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.

Objective: To explore the association between CDKAL1 rs35261542, FAIM2 rs 3205718, and VGLL4 rs 2574704 polymorphisms with childhood obesity and related metabolic phenotypes to provide evidence for personalized prevention and management strategies. Methods: Based on the 2023 Long term Nutritional Health Effects of Early Childhood Nutrition Package Intervention project, the study enrolled 1 078 children aged 5-7 years from four counties in Henan (Songxian and Ruyang countries) and Guizhou (Guiding and Fuquan countries) provinces. Using BMI Z scores, 87 overweight and obese(OVOB) children were selected and matched by sex, age, and BMI Z score with 117 normal weight controls. Participants were further stratified into four metabolic phenotype groups: metabolically healthy normal weight (MHNW, n =51), metabolically unhealthy normal weight (MUNW, n =66), metabolically healthy obesity (MHO, n =31) and metabolically unhealthy obesity (MUO, n =56) based on four conventional cardiometabolic risk factor (CR) criteria. Data were collected through questionnaires, anthropometric measurements, serum biochemical tests, and KASP genotyping. The distribution of three genetic polymorphisms ( CDKAL1 rs35261542, FAIM2 rs3205718, VGLL4 rs 2574704) across metabolic subgroups was analyzed. Multivariate Logistic regression models assessed associations between these polymorphisms and obesity/metabolic phenotypes. Results: Multivariate Logistic regression analysis showed that Homozygous mutant AA genotype of CDKAL1 rs 35261542 was positively associated with OVOB( OR =3.63), MHO ( OR =11.04), MUO ( OR = 4.88 ) ( P <0.05). Homozygous TT genotype of FAIM2 rs 3205718 increased OVOB risk ( OR =4.44, P <0.05) but showed no association with metabolic phenotypes ( P >0.05). Homozygous mutant TT of VGLL4 rs 2574704 reduced the risks of MHO and MUO ( OR = 0.30, 0.24， P <0.05). Cumulative genetic effects analysis demonstrated carriers of 1 or 2 risk genotypes of rs 35261542 and rs 3205718 had progressively higher OVOB risk ( OR =2.53, 20.79), and the combination of rs 35261542 and rs 2574704 increased risks for both MHO ( OR =8.50) and MUO ( OR =5.00) ( P <0.05). Conclusions The AA genotype of rs 35261542 ( CDKAL1 ) positively correlates with childhood obesity and metabolic abnormalities. The TT genotype of rs 3205718 ( FAIM 2) increases obesity risk but not metabolic phenotypes. The TT genotype of rs 2574704 ( VGLL 4) shows protective effects against metabolic dysfunction. Risk genotypes exhibit dosedependent cumulative effects on obesity and metabolic outcomes.

ObjectiveThe scale of microalgae farming industry is huge. During farming, it is easy for microalgae to be affected by miscellaneous bacteria and other contaminants. Because of that, periodic test is necessary to ensure the growth of microalgae. Present microscopy imaging and spectral analysis methods have higher requirements for experiment personnel, equipment and sites, for which it is unable to achieve real-time portable detection. For the purpose of real-time portable microalgae detection, a real-time microalgae detection system of low detection requirement and fast detection speed is needed. MethodsThis study has developed a microalgae detection system based on deep learning. A microscopy imaging device based on bright field was constructed. With imaged captured from the device, a neural network based on YOLOv3 was trained and deployed on microcomputer, thus realizing real-time portable microalgae detection. This study has also improved the feature extraction network by introducing cross-region residual connection and attention mechanism and replacing optimizer with Adam optimizer using multistage and multimethod strategy. ResultsWith cross-region residual connection, the mAP value reached 0.92. Compared with manual result, the detection error was 2.47%. ConclusionThe system could achieve real-time portable microalgae detection and provide relatively accurate detection result, so it can be applied to periodic test in microalgae farming.

