Background Pulmonary heart disease (PHD) is a critical cardiopulmonary disease in high-altitude areas, with pulmonary hypertension (PH) as its central pathological basis. Cold and hypoxia are vital environmental characteristics of high-altitude regions; however, their joint effect on the hospitalization risk for PHD and the underlying biological mechanisms remain unclear. Objective To evaluate the associations of cold, hypoxia, and their joint exposure with PHD hospitalization risk, and to investigate PH-related pathological changes induced by combined cold and hypoxic exposure and the intervention effects of γ-aminobutyric acid (GABA) in rats. Methods Based on hospitalization data for 92986 PHD cases from 333 hospitals in Gansu Province (2018–2022), a generalized additive model (GAM) with a quasi-Poisson distribution was used to assess the associations between daily mean temperature, atmospheric oxygen content (AOC) and the daily number of PHD hospitalizations. Four joint exposure categories were constructed using the fifth percentile of temperature (P₅, −7.90℃) and AOC grades (high or low) to assess the associations between different exposure combinations and daily PHD hospital admissions. To further investigate PH-related pathological changes and the intervention effects of GABA, 24 specific-pathogen-free male Sprague–Dawley rats were randomly assigned to four groups (n=6): normal temperature and normoxia control group (NC), combined cold and hypoxia exposure group (CH), combined exposure plus low-dose GABA intervention group (CHL), and combined exposure plus high-dose GABA intervention group (CHH). After 5 weeks, pulmonary hemodynamics and vascular remodeling were evaluated. Gut microbiota and inflammatory cytokines in lung tissue were assessed using 16S rRNA sequencing and enzyme-linked immunosorbent assay (ELISA), respectively. Results Each 1 g·m⁻³ decrease in AOC and each 1 ℃ decrease in daily mean temperature were associated with 2.6% (RR=1.026, 95%CI: 1.024, 1.027) and 1.8% (RR=1.018, 95%CI: 1.015, 1.020) increases in PHD hospitalization risk, respectively. Compared with the non-low-temperature and high-AOC group, the low-temperature and low-AOC group had the highest risk estimate (RR=1.673, 95%CI: 1.567, 1.785). Animal experiments demonstrated that, compared with the NC group, rats in the CH group showed shortened pulmonary artery acceleration time (PAT) and increased mean pulmonary artery pressure (MPAP), right ventricular hypertrophy index (RVHI), pulmonary arterial wall thickness, and α-smooth muscle actin (α-SMA) expression (P<0.05). The Observed Species and Chao1 indices of the gut microbiota were significantly lower in the CH group than in the NC group (P<0.05), while levels of tumor necrosis factor-alpha (TNF-α) and interleukin-1beta (IL-1β) in lung tissue were elevated (P<0.05). GABA intervention improved selected hemodynamic, vascular remodeling, and inflammatory indices. Conclusion Decreases in daily mean temperature and AOC are associated with an increased risk of PHD hospitalization, with the highest risk estimate observed in the low-temperature and low-AOC group. Animal experiments suggest that combined cold and hypoxic exposure induce PH-related pathological changes accompanied by alterations in the gut microbiota and pulmonary inflammation. GABA may partially ameliorate these changes.

Sleep is an instinctive behavior alternating awakening state, sleep entails many active processes occurring at the cellular, circuit and organismal levels. The function of sleep is to restore cellular energy, enhance immunity, promote growth and development, consolidate learning and memory to ensure normal life activities. However, with the increasing of social pressure involved in work and life, the incidence of sleep disorders (SD) is increasing year by year. In the short term, sleep disorders lead to impaired memory and attention; in the longer term, it produces neurological dysfunction or even death. There are many ways to directly or indirectly contribute to sleep disorder and keep the hormones, including pharmacological alternative treatments, light therapy and stimulus control therapy. Exercise is also an effective and healthy therapeutic strategy for improving sleep. The intensities, time periods, and different types of exercise have different health benefits for sleep, which can be found through indicators such as sleep quality, sleep efficiency and total sleep time. So it is more and more important to analyze the mechanism and find effective regulation targets during sleep disorder through exercise. Dopamine (DA) is an important neurotransmitter in the nervous system, which not only participates in action initiation, movement regulation and emotion regulation, but also plays a key role in the steady-state remodeling of sleep-awakening state transition. Appreciable evidence shows that sleep disorder on humans and rodents evokes anomalies in the dopaminergic signaling, which are also implicated in the development of psychiatric illnesses such as schizophrenia or substance abuse. Experiments have shown that DA in different neural pathways plays different regulatory roles in sleep behavior, we found that increasing evidence from rodent studies revealed a role for ventral tegmental area DA neurons in regulating sleep-wake patterns. DA signal transduction and neurotransmitter release patterns have complex interactions with behavioral regulation. In addition, experiments have shown that exercise causes changes in DA homeostasis in the brain, which may regulate sleep through different mechanisms, including cAMP response element binding protein signal transduction, changes in the circadian rhythm of biological clock genes, and interactions with endogenous substances such as adenosine, which affect neuronal structure and play a neuroprotective role. This review aims to introduce the regulatory effects of exercise on sleep disorder, especially the regulatory mechanism of DA in this process. The analysis of intracerebral DA signals also requires support from neurophysiological and chemical techniques. Our laboratory has established and developed an in vivo brain neurochemical analysis platform, which provides support for future research on the regulation of sleep-wake cycles by movement. We hope it can provide theoretical reference for the formulation of exercise prescription for clinical sleep disorder and give some advice to the combined intervention of drugs and exercise.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

