Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that severely affects the health of the elderly, marked by its incurability, high prevalence, and extended latency period. The current approach to AD prevention and treatment emphasizes early detection and intervention, particularly during the pre-AD stage of mild cognitive impairment (MCI), which provides an optimal “window of opportunity” for intervention. Clinical detection methods for MCI, such as cerebrospinal fluid monitoring, genetic testing, and imaging diagnostics, are invasive and costly, limiting their broad clinical application. Speech, as a vital cognitive output, offers a new perspective and tool for computer-assisted analysis and screening of cognitive decline. This is because elderly individuals with cognitive decline exhibit distinct characteristics in semantic and audio information, such as reduced lexical richness, decreased speech coherence and conciseness, and declines in speech rate, voice rhythm, and hesitation rates. The objective presence of these semantic and audio characteristics lays the groundwork for computer-based screening of cognitive decline. Speech information is primarily sourced from databases or collected through tasks involving spontaneous speech, semantic fluency, and reading, followed by analysis using computer models. Spontaneous language tasks include dialogues/interviews, event descriptions, narrative recall, and picture descriptions. Semantic fluency tasks assess controlled retrieval of vocabulary items, requiring participants to extract information at the word level during lexical search. Reading tasks involve participants reading a passage aloud. Summarizing past research, the speech characteristics of the elderly can be divided into two major categories: semantic information and audio information. Semantic information focuses on the meaning of speech across different tasks, highlighting differences in vocabulary and text content in cognitive impairment. Overall, discourse pragmatic disorders in AD can be studied along three dimensions: cohesion, coherence, and conciseness. Cohesion mainly examines the use of vocabulary by participants, with a reduction in the use of nouns, pronouns, verbs, and adjectives in AD patients. Coherence assesses the ability of participants to maintain topics, with a decrease in the number of subordinate clauses in AD patients. Conciseness evaluates the information density of participants, with AD patients producing shorter texts with less information compared to normal elderly individuals. Audio information focuses on acoustic features that are difficult for the human ear to detect. There is a significant degradation in temporal parameters in the later stages of cognitive impairment; AD patients require more time to read the same paragraph, have longer vocalization times, and produce more pauses or silent parts in their spontaneous speech signals compared to normal individuals. Researchers have extracted audio and speech features, developing independent systems for each set of features, achieving an accuracy rate of 82% for both, which increases to 86% when both types of features are combined, demonstrating the advantage of integrating audio and speech information. Currently, deep learning and machine learning are the main methods used for information analysis. The overall diagnostic accuracy rate for AD exceeds 80%, and the diagnostic accuracy rate for MCI also exceeds 80%, indicating significant potential. Deep learning techniques require substantial data support, necessitating future expansion of database scale and continuous algorithm upgrades to transition from laboratory research to practical product implementation.

With the continuous development of society, a large number of new chemicals are continuously emerging, which presents a challenge to current risk assessment and safety management of chemicals. Traditional toxicology research methods have certain limitations in quickly, efficiently, and accurately assessing the toxicity of many chemicals, and cannot meet the actual needs. In response to this challenge, computational toxicology that use mathematical and computer models to achieve the prediction of chemical toxicity has emerged. In the meantime, as researchers increasingly pay attention to understanding the interaction mechanisms between exogenous chemical substances and the body from the system level, and multiomics technologies develop rapidly such as genomics, transcriptomics, proteomics, and metabolomics, huge amounts of data have been generated, providing rich information resources for studying the interactions between chemical substances and biological molecules. System toxicology and network toxicology have also developed accordingly. Of these, network toxicology can integrate these multiomics data to construct biomolecular networks, and then quickly predict the key toxicological targets and pathways of chemicals at the molecular level. This paper outlined the concept and development of network toxicology, summarized the main methods and supporting tools of network toxicology research, expounded the application status of network toxicology in studying potential toxicity of exogenous chemicals such as agricultural chemicals, environmental pollutants, industrial chemicals, and foodborne chemicals, and analyzed the development prospects and limitations of network toxicology research. This paper aimed to provide a reference for the application of network toxicology in other fields.