In recent years, nucleic acid therapy, as a revolutionary therapeutic tool, has shown great potential in the treatment of genetic diseases, infectious diseases and cancer. Lipid nanoparticles (LNPs) are currently the most advanced mRNA delivery carriers, and their emergence is an important reason for the rapid approval and use of COVID-19 mRNA vaccines and the development of mRNA therapy. Currently, mRNA therapeutics using LNP as a carrier have been widely used in protein replacement therapy, vaccines and gene editing. Conventional LNP is composed of four components: ionizable lipids, phospholipids, cholesterol, and polyethylene glycol (PEG) lipids, which can effectively load mRNA to improve the stability of mRNA and promote the delivery of mRNA to the cytoplasm. However, in the face of the complexity and diversity of clinical diseases, the structure, properties and functions of existing LNPs are too homogeneous, and the lack of targeted delivery capability may result in the risk of off-targeting. LNPs are flexibly designed and structurally stable vectors, and the adjustment of the types or proportions of their components can give them additional functions without affecting the ability of LNPs to deliver mRNAs. For example, by replacing and optimizing the basic components of LNP, introducing a fifth component, and modifying its surface, LNP can be made to have more precise targeting ability to reduce the side effects caused by treatment, or be given additional functions to synergistically enhance the efficacy of mRNA therapy to respond to the clinical demand for nucleic acid therapy. It is also possible to further improve the efficiency of LNP delivery of mRNA through machine learning-assisted LNP iteration. This review can provide a reference method for the rational design of engineered lipid nanoparticles delivering mRNA to treat diseases.

As a microbial therapy method, fecal microbiota transplantation (FMT) has attracted the attention of researchers in recent years. As one of the most direct and effective methods to improve gut microbiota, FMT achieves therapeutic benefits by transplanting functional gut microbiota from healthy human feces into the intestines of patients to reconstruct new gut microbiota. FMT has been proven to be an effective treatment for gastrointestinal diseases such as Clostridium difficile infection, irritable bowel syndrome, and inflammatory bowel disease. In addition, the clinical and basic research of FMT outside the gastrointestinal system is also emerging. It is worth noting that there is bidirectional communication between the gut microbial community and the central nervous system (CNS) through the gut-brain axis. Some gut bacteria can synthesize and release neurotransmitters such as glutamate, gamma-aminobutyric acid (GABA) and dopamine. Imbalanced gut microbiota may interfere with the normal levels of these neurotransmitters, thereby affecting brain function. Gut microbiota can also produce metabolites that may cross the blood-brain barrier and affect CNS function. FMT may affect the occurrence and development of CNS and its related diseases by reshaping the gut microbiota of patients through a variety of pathways such as nerves, immunity, and metabolites. This article introduces the development of FMT and the research status of FMT in China, and reviews the basic and clinical research of FMT in neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease), neurotraumatic diseases (spinal cord injury, traumatic brain injury) and stroke from the characteristics of three types of nervous system diseases, the characteristics of intestinal flora, and the therapeutic effect and mechanism of fecal microbiota transplantation, summarize the common mechanism of fecal microbiota transplantation in the treatment of CNS diseases and the therapeutic targets. We found that the common mechanisms of FMT in the treatment of nervous system diseases may include the following 3 categories through summary and analysis. (1) Gut microbiota metabolites, such as SCFAs, TMAO and LPS. (2) Inflammatory factors and immune inflammatory pathways such as TLR-MyD88 and NF-κB. (3) Neurotransmitter 5-HT. In the process of reviewing the studies, we found the following problems. (1) In basic researches on the relationship between FMT and CNS diseases, there are relatively few studies involving the autonomic nervous system pathway. (2) Clinical trial studies have shown that FMT improves the severity of patients’ symptoms and may be a promising treatment for a variety of neurological diseases. (3) The improvement of clinical efficacy is closely related to the choice of donor, especially emphasizing that FMT from healthy and young donors may be the key to the improvement of neurological diseases. However, there are common challenges in current research on FMT, such as the scientific and rigorous design of FMT clinical trials, including whether antibiotics are used before transplantation or different antibiotics are used, as well as different FMT processes, different donors, different functional analysis methods of gut microbiota, and the duration of FMT effect. Besides, the safety of FMT should be better elucidated, especially weighing the relationship between the therapeutic benefits and potential risks of FMT carefully. It is worth mentioning that the clinical development of FMT even exceeds its basic research. Science and TIME rated FMT as one of the top 10 breakthroughs in the field of biomedicine in 2013. FMT therapy has great potential in the treatment of nervous system diseases, is expected to open up a new situation in the medical field, and may become an innovative weapon in the medical field.