Bismuth iodide(BiOI)with different crystal plane ratios of(110)to(001)was synthesized,and typeⅡheterojunction formed between(001)and(110)crystal planes of BiOI was used to improve the separation efficiency of photogenerated electrons and holes.Then the BiOI(001)/(110)was composited with nitrogen-doped graphene quantum dots(N-GQDs)to prepare a ternary composites,which could enhance the range and intensity of light absorption,and prolonged the lifetime of photogenerated electrons due to the formation of Z-scheme heterojunctions between BiOI and N-GQDs,thereby leading to the excellent photoelectric performance of the BiOI/N-GQDs for generating sensitive photoelectric response signals.A photoelectrochemical sensor for sensitive detection of chlorpyrifos(CPF)was designed with BiOI/N-GQDs-modified FTO electrode as a photocathode.The S and N atoms contained in CPF were coordinated with Bi(Ⅲ)on the surface of BiOI,which reduced the photocurrent of BiOI/N-GQDs.The photocurrent change was linear with logarithm of concentration of CPF in the range of 1.5×10-12-5.0×10-9 mol/L,and the detection limit was 1.5×10-12 mol/L.The sensor was highly sensitive,selective and stable,and could be used for determination of trace CPF in environmental and food samples.

Water-soluble silver nanoclusters(DHLA-AgNCs)with red fluorescence emission were synthesized using silver nitrate(AgNO3)as silver source,dihydrolipoic acid(DHLA)as ligand and sodium borohydride(NaBH4)as reducing agent by one-pot method.Based on the selective quenching of DHLA-AgNCs by tetracycline(TC),a rapid and selective fluorescence analysis method for detection of TC was constructed by monitoring the fluorescence intensity change at 650 nm.Under the optimal detection conditions,the fluorescence quenching efficiency of DHLA-AgNCs showed good linear relationship with concentration of TC within the range of 10.0-120.0 μmol/L,and the limit of detection(LOD)was 0.39 μmol/L.This method was successfully applied to detection of TC in milk samples,with spiked recoveries ranging from 99.5%to 102.5%,and relative standard deviations(RSDs)less than 5%.This method had the advantages of simplicity,rapidity,strong specificity and low cost,and thus provided a simple and feasible strategy for selective detection of TC.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

In-fusion cloning technology,as a revolutionary and efficient molecular biology tool,has been applied in multiple research fields such as basic biology,biotechnology,and biomedicine.In this article,we introduce a teaching reform project suitable for undergraduate students in the course of"Molecular Bi-ology Laboratory",which utilizes in-Fusion cloning technology to construct a prokaryotic expression vector for alkaline phosphatase mutant genes.Through specific teaching cases,we systematically explored the design and implementation of experimental projects,and focused on analyzing the key and difficult points of the teaching content.Our teaching practice has found that the implementation of this educational re-form project has achieved very good results in enhancing students' core biological literacy,bioinformatics skills,research thinking,and innovation abilities.At the same time,the application of this technology can significantly improve the quality of experimental teaching,providing new ideas and practical refer-ences for promoting the reform and innovation of National First-Class Courses.

The development of anticancer drugs to treat triple-negative breast cancer (TNBC) is an ongoing challenge. Immunogenic cell death (ICD) has garnered considerable interest worldwide as a promising synergistic modality for cancer chemoimmunotherapy. However, only few drugs or treatment modalities can trigger an ICD response and none of them exert a considerable clinical effect against TNBC. Therefore, new agents with potentially effective chemoimmunotherapeutic response are required. In this study, five new cyclometalated Ir(III) complexes containing isoquinoline alkaloid CˆN ligands were designed and synthesized. Among them, Ir-1 exhibited the highest in vitro cytotoxicity. Mechanistically, Ir-1 could trigger autophagy-dependent ferroptosis and a subsequent ferroptosis-dependent ICD response as well as indoleamine 2,3-dioxygenase (IDO) inhibition via reactive oxygen species (ROS)-mediated endoplasmic reticulum (ER) stress in MDA-MB-231 cells. When immunocompetent BALB/c mice were vaccinated with Ir-1-treated dying TNBC cells, antitumor CD8⁺ T-cell response and Foxp3⁺ T-cell depletion were induced, resulting in long-lasting antitumor immunity in TNBC cells. Moreover, combination therapy with Ir-1 and anti-PD1 could substantially augment in vivo therapeutic effects. Based on these results, Ir-1 is a promising candidate for chemoimmunotherapy against TNBC and its effects are mediated synergistically via ICD induction and IDO blockage.

Intervention in chronically activated microglia-mediated neuroinflammation is a novel approach to treat Alzheimer's disease (AD). The low permeability of the blood‒brain barrier (BBB) and non-selective distribution in the brain severely restrict AD drugs' disease-modifying efficacy. Here, an immunosuppressant TREM2-lowing antisense oligonucleotides (ASOs) and resveratrol co-loaded cationic liposome is developed as an immune reprogramming nanomodulator modified by acid-cleavable BBB-targeting peptide and microglia-targeting peptide (Res@TcMNP/ASO) for AD management. Res@TcMNP/ASO can enter brain endothelial cells via D-T7 peptides. Then D-T7 undergoes an acid-responsive cleavage, facilitating the escape of Res@MNP/ASO from endo/lysosomes to cross the BBB. The detached Res@MNP/ASO specifically targets M1-phenotype microglia via exposed MG1 peptides to prompt the simultaneous delivery of two drugs into activated microglia. This nanomodulator can not only restore the immune function of microglia through TREM2-lowing ASO but also mitigate the immune stimulation to microglia caused by reactive oxygen species (ROS) through resveratrol, thereby synergistically inhibiting the chronic activation of microglia to alleviate neuroinflammation in AD. Our results indicate that this combination treatment can achieve significant behavioral and cognitive improvements in late APP/PS1 mice.