl]-but-2-enedinitrile (DTTB). Notably, these compounds exhibited intense solid state fluorescence owing to AIE effect, especially DTTB shows NIR emission with high solid state quantum efficiency (712 nm, ΦF=14.2 %). Then we prepared DTTB@PS-PEG NPs nanoparticles by encapsulating DTTB with the amphiphilic polymer polystyrene-polyethylene glycol (PS-PEG). Importantly, DTTB@PS-PEG NPs exhibited highly efficient NIR luminescence (ΦF=28.7 %) and a large two-photon absorption cross-section (1900 GM) under 800 nm laser excitation. The bright two-photon fluorescence of DTTB@PS-PEG indicated that it can be a highly promising candidate for two-photon fluorescence probe. Therefore, this work provides valuable insights for the design of highly efficient and NIR-emitting two-photon fluorescent probes.

Comparison of seven CD19 CAR designs in engineering NK cells for enhancing anti-tumour activity.

Chimeric antigen receptor-natural killer (CAR-NK) cell therapy is emerging as a promising cancer treatment, with notable safety and source diversity benefits over CAR-T cells. This study focused on optimizing CAR constructs for NK cells to maximize their therapeutic potential. We designed seven CD19 CAR constructs and expressed them in NK cells using a retroviral system, assessing their tumour-killing efficacy and persistence. Results showed all constructs enhanced tumour-killing and prolonged survival in tumour-bearing mice. In particular, CAR1 (CD8 TMD-CD3ζ SD)-NK cells showed superior efficacy in treating tumour-bearing animals and exhibited enhanced persistence when combined with OX40 co-stimulatory domain. Of note, CAR1-NK cells were most effective at lower effector-to-target ratios, while CAR4 (CD8 TMD-OX40 CD- FcεRIγ SD) compromised NK cell expansion ability. Superior survival rates were noted in mice treated with CAR1-, CAR2 (CD8 TMD- FcεRIγ SD)-, CAR3 (CD8 TMD-OX40 CD- CD3ζ SD)- and CAR4-NK cells over those treated with CAR5 (CD28 TMD- FcεRIγ SD)-, CAR6 (CD8 TMD-4-1BB CD-CD3ζ 1-ITAM SD)- and CAR7 (CD8 TMD-OX40 CD-CD3ζ 1-ITAM SD)-NK cells, with CAR5-NK cells showing the weakest anti-tumour activity. Increased expression of exhaustion markers, especially in CAR7-NK cells, suggests that combining CAR-NK cells with immune checkpoint inhibitors might improve anti-tumour outcomes. These findings provide crucial insights for developing CAR-NK cell products for clinical applications.

In-line attosecond photoelectron holography for single photon ionization.

The momentum distribution of photoelectrons in H2+ molecules subjected to an attosecond pulse is theoretically investigated. To better understand the laser-molecule interaction, we develop an in-line photoelectron holography approach that is analogous to optical holography. This approach is specifically suitable for extracting the amplitude and phase of the forward-scattered electron wave packet in a dissociating molecule with atomic precision. We also extend this approach to imaging the transient scattering cross-section of a molecule dressed by a near infrared laser field. This attosecond photoelectron holography sheds light on structural microscopy of dissociating molecules with high spatial-temporal resolution.

Human-derived fecal microbiota transplantation alleviates social deficits of the BTBR mouse model of autism through a potential mechanism involving vitamin B6 metabolism.

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by social communication deficiencies and stereotypic behaviors influenced by hereditary and/or environmental risk factors. There are currently no approved medications for treating the core symptoms of ASD. Human fecal microbiota transplantation (FMT) has emerged as a potential intervention to improve autistic symptoms, but the underlying mechanisms are not fully understood. In this study, we evaluated the effects of human-derived FMT on behavioral and multi-omics profiles of the BTBR mice, an established model for ASD. FMT effectively alleviated the social deficits in the BTBR mice and normalized their distinct plasma metabolic profile, notably reducing the elevated long-chain acylcarnitines. Integrative analysis linked these phenotypic changes to specific Bacteroides species and vitamin B6 metabolism. Indeed, vitamin B6 supplementation improved the social behaviors in BTBR mice. Collectively, these findings shed new light on the interplay between FMT and vitamin B6 metabolism and revealed a potential mechanism underlying the therapeutic role of FMT in ASD.IMPORTANCEAccumulating evidence supports the beneficial effects of human fecal microbiota transplantation (FMT) on symptoms associated with autism spectrum disorder (ASD). However, the precise mechanism by which FMT induces a shift in the microbiota and leads to symptom improvement remains incompletely understood. This study integrated data from colon-content metagenomics, colon-content metabolomics, and plasma metabolomics to investigate the effects of FMT treatment on the BTBR mouse model for ASD. The analysis linked the amelioration of social deficits following FMT treatment to the restoration of mitochondrial function and the modulation of vitamin B6 metabolism. Bacterial species and compounds with beneficial roles in vitamin B6 metabolism and mitochondrial function may further contribute to improving FMT products and designing novel therapies for ASD treatment.

Discovery of Novel p53-MDM2 Inhibitor (RG7388)-Conjugated PlatinumIV Complexes as Potent Antitumor Agents.

