Periodontitis is a chronic inflammatory disease. The heterotopic colonization of periodontal pathogens results in the development of several systemic diseases. Porphyromonas gingivalis (P. gingivalis), a key pathogen for periodontitis, has been linked to the development of various cancers, such as oral squamous cell carcinoma (OSCC), lung cancer, esophageal cancer, pancreatic cancer, colorectal cancer, cervical cancer, and prostate cancer. P. gingivalis promote the progression of tumor through various mechanisms, P. gingivalis regulates proteins targeting cell cycle and apoptosis to promote proliferation of tumor cells directly, enhances tumor stemness by upregulating the expression of cluster of differentiation 44 (CD44) and cluster of differentiation 133 (CD133), activates inflammasome and p38/c-Jun N-terminal kinase 1(JNK) pathways, regulates tumor-associated neutrophil (TAN) polarization to remodel the tumor microenvironment, regulates epithelial-mesenchymal transition (EMT) to promote tumor metastasis, remodel macrophage function to evade host immune response, and regulates multi-communicating with symbiotic bacteria. In addition, P. gingivalis accelerates the progression of esophageal cancer, pancreatic cancer, colorectal cancer, and prostate cancer by promoting cell proliferation, inhibiting apoptosis, inducing chronic inflammation, and escaping immunity. However, the oral microbiome is a complex system, whether the interactions between oral bacteria affect tumor progression needs to be further investigated.

ObjectiveTo analyze the epidemiological characteristics of 8 major respiratory pathogens in influenza-like illness (ILI) cases with acute respiratory infections at fever clinics in Huangpu District, Shanghai from 2015 to 2024, and to provide a scientific basis for the prevention and treatment of respiratory diseases. MethodsA retrospective study was conducted in Huangpu District. Individuals meeting the case definition of ILI from 2015 to 2024 was registered. Their nasopharyngeal swabs were collected for pathogen detection. A total of 8 respiratory viruses were tested, including Influenza A virus (Flu A), Influenza B virus (Flu B), adenovirus (ADV), enterovirus/human rhinovirus (EV/HRV), human parainfluenza virus (HPIV), human coronavirus (HCoV), respiratory syncytial virus (RSV), and human metapneumovirus (HMPV). ResultsFrom 2015 to 2019, a total of 344 ILI cases were tested, of which 192 out of 344 cases (55.81%) were tested positive for single respiratory pathogen. From 2023 to 2024, 1 557 ILI cases were tested, with 572 out of 1 557 cases (36.74%) being positive for single pathogen. From 2023 to 2024, the positive rate of single pathogen in ILI cases was significantly lower than that in 2015‒2019 (χ²=42.66, P<0.001). Specifically, the positive rate of Flu A (χ²=74.43, P<0.001) decreased, while that of HPIV (χ²=8.66, P=0.003) increased, both with statistically significant differences. According to the seasonal pattern, the epidemic intensity of Flu A decreased in summer, while that of HPIV increased in summer and autumn. Demographic results showed statistically significant differences in the positive rates of EV/HRV between genders (χ²=22.38, P<0.001), with males exhibiting a higher positive rate than females. No statistically significant differences were identified in the positive rates of single pathogen among different age groups (χ²=4.42, P=0.110). Nevertheless, statistically significant differences were noted when comparing the positive rates of EV/HRV, Flu A, Flu B and HPIV across different age groups (P<0.05). EV/HRV was more commonly detected in the 15‒<25 age group (10.93%), while Flu A and HPIV had the highest positive rates in the ≥60 age group (21.24% and 4.77%). Flu B had the highest positive rate in the 25‒<60 age group (11.26%). 52.63% of cases with co-infections occurred during winter, with the primary pathogens involved being EV/HRV (9 cases) and HCoV (6 cases). The most prevalent combination of co-infection was Flu A with EV/HRV. ConclusionThe prevalence of respiratory pathogens among ILI cases from 2023 to 2024 exhibited notable fluctuations compared to that from 2015 to 2019. Therefore, influenza surveillance should be strengthened, and attention should also be paid to the prevalence of respiratory pathogens such as HPIV. These findings have profound implications for future research, surveillance, vaccine planning, and public health policy making.

