BACKGROUND:Rheumatoid arthritis is a chronic autoimmune disease.Early diagnosis is crucial for preventing disease progression and for effective treatment.Therefore,it is of significance to investigate the diagnostic characteristics and immune cell infiltration of rheumatoid arthritis. OBJECTIVE:Based on the Gene Expression Omnibus(GEO)database,to screen potential diagnostic markers of rheumatoid arthritis using machine learning algorithms and to investigate the relationship between the diagnostic characteristics of rheumatoid arthritis and immune cell infiltration in this pathology. METHODS:The gene expression datasets of synovial tissues related to rheumatoid arthritis were obtained from the GEO database.The data sets were merged using a batch effect removal method.Differential expression analysis and functional correlation analysis of genes were performed using R software.Bioinformatics analysis and three machine learning algorithms were used for the extraction of disease signature genes,and key genes related to rheumatoid arthritis were screened.Furthermore,we analyzed immune cell infiltration on all differentially expressed genes to examine the inflammatory state of rheumatoid arthritis and investigate the correlation between their diagnostic characteristics and infiltrating immune cells. RESULTS AND CONCLUSION:In both rheumatoid arthritis and normal synovial tissues,we identified 179 differentially expressed genes,with 124 genes up-regulated and 55 genes down-regulated.Enrichment analysis revealed a significant correlation between rheumatoid arthritis and immune response.Three machine learning algorithms identified LRRC15 and MICB as potential biomarkers of rheumatoid arthritis.LRRC15(area under the curve=0.964,95%confidence interval:0.924-0.992)and MICB(area under the curve=0.961,95%confidence interval:0.923-0.990)demonstrated strong diagnostic performance on the validation dataset.The infiltration of 13 types of immune cells was altered,with macrophages being the most affected.In rheumatoid arthritis,the majority of proinflammatory pathways in immune cell function were activated.Immunocorrelation analysis revealed that LRRC15 and MICB had the strongest correlation with M1 macrophages.To conclude,this study identified LRRC15 and MICB as potential diagnostic markers for rheumatoid arthritis,with strong diagnostic performance and significant correlation with immune cell infiltration.Machine learning and bioinformatics analysis deepened the understanding of immune infiltration in rheumatoid arthritis and provided new ideas for the diagnosis and treatment of rheumatoid arthritis.

Metastasis is a key cause of death in tumor patients, and a number of tumor patients have comorbid psychosomatic abnormalities and are in a state of chronic stress. Chronic stress affects the release of many kinds of hormones and neurotransmitters, such as epinephrine, norepinephrine, dopamine, glucocorticoids, cortisol, sex hormones, etc., through the hypothalamus–pituitary–adrenal axis and sympathetic nervous system. These substances can act on the β-adrenergic receptor, glucocorticoid receptor, etc., on tumor cells, immune cells, and other cells in the tumor microenvironment and promote the tumor progression and metastasis by directly enhancing the invasive and metastatic ability of tumor cells, inducing the formation of the immunosuppressive microenvironment and promoting tumor angiogenesis and other pathways. Antipsychotic drugs, β-blockers, and glucocorticoid receptor antagonists have inhibitory effects on chronic stress-mediated tumor metastasis and have achieved certain clinical efficacy. Relevant studies have been carried out on traditional Chinese medicine decoctions and monomers, which can inhibit tumor metastasis by modulating the immune microenvironment and reversing chronic stress-mediated hormonal changes. The psychological problems of tumor patients have gradually received attention, and the development of new anti-metastatic drugs based on the mechanism of action of chronic stress in promoting tumor progression and metastasis provides new ideas for the improvement of the overall efficiency of tumor prevention and treatment.

To introduce and analyze guideline terminology related to clinical question formulation, evidence retrieval and appraisal, and recommendation development.

ObjectiveTo investigate the status of tooth loss in people aged 50 and above, so as to understand their oral health status and provide scientific evidences for promoting oral health of middle-aged and elderly people. MethodsA total of 400 patients who visited the department of stomatology at Sijing Hospital in Songjiang District of Shanghai were performed oral health examinations and their information was collected according to the national epidemiological survey standards for oral health. ResultsThere were statistically significant differences in tooth loss among people aged 50 and above with different ages, educational levels, occupations, types of medical insurance and chronic diseases (P<0.05), but gender and monthly income had no statistically significant correlations with tooth loss (P>0.05). Among lifestyle factors, smoking, alcohol consumption and tea drinking had no statistically significant impacts on the number of remaining teeth (P>0.05), but toothbrushing frequency, flossing frequency, toothpick use frequency, toothbrush replacement frequency, and tooth loosening were statistically associated with the number of remaining teeth (P<0.05). Multiple linear regression analyses indicated that a total of 7 related factors including age, educational level, occupation, medical payment type, chronic disease, tooth loosening and toothpick use frequency were significantly associated with the number level of remaining teeth in individuals aged 50 and above. ConclusionAge, chronic disease, and tooth loosening were influencing factors affecting the number of teeth left in people aged 50 and above. It is recommended to strengthen oral health education and improve healthcare awareness to reduce the risk of tooth loss in people aged 50 and above.

To address the current issue of low performance in predicting the binding affinity between antigenic peptides and specific MHC class II molecules, which fails to meet clinical requirements, we proposed T5MHCII, a deep learning-based prediction model for the affinity of MHC II class molecules to peptides. The model employed the knowledge previously acquired from the protein language model ProtT5 to extract the amino acid sequences via a transfer learning approach, thereby generating high-quality characterizations. This knowledge was then integrated with the robust learning abilities of deep learning to develop a novel model with enhanced predictive capabilities. The results of the five-fold cross-validation demonstrated that the model exhibited superior performance compared to NetMHCIIpan-3.2, PUFFIN, DeepMHCII, and RPEMH, with an AUC of 0.893±0.003 and a PCC of 0.780±0.006. The leave-one-out cross-validation (LOOCV) further demonstrated that the model exhibited enhanced generalization capabilities. This study proposes a novel approach to enhance the precision of peptide-MHCII prediction in the context of limited data affinity through the application of deep learning techniques.

Objective: To develop a modified calculation formula for renal tumor volume and tumor contact surface area (CSA) based on the modeling results of 3D Slicer software, and to create a webpage of the calculation formula for use. Methods: The general information and tumor anatomical data of 98 patients who underwent partial nephrectomy during Jan.2021 and Jul.2023 in the Second Affiliated Hospital of Xi'an Jiaotong University were retrospectively analyzed.The imaging data were input into 3D Slicer software in the form of Dicom files for tumor and ipsilateral kidney modeling to obtain tumor anatomical data.The relationship between tumor anatomical parameters and tumor volume and CSA was analyzed using multifactorial linear regression.The initial modified formulas (V2, C2) and the optimized modified formulas (V3, C3) for tumor volume over CSA were established, respectively, after insignificant variables were eliminated.The mean square error (MSE) and Akaike information criterion (AIC) of the modified and traditional formulas (V1, C1) were compared, and the formula with the smallest MSE and AIC was selected as the optimal tumor volume and CSA calculation formula.The median tumor volume and CSA obtained from 3D modeling were used as the cutoff values.The optimal formula and conventional formula were applied to calculate tumor volume and CSA for all patients, and risk stratification was performed for all patients based on these cutoff values, and the perioperative indicators of patients in the upper and lower groups were compared.Finally, an online calculation tool was developed based on HTML. Results: Based on multifactorial linear regression analysis, we obtained the modified tumor volume calculation formula: V=0.382abc+2.488a+2.372b－4.146c+1.948（V2）, V=0.469abc－4.586c+13.816（V3）; the modified tumor CSA calculation formula CSA=2.469a －2.262L －19.23a+6.206b+1.212c+18.017L+1.616h－3.97h －2.185h/h －0.388（C2）, CSA=2.376a －2.144L －20.157a+5.024b+1.128c+17.578L+2.525h－2.634（C3）.Both of the modified volume formula (MSE=151.298 vs. 127.807 vs. 104.106) and modified CSA formula (MSE=309.878 vs.23.556 vs.30.388) had smaller errors compared to the conventional formula.The modified volume calculation formula showed that bleeding was more and thermal ischemia time was longer in patients with larger tumor volumes than in patients with smaller tumor volumes (P<0.05); and the modified CSA calculation formula showed that bleeding was more, surgery and thermal ischemia time were longer in patients with high CSA than in patients with low CSA (P＜0.05).Finally, V3 and C3 are selected as the best calculation formula, and a web page (https://lizihao-bot.github.io/RCC-Calculate/) was established for easy use. Conclusion: This study combined data from a medical information technology platform with numerical modeling methods to provide a faster and more accurate method to calculate the renal tumor volume and CSA.Meanwhile, a webpage version of the tool was developed to enhance its practicability.

ObjectiveTo analyze the distribution, drug resistance characteristics, and changing trends of Acinetobacter baumannii (AB) isolated from environmental surfaces and healthcare workers’ hands in a grade Ⅱ level A general hospital in Xuhui District of Shanghai from 2018 to 2023, and to provide reference for infection control in the hospital. MethodsEnvironmental samples were collected quarterly from critical surfaces and healthcare workers’ hands in the intensive care unit (ICU), geriatrics, and respiratory departments from 2018 to 2023. Clinical isolates were obtained from all patients with AB infections in ICU, geriatrics, respiratory department, rehabilitation department, infectious diseases department, emergency department, cardiology department, and orthopedics of the hospital from 2018 to 2023. Retrospective analyses were performed on AB detection rates, strain origins, resistance rates to commonly used antimicrobial agents, and resistance gene features, comparing the antimicrobial resistance