Ankylosing spondylitis (AS) combined with lower cervical fracture is often categorized into unstable fracture, with a high incidence of neurological injury and a high rate of disability and morbidity. As factors such as shoulder occlusion may affect the accuracy of X-ray imaging diagnosis, it is often easily misdiagnosed at the primary diagnosis. Non-operative treatment has complications such as bone nonunion and the possibility of secondary neurological damage, while the timing, access and choice of surgical treatment are still controversial. Currently, there are no clinical practice guidelines for the treatment of AS combined with lower cervical fracture with or without dislocation. To this end, the Spinal Trauma Group of Orthopedics Branch of Chinese Medical Doctor Association organized experts to formulate Clinical guidelines for the treatment of ankylosing spondylitis combined with lower cervical fracture in adults ( version 2024) in accordance with the principles of evidence-based medicine, scientificity and practicality, in which 11 recommendations were put forward in terms of the diagnosis, imaging evaluation, typing and treatment, etc, to provide guidance for the diagnosis and treatment of AS combined with lower cervical fracture.

By introducing the connotation of deductive teaching and analyzing the significance of early contact clinical course of "doctor-patient communication", the author reinterpreted the educational concept of deductive teaching combined with the characteristics of medical humanities, and reformed the early contact clinical course of "doctor-patient communication" by introducing deductive teaching method. Taking the early contact clinical course of "doctor-patient communication" of a medical university as an example, the teaching framework was designed to share experience from three aspects: teaching content, teaching process and teaching effect evaluation. At the same time, combined with the characteristics of the course and the needs of students, found out the problems encountered in the teaching process, and put forward constructive opinions and strategies, in order to provide theoretical and practical reference for the teaching of medical humanities course.

Objective:To establish and validate a gallstones classification method based on deep learning.Methods:A total of 618 gallstones samples were collected from East Hospital Affiliated to Tongji University, and 1 023 high-definition cross-sectional gallstones profile images were captured to construct a cross-sectional gallstones profile image dataset. Based on the traditional eight-category gallstones classification method, a lightweight network model, MobileNet V3, was trained using deep learning and transfer learning methods. The classification performance of MobileNet was evaluated using a confusion matrix with metrics such as accuracy rate, precision rate, F1 score, and recall rate. The MobileNet V3 was improved and further validated using accuracy and loss values.Results:The accuracy rate (94.17%), precision rate (94.03%), F1 score (92.96%) and recall rate (92.99%) of the improved MobileNet V3 model were better than other networks. The improved MobileNet V3 model achieved the highest accuracy rate (94.17%) in gallstones profile classification and was validated by the test set. The confusion matrix showed a weighted average of accuracy rate (92.0%), precision rate (92.6%), and F1 score (92.2%) for each category of gallstones.Conclusions:Based on deep learning, a high-accuracy gallstones classification method is proposed, which provides a new idea for the intelligent identification of gallstones.