In recent years, with the improvement of people's awareness of physical examination and the more accurate detection equipment, the detection rate of pulmonary nodules is getting higher and higher. Surgical resection is the first choice for the treatment of malignant pulmonary nodules, but multiple pulmonary nodules, nodules in complex areas and those with surgical contraindications are not suitable for surgery. As an effective, less invasive and low-cost treatment, ablation has developed rapidly in the treatment of multiple pulmonary nodules. This article introduces the progress of several common ablation techniques (radiofrequency ablation, microwave ablation, cryoablation) in the treatment of multiple pulmonary nodules, the indications and contraindications of ablation techniques, the efficacy evaluation and complications after ablation therapy, and the prospects of ablation techniques in the treatment of multiple pulmonary nodules.

In recent years, the deep integration of artificial intelligence (AI) into medical education has created new opportunities for teaching Biochemistry and Molecular Biology, while also offering innovative solutions to the pedagogical challenges associated with protein structure and function. Focusing on the case of anaplastic lymphoma kinase (ALK) gene mutations in non-small-cell lung cancer (NSCLC), this study integrates AI into case-based learning (CBL) to develop an AI-CBL hybrid teaching model. This model features an intelligent case-generation system that dynamically constructs ALK mutation scenarios using real-world clinical data, closely linking molecular biology concepts with clinical applications. It incorporates AI-powered protein structure prediction tools to accurately visualize the three-dimensional structures of both wild-type and mutant ALK proteins, dynamically simulating functional abnormalities resulting from conformational changes. Additionally, a virtual simulation platform replicates the ALK gene detection workflow, bridging theoretical knowledge with practical skills. As a result, a multidimensional teaching system is established—driven by clinical cases and integrating molecular structural analysis with experimental validation. Teaching outcomes indicate that the three-dimensional visualization, dynamic interactivity, and intelligent analytical capabilities provided by AI significantly enhance students’ understanding of molecular mechanisms, classroom engagement, and capacity for innovative research. This model establishes a coherent training pathway linking “fundamental theory-scientific research thinking-clinical practice”, offering an effective approach to addressing teaching challenges and advancing the intelligent transformation of medical education.

Lung transplantation is the ultimate therapeutic option for end-stage pulmonary diseases such as interstitial pneumonia, chronic obstructive pulmonary disease and pneumoconiosis. Currently, the shortage of allogeneic lung donors significantly limits the opportunity for end-stage lung disease patients to receive lung transplantation. In recent years, with the rapid development of biomedical engineering technologies, especially the major breakthroughs in genetic modification and cloning, xenogeneic lung transplantation has shown important potential for clinical translation. Among them, genetically modified pigs have become the most promising xenogeneic lung source due to the close similarity of organ size and physiological characteristics to humans, and the ability to perform targeted gene knockouts (such as α-Gal antigen knockout) to reduce the occurrence of hyperacute rejection. This article focuses on the research progress of porcine xenogeneic lung transplantation, systematically reviews the latest achievements and challenges in animal experiments and human trials, and introduces the technical experience accumulated by Shenzhen Third People's Hospital in the porcine-to-monkey xenogeneic lung transplantation model, in the hope of providing practical references for future research in this field.

Objective To observe the efficacy and safety of Morinda officinalis oligosaccharides in the continuation treatment of mild and moderate depression.Methods An open,single-arm,multi-center design was adopted in our study.Adult patients with mild and moderate depression who had received acute treatment of Morinda officinalis oligosaccharides were enrolled and continue to receive Morinda officinalis oligosaccharides capsules for 24 weeks,the dose remained unchanged during continuation treatment.The remission rate,recurrence rate,recurrence time,and the change from baseline to endpoint of Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Clinical Global Impression-Severity(CGI-S)and Arizona Sexual Experience Scale(ASEX)were evaluated.The incidence of treatment-related adverse events was reported.Results The scores of HAMD-17 at baseline and after treatment were 6.60±1.87 and 5.85±4.18,scores of HAMA were 6.36±3.02 and 4.93±3.09,scores of CGI-S were 1.49±0.56 and 1.29±0.81,scores of ASEX were 15.92±4.72 and 15.57±5.26,with significant difference(P＜0.05).After continuation treatment,the remission rate was 54.59％(202 cases/370 cases),and the recurrence rate was 6.49％(24 cases/370 cases),the recurrence time was(64.67±42.47)days.The incidence of treatment-related adverse events was 15.35％(64 cases/417 cases).Conclusion Morinda officinalis oligosaccharides capsules can be effectively used for the continuation treatment of mild and moderate depression,and are well tolerated and safe.

Objective To explore the molecular mechanism of Gualou Guizhi decoction which regulates the interferon regulator factor 5(IRF5)signaling pathway to inhibit M1 type microglia activation and reduce the inflammatory response to protect damaged nerve cells.Methods Microglia(BV2)cells were randomly divided into BV2-control,BV2-model,BV2-experimental-L,-M,-H groups.The BV2-control group was given routine culture;the BV2-model group used 100 ng·mL-1 lipopolysaccharide(LPS)to stimulate BV2 which establish an inflammatory model;the BV2-experimental-L,-M,-H groups were cultured in 50,100,200 μg·mL-1 GLGZD and 100 ng·mL-1 LPS.The HT22 cells were divided into the HT-22-blank group,HT-22-model group,HT-22-control group and HT-22 experimental group.HT-22-blank group were conventional culture;HT-22-model group were oxygen glucose deprivation was performed for 2 h,then the complete medium was replaced for 24 h;HT-22-control group were after 2 h of oxygen glucose deprivation,the 100 ng·mL-1 LPS conditioned medium was replaced and incubated for 24 h;HT-22-experimental group were after 2 h of oxygen glucose deprivation,the 200 μg·mL-1 GLGZD conditioned medium was added for 24 h.Interleukin-12(IL-12)and IL-23 were detected by enzyme-linked immunosorbent assay(ELISA);the protein of IRF5,cluster differentiation 16(CD1 6)and MHC class Ⅱ(MHC-Ⅱ)was detected by Western blot;the expression of the synaptic marker protein class Ⅲ β-Tubulin(Tuj-1)was observed by immunofluorescence.Results IL-12 contents in the BV2-control,BV2-model and BV2-experimental-L,-M,-H groups were(2.62±1.02),(10.67±3.22),(6.87±1.61),(3.96±1.22)and(3.36±1.04)pg·mL-1;IL-23 contents were(20.40±2.04),(77.08±3.25),(76.28±3.75),(63.96±4.94)and(54.48±3.34)pg·mL-1;relative expression levels of IRF5 protein were 0.80±0.41,2.22±0.69,1.11±0.11,0.92±0.39 and 0.65±0.29;relative expression levels of CD16 protein were 0.69±0.45,1.91±0.52,1.42±0.22,1.04±0.15 and 0.67±0.30;relative expression levels of MHC-Ⅱ protein were 0.89±0.27,1.96±0.19,1.34±0.38,1.15±0.19 and 0.68±0.24.BV2-experimental-M,-H groups were compared with the BV2-model group,the differences were statistically significant(all P＜0.05).The Tuj-1 protein expression levels were 28.85±6.69,14.44±1.98,7.75±1.12 and 20.05±3.54,determined in the HT22-blank,HT22-model,HT22-control and HT22-experimental groups.The HT22-experimental group was compared with the HT22-control group,the difference was statistically significant(P＜0.05).Conclusion GLGZD may reduces the activation of microglia M1 phenotype through IRF5 signaling pathway,and then inhibits inflammatory response to protect damaged nerve cells.