While a number of p53-MDM2 inhibitors have progressed into clinical trials for the treatment of cancer, their progression has been hampered by a variety of problems, including acquired drug resistance, dose-dependent toxicity, and limited clinical efficiency. To make more progress, we integrated the advantages of MDM2 inhibitors and platinum drugs to construct novel PtIV-RG7388 (a selective MDM2 inhibitor) complexes. Most complexes, especially 5a and 5b, displayed greatly improved antiproliferative activity against both wild-type and mutated p53 cancer cells. Remarkably, 5a exhibited potent in vivo tumor growth inhibition in the A549 xenograft model (66.5%) without apparent toxicity. It arrested the cell cycle at both the S phase and the G2/M phase and efficiently induced apoptosis via the synergistic effects of RG7388 and cisplatin. Altogether, PtIV-RG7388 complex 5a exhibited excellent in vitro and in vivo antitumor activities, highlighting the therapeutic potential of PtIV-RG7388 complexes as antitumor agents.

Development and validation of a nomogram to predict postoperative delirium in older patients after major abdominal surgery: a retrospective case-control study.

BACKGROUND: Postoperative delirium is a common complication in older patients, with poor long-term outcomes. This study aimed to investigate risk factors and develop a predictive model for postoperative delirium in older patients after major abdominal surgery. METHODS: This study retrospectively recruited 7577 patients aged ≥ 65 years who underwent major abdominal surgery between January 2014 and December 2018 in a single hospital in Beijing, China. Patients were divided into a training cohort (n = 5303) and a validation cohort (n = 2224) for univariate and multivariate logistic regression analyses and to build a nomogram. Data were collected for 43 perioperative variables, including demographics, medical history, preoperative laboratory results, imaging, and anesthesia information. RESULTS: Age, chronic obstructive pulmonary disease, white blood cell count, glucose, total protein, creatinine, emergency surgery, and anesthesia time were associated with postoperative delirium in multivariate analysis. We developed a nomogram based on the above 8 variables. The nomogram achieved areas under the curve of 0.731 and 0.735 for the training and validation cohorts, respectively. The discriminatory ability of the nomogram was further assessed by dividing the cases into three risk groups (low-risk, nomogram score < 175; medium-risk, nomogram score 175~199; high-risk, nomogram score > 199; P < 0.001). Decision curve analysis revealed that the nomogram provided a good net clinical benefit. CONCLUSIONS: We developed a nomogram that could predict postoperative delirium with high accuracy and stability in older patients after major abdominal surgery.

A small target detection algorithm based on improved YOLOv5 in aerial image.

Uncrewed aerial vehicle (UAV) aerial photography technology is widely used in both industrial and military sectors, but remote sensing for small target detection still faces several challenges. Firstly, the small size of targets increases the difficulty of detection and recognition. Secondly, complex aerial environmental conditions, such as lighting changes and background noise, significantly affect the quality of detection. Rapid and accurate identification of target categories is also a key issue, requiring improvements in detection speed and accuracy. This study proposes an improved remote sensing target detection algorithm based on the YOLOv5 architecture. In the YOLOv5s model, the Distribution Focal Loss function is introduced to accelerate the convergence speed of the network and enhance the network's focus on annotated data. Simultaneously, adjustments are made to the Cross Stage Partial (CSP) network structure, modifying the convolution kernel size, adding a new stack-separated convolution module, and designing a new attention mechanism to achieve effective feature fusion between different hierarchical structure feature maps. Experimental results demonstrate a significant performance improvement of the proposed algorithm on the RSOD dataset, with a 3.5% increase in detection accuracy compared to the original algorithm. These findings indicate that our algorithm effectively enhances the precision of remote sensing target detection and holds potential application prospects.

The Intersection of cerebral cholesterol metabolism and Alzheimer's disease: Mechanisms and therapeutic prospects.

Alzheimer's disease (AD) is a common neurodegenerative disease in the elderly, the exact pathogenesis of which remains incompletely understood, and effective preventive and therapeutic drugs are currently lacking. Cholesterol plays a vital role in cell membrane formation and neurotransmitter synthesis, and its abnormal metabolism is associated with the onset of AD. With the continuous advancement of imaging techniques and molecular biology methods, researchers can more accurately explore the relationship between cholesterol metabolism and AD. Elevated cholesterol levels may lead to vascular dysfunction, thereby affecting neuronal function. Additionally, abnormal cholesterol metabolism may affect the metabolism of ß-amyloid protein, thereby promoting the onset of AD. Brain cholesterol levels are regulated by multiple factors. This review aims to deepen the understanding of the subtle relationship between cholesterol homeostasis and AD, and to introduce the latest advances in cholesterol-regulating AD treatment strategies, thereby inspiring readers to contemplate deeply on this complex relationship. Although there are still many unresolved important issues regarding the risk of brain cholesterol and AD, and some studies may have opposite conclusions, further research is needed to enrich our understanding. However, these findings are expected to deepen our understanding of the pathogenesis of AD and provide important insights for the future development of AD treatment strategies targeting brain cholesterol homeostasis.