Objective To explore the core drugs and antidepressant molecular mechanisms of Chinese herbal compound in the clinical treatment of depression.Methods Relevant literature was searched through CNKI,Wanfang and VIP to screen and sort out the Chinese Herbal compounds used in clinical practice.By analyzing the frequency of medication and the association rules of Chinese medicines,Bupleuri Radix,Paeoniae Radix Alba,Curcumae Radix were determined as the core Chinese medicinals for the treatment of depression.The active components and target proteins of these drugs were screened by TCMSP database,and depression-related targets were obtained from TTD,OMIM,DrugBank and GeneCards.Protein-protein interaction(PPI)network and'core drug-active ingredient-intersection target'network were constructed by STRING and Cytoscape.Gene Ontology(GO)enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis were performed by Metascape.Molecular docking analysis used RCSB database,AutoDockTools and PyMOL to verify the binding activity of key active components with core targets.Results The drugs for the treatment of depression in clinical Chinese herbal compound prescriptions were mainly cold,sweet,bitter and pungent,and the liver meridian was the first.Bupleuri Radix,Paeoniae Radix Alba,Curcumae Radix had the highest frequency of medication,and the combination of"Paeoniae Radix Alba-Bupleuri Radix"and"Curcumae Radix-Paeoniae Radix Alba-Bupleuri Radix"had the highest support.In the PPI network,SLC6A4,AKT1 and CHRNA4 were the key targets.GO enrichment analysis results showed that core drugs play a role in synaptic membranes and other parts through biological processes such as chemical synaptic transmission,as well as molecular functions such as G protein-coupled receptor activity.KEGG pathway enrichment analysis results showed that core drugs may treat depression through neuroactive ligand-receptor interaction,calcium signaling pathway and other pathways.Molecular docking confirmed that there was a strong binding activity between the key active ingredients and the core target.Conclusion Bupleuri Radix,Paeoniae Radix Alba,Curcumae Radix are the core drugs for clinical treatment of depression,which can treat depression through multi-component,multi-target and multi-pathway.

Objective To investigate the dynamic expression with the time change of N6-methyladenosine(m6A)methylation-related factors in the repair process of skeletal muscle injury and its mechanism in the inflammatory response of macrophage in the injure process.Methods In vivo mice models of BaCl2 injury in the gastrocnemius were established.Four mice per group in the control group and injury group.Gastrocnemius tissues were harvested at day 1,3,5,7,and 9 after injury for experiments.Primary gastrocnemius muscle tissue cells,muscle satellite cells,muscle cells,and cell line C2C12 cells were treated with dexamethasone(DEX,50 μmol/L)to mimic injury.Lipopolysaccharide(LPS,100 μg/L)induced RAW264.7 cell lines to mimic the inflammatory response after skeletal muscle injury,and STM2457(30 μmol/L)was added to inhibit the effect of methyltransferase 3(Mettl3)before LPS treatment.The expression of m6A methylation-related factors(Writers,Erasers,Readers)and inflammation factors were detected by Real-time PCR and Western blotting.Results The muscle fibers were dissolved and then gradually repaired with the extension of injury time,the number of monocytes/macrophages increased first and then decreased,and the Pax7 mRNA level increased first and then decreased with the change of injury time.Compared with the control group,the mRNA and protein levels of m6A methylation-related factors in gastrocnemius did not change significantly on the injury-1 day.However,they were significantly increased on the injury-3 days compared with the control group(P＜0.05),and then obviously decreased on the injury-5 days group compared with the injury-3 days group(P＜0.05).Compared with the control group,they were no significant differences on the injury-7 days group and-9 days group.In vitro DEX decreased the mRNA levels of m6A methyltransferase factors in primary muscle satellite cells and C2C12 cells and increased the mRNA expression level of methylation-recognition enzyme factors(P＜0.05).The mRNA levels of m6A methylation-related factors increased significantly in skeletal muscle tissue cells and myocytes after DEX treatment(P＜0.05).After LPS treatment,the mRNA and protein expression levels of m6A methylation-related factors and the mRNA expression levels of inflammatory factors interleukin(IL)-6 and IL-1β in macrophages increased significantly(P＜0.05),while the levels of IL-6 and IL-1β mRNA in macrophages decreased significantly when the Mettl3 was inhibited(P＜0.05).Conclusion m6A methylation-related factors primarily is activated in the damaged muscle cells and inflammation response of macrophages.Inhibition of m6A methyltransferase can reduce the inflammatory response of macrophages.