Celastrol and wilforlide A are the main active triterpenoids of the traditional Chinese medicine Lei Gong Teng, which have anti-tumour, anti-inflammatory and immunosuppressive activities, and are the material basis for the clinical efficacy of Lei Gong Teng-related Chinese medicinal preparations. By analysing the biosynthetic pathway of active ingredients, optimizing genetic elements and utilizing "cell factory" to produce triterpenoids heterologously will be an effective way to obtain from Tripterygium wilfordii in the future in a "low-cost and high-efficient" manner. CYP712Ks are the first cytochrome P450s involved in the skeleton modification of friedelane-type and oleanane-type triterpenoids in T. wilfordii, and they can catalyse the generation of polpunonic acid from friedelin and 3-epi-katonic acid from β-amyrin by carboxylation at C-29 position. In this study, four multifunctional TwCYP712K1/2/3/5 were used to clarify the catalytic function and substrate selection preference using in vivo functional characterization in Saccharomyces cerevisiae. The spatial structure of the protein-substrate binding was clarified through homology modeling and molecular docking, and then the differential amino acids in the active pocket of the protein were mutated to clarify the crucial amino acids determining the catalytic function and the selection of substrate structure. A total of 63 mutant elements were constructed, and the amino acid sites affecting the carboxylation function of TwCYP712Ks were analyzed. In particular, the key amino acids affecting the substrate selectivity of TwCYP712K2 towards oleanane-type and friedelane-type triterpenoids were revealed and the TwCYP712K2^F127I and TwCYP712K2^A227T mutants would result in a reversal of the product ratio. In conclusion, four proteins of TwCYP712Ks were semi-rationally designed by homologous protein alignment and mutual mutation to elucidate multiple amino acid sites determining the catalytic function of the proteins, and a series of activity-enhancing or altering mutants were obtained, which provide abundant catalytic elements for the biosynthesis of active triterpenoids from T. wilfordii, and the mechanism of carboxylation in the C-29 position was initially elucidated.

Digital polymerase chain reaction(dPCR)is a PCR technology that realizes accurate quantification of single-copy nucleic acid molecules by dividing the reaction system into tens of thousands of independent PCR reaction units for single-molecule-level amplification and integrating Poisson distribution.Due to its single-copy sensitivity and accurate quantification without the need of standard curves,dPCR has been widely used in disease diagnosis.By introducing technologies such as stepped emulsification and three-dimensional imaging,dPCR has been greatly improved in terms of accuracy,multiplexability and turnaround time,significantly enhancing its performance in clinical disease diagnosis.Based on this,this paper traced the technological development history of dPCR,gave an overview of its application in detection of tumors,infections and other diseases,and further discussed the challenges and opportunities of the development of dPCR,with the aim of providing a reference for the development and utilization of dPCR in the future,and promoting the high-quality development of molecular technology in clinical testing.

Objective To study the factors affecting hospital death in elderly patients with novel coronavirus infec-tion/disease 2019(COVID-19),and to build a risk prediction model.Methods According to the diagnostic criteria of Diagnosis and Treatment Protocol for COVID-19 Infection(Trial 10th Edition).Totally 775 elderly patients(≥60 years old)diagnosed as COVID-19 infection in the emergency department and fever clinic of the First Hospital of Changsha were selected as the research objects.General data and serum biomarkers of patients were collected.After treatment,the patients'data were divided into survival group and hospital death group.Binary Logistic regres-sion was used to screen the independent influencing factors of death,and ROC curve was used to analyze the pre-dictive value of related indicators on hospital death.Results After treatment,712 patients(91.9%)survived and 63 patients(8.3%)died in hospital.Binary Logistic regression analysis showed that:≥90 years old[OR=5.065,95%CI(1.427,17.974)],type 2 diabetes mellitus[OR= 3.757,95%CI(1.649,8.559)],COPD[OR= 5.625,95%CI(2.357,13.421)],monocyte ratio[OR=0.908,95%CI(0.857,0.963)],plasma fibringen[OR=1.376,95%CI(1.053,1.800)]and lactate dehydrogenase[OR=1.005,95%CI(1.001,o1.008)]were independent factors of in-hospital death(P＜0.05).The predictive value of diabetes mellitus+COPD+age+monocyte ratio+plasma fibrinogen+lactate dehydrogenase was proved in hospital death from COVID-19 infected patients:the area under the curve(AUC)was 0.883(95%CI:0.827,0.940,P＜0.001),the critical value≥0.710 suggested the risk of death in hospital,the specificity was 0.851,the sensitivity was 0.857.Conclusions The hospital mortality of the elderly after COVID-19 infection is higher and closely related to type 2 diabetes,COPD,monocyte ratio,plasma fibrinogen and lactate dehydrogenase.