Objective To explore the effectiveness of the generative large language model MedGo in nursing decision-making for elderly patients with multimorbidity. Methods A quasi-randomized controlled trial study was conducted involving 6 junior nurses, 6 senior nurses and the MedGo model from January 1, 2025 to March 31, 2025 at the Emergency Internal Medicine Ward of Shanghai East Hospital Affiliated to Tongji University. Clinical data of 120 elderly patients with multimorbidity were analyzed to compare the performance of the three groups in four tasks (nursing diagnosis assessment, nursing intervention formulation, complication identification, and complication prevention) from three evaluation dimensions: decision-making time consumption, decision accuracy, and decision-making quality. Results In terms of decision-making time, the senior nurse group completed all four tasks faster than the junior nurse group (P＜0.01), and the MedGo group completed all four tasks faster than the junior nurse group (P＜0.001) and the senior nurse group (P＜0.001). In terms of decision-making accuracy, senior nurse group scored higher than junior nurse group in all four tasks (P＜0.001), while the MedGo group outperformed the senior nurse group only in complication identification (P＜0.001). In terms of decision-making quality, the MedGo group scored higher than junior nurse group (P＜0.001) and senior nurse group (P＜0.001) in all four tasks. Conclusions The MedGo model demonstrates advantages of high efficiency, accuracy, and quality in nursing decision-making for elderly patients with multimorbidity; senior nurses outperform junior nurses in decision-making, providing diverse references for clinical nursing decision-making.

Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid medicinal chemists in optimizing molecular structures. Nonetheless, they also form a major source of prediction error in structure-activity relationship (SAR) models. To date, several studies have demonstrated that deep neural networks based on molecular images or graphs might need to be improved further in predicting the potency of ACs. In this paper, we integrated the triplet loss in face recognition with pre-training strategy to develop a prediction model ACtriplet, tailored for ACs. Through extensive comparison with multiple baseline models on 30 benchmark datasets, the results showed that ACtriplet was significantly better than those deep learning (DL) models without pre-training. In addition, we explored the effect of pre-training on data representation. Finally, the case study demonstrated that our model's interpretability module could explain the prediction results reasonably. In the dilemma that the amount of data could not be increased rapidly, this innovative framework would better make use of the existing data, which would propel the potential of DL in the early stage of drug discovery and optimization.

OBJECTIVE: To investigate the current status of long-term quality of life in patients with severe acute pancreatitis (SAP) who have been cured and discharged, and to analyze the influencing factors affecting long-term quality of life in SAP cured patients after discharge. METHODS: A retrospective collection was conducted. Patients who were received standardized treatment before being cured and discharged from the hospital admitted to the first department of critical care medcine of the Second Affiliated Hospital of Anhui Medical University from January 2017 to December 2023 were enrolled. According to the 36-item short form health survey scale (SF-36) score, patients were divided into high score group (high quality of life, the top 50% of patients with total SF-36 score) and low score group (low quality of life, the bottom 50% of patients with total SF-36 score). The gender, age, history of hypertension and diabetes, etiology of pancreatitis, acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), CT severity index (CTSI), laboratory indicators such as C-reactive protein (CRP), procalcitonin (PCT), blood glucose, and triglycerides upon admission, use of vasoactive drugs, non-invasive/high-flow ventilation, invasive ventilation, retroperitoneal puncture and drainage, open pancreatic surgery treatment and secondary infection during hospitalization were collected, as well as the retention of abdominal drainage tubes at discharge from hospital. Distribute follow-up questionnaires or telephone follow-up surveys through WeChat and Question Star programs to investigate the pancreatic secretion function, chronic abdominal pain, and recurrence of pancreatitis of patients after discharge. Multivariable Logistic regression was used to analyze the relevant factors affecting the long-term quality of life of cured patients with SAP. RESULTS: A total of 86 patients were ultimately enrolled. There were 43 patients in both the high and low score groups. Among 86 patients, 20 experienced acute pancreatitis recurrence, with a recurrence rate of 23.26%. Twenty-two (25.58%) experienced chronic abdominal pain after discharge, and 5 patients (5.81%) needed medication to relieve pain. Thirty-three patients (38.37%) had pancreatic exocrine dysfunction after discharge, characterized by abdominal distension, constipation or diarrhea. Twenty-two patients (25.58%) suffered from pancreatic endocrine dysfunction, and were diagnosed with diabetes. Univariate analysis showed that compared with the high score group, the low score group had more patients with hypertension, initial renal dysfunction, initial severe metabolic acidosis, initial serum calcium < 2.0 mmol/L, blood glucose > 11.1 mmol/L and cultured Gram positive bacteria (from blood/body fluid/pancreatic necrotic tissue) during treatment (48.84% vs. 16.28%, 60.47% vs. 32.56%, 18.60% vs. 4.65%, 88.37% vs. 62.79%, 55.81% vs. 30.23%, 34.88% vs. 13.95%), had higher CTSI score (6.60±1.61 vs. 5.77±1.32), lower hemoglobin level at discharge (g/L: 102.30±18.78 vs. 110.72±16.68), and a lower proportion of etiological interventions after discharge (34.88% vs. 67.44%), the differences were statistically significant (all P < 0.05). Multivariate Logistic regression analysis showed that hypertension [odds ratio (OR) = 4.814, 95% confidence interval (95%CI) was 1.196-19.378], initial serum calcium < 2.0 mmol/L (OR = 6.688, 95%CI was 1.321-33.873) and initial blood glucose > 11.1 mmol/L (OR = 6.473, 95%CI was 1.399-29.950) were risk factors for long-term quality of life in cured SAP patients (all P < 0.05), while post discharge prophylactic intervention was a protective factor for long-term quality of life (OR = 0.092, 95%CI was 0.020-0.425, P < 0.01). CONCLUSIONS Cured SAP patients have varying degrees of impaired secretion function and the possibility of recurrence of acute pancreatitis. Hypertension, initial serum calcium < 2.0 mmol/L and blood glucose > 11.1 mmol/L are independent influencing factors for low long-term quality of life in cured SAP patients. Prevention and intervention targeting the etiology of pancreatitis after discharge can improve the long-term quality of life of cured SAP patients.
Humans ; Quality of Life ; Retrospective Studies ; Pancreatitis/therapy* ; Patient Discharge ; Male ; Female ; Middle Aged ; APACHE ; Adult ; Acute Disease ; Aged

Extracorporeal membrane oxygenation (ECMO) is a key continuous extracorporeal life support technology that can partially or completely replace a patient's cardiopulmonary function, thereby winning valuable time for the diagnosis and treatment of the primary disease. With the widespread application of ECMO, the need for transport has increased. However, during transfers, the standard heater unit is often large and inconvenient to carry, while alternative warming measures tend to be ineffective. This frequently leads to complications such as hypothermia or the inability to maintain body temperature, which can seriously affect the patient's prognosis. In response to this challenge, the medical and nursing staff of the critical care medicine department at Zhongda Hospital Affiliated to Southeast University jointly designed an insulation device for ECMO transport pipelines. The device was successfully granted a National Utility Model Patent of China (patent number: ZL 2021 2 0653569.3). It primarily consists of key components such as a heating pad, velcro straps, a cover layer, a backing layer, an electric heating layer, and a wiring plug. Its advantages include portability, the ability to effectively wrap around and warm the ECMO circuit during transit, and a reduction in the incidence of hypothermia-related complications. Furthermore, its transparent material design allows for real-time monitoring of the ECMO system's status, making it both economical and practical.
Extracorporeal Membrane Oxygenation/instrumentation* ; Humans ; Equipment Design

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional

BACKGROUND: Electroacupuncture (EA) may affect the severity of hot flashes (HFs) associated with natural menopause and provide additional benefits for postmenopausal women. However, the evidence for its effectiveness in the management of early postmenopausal HFs remains inadequately understood. OBJECTIVE: We designed this trial to assess the efficacy and safety of EA for relieving early postmenopausal HFs. DESIGN, SETTING, PARTICIPANTS AND INTERVENTIONS: This randomized sham-controlled trial involved 72 women with HFs. The participants were divided equally into the intervention and control groups. The intervention group was treated with EA, while the control group was treated with sham acupuncture. The main acupoints used were Hegu (LI4), Guanyuan (RN4), Sanyinjiao (SP6), Taixi (KI3), Fuliu (KI7) and Shenshu (BL23). All participants received 18 treatment sessions, distributed across a 6-week period. The treatment was administered on three occasions per week, adhering to a fixed weekday schedule (Monday, Wednesday, Friday or Tuesday, Thursday, Saturday) with a minimum interval of one day between sessions. Each patient received a 12-week follow-up. MAIN OUTCOME MEASURES: The HF score was the primary outcome. Participants documented the frequency and severity of HFs in a 7-day symptom diary, which provided data for calculating the HF score. Secondary outcomes were the Menopause Rating Scale (MRS), Menopause-Specific Quality of Life Questionnaire (MENQOL), Pittsburgh Sleep Quality Index (PSQI) and Traditional Chinese Medicine Syndrome Score Scale (TCMSSS), as well as estradiol (E₂), luteinizing hormone (LH) and follicle-stimulating hormone (FSH) levels. RESULTS: Both groups demonstrated significant reductions in HF scores after the treatment and during the follow-up (P < 0.001). Immediately after completion of the 6-week treatment cycle and at 12 weeks post-intervention, the HF scores were similar in both groups. At week 6, the intervention group showed significantly greater improvements in MRS, MENQOL (vasomotor, psychosocial, and physical), PSQI and TCMSSS scores (P < 0.05). The improvements in the MENQOL (vasomotor, and psychosocial) and PSQI total scores persisted through the follow-up (P < 0.05). However, the results showed no significant inter- or intragroup differences in sexual scores on the MENQOL (P > 0.05). EA did not significantly decrease E₂, LH or FSH levels compared to placebo. The incidence of adverse events was similar in both groups. CONCLUSION: EA does not significantly improve HFs in early postmenopausal patients. However, it enhances the quality of sleep and decreases menopausal symptoms across vasomotor, psychosocial and physical domains. TRIAL REGISTRATION Chinese Clinical Trial Registry (http://www.chictr.org.cn); Trial ID: ChiCTR2300072002. Please cite this article as: Wang HX, Yu XT, Hu J, Chen JJ, Mei YT, Chen YF. Electroacupuncture for hot flashes in early menopause: A randomized sham-controlled trial. J Integr Med. 2025; 23(5):519-527.
Humans ; Female ; Electroacupuncture ; Hot Flashes/therapy* ; Middle Aged ; Acupuncture Points ; Quality of Life ; Menopause ; Treatment Outcome ; Adult

Benzylisoquinoline alkaloids (BIAs) are a structurally diverse group of plant metabolites renowned for their pharmacological properties. However, sustainable sources for these compounds remain limited. Consequently, researchers are focusing on elucidating BIA biosynthetic pathways and genes to explore alternative sources using synthetic biology approaches. CYP80B, a family of cytochrome P450 (CYP450) enzymes, plays a crucial role in BIA biosynthesis. Previously reported CYP80Bs are known to catalyze the 3'-hydroxylation of (S)-N-methylcoclaurine, with the N-methyl group essential for catalytic activity. In this study, we successfully cloned a full-length CYP80B gene (StCYP80B) from Stephania tetrandra (S. tetrandra) and identified its function using a yeast heterologous expression system. Both in vivo yeast feeding and in vitro enzyme analysis demonstrated that StCYP80B could catalyze N-methylcoclaurine and coclaurine into their respective 3'-hydroxylated products. Notably, StCYP80B exhibited an expanded substrate selectivity compared to previously reported wild-type CYP80Bs, as it did not require an N-methyl group for hydroxylase activity. Furthermore, StCYP80B displayed a clear preference for the (S)-configuration. Co-expression of StCYP80B with the CYP450 reductases (CPRs, StCPR1, and StCPR2), also cloned from S. tetrandra, significantly enhanced the catalytic activity towards (S)-coclaurine. Site-directed mutagenesis of StCYP80B revealed that the residue H205 is crucial for coclaurine catalysis. Additionally, StCYP80B exhibited tissue-specific expression in plants. This study provides new genetic resources for the biosynthesis of BIAs and further elucidates their synthetic pathway in natural plant systems.
Cytochrome P-450 Enzyme System/chemistry* ; Benzylisoquinolines/chemistry* ; Hydroxylation ; Plant Proteins/chemistry* ; Alkaloids/metabolism* ; Stephania tetrandra/genetics*

OBJECTIVE: To investigate the associations between eight serum per- and polyfluoroalkyl substances (PFASs) and regional fat depots, we analyzed the data from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 cycles. METHODS: Multiple linear regression models were developed to explore the associations between serum PFAS concentrations and six fat compositions along with a fat distribution score created by summing the concentrations of the six fat compositions. The associations between structurally grouped PFASs and fat distribution were assessed, and a prediction model was developed to estimate the ability of PFAS exposure to predict obesity risk. RESULTS: Among females aged 39-59 years, trunk fat mass was positively associated with perfluorooctane sulfonate (PFOS). Higher concentrations of PFOS, perfluorohexane sulfonate (PFHxS), perfluorodecanoate (PFDeA), perfluorononanoate (PFNA), and n-perfluorooctanoate (n-PFOA) were linked to greater visceral adipose tissue in this group. In men, exposure to total perfluoroalkane sulfonates (PFSAs) and long-chain PFSAs was associated with reductions in abdominal fat, while higher abdominal fat in women aged 39-59 years was associated with short-chain PFSAs. The prediction model demonstrated high accuracy, with an area under the curve (AUC) of 0.9925 for predicting obesity risk. CONCLUSION PFAS exposure is associated with regional fat distribution, with varying effects based on age, sex, and PFAS structure. The findings highlight the potential role of PFAS exposure in influencing fat depots and obesity risk, with significant implications for public health. The prediction model provides a highly accurate tool for assessing obesity risk related to PFAS exposure.
Humans ; Fluorocarbons/blood* ; Female ; Adult ; Middle Aged ; Male ; Environmental Pollutants/blood* ; Abdominal Fat ; Nutrition Surveys ; Alkanesulfonic Acids/blood* ; Obesity ; Environmental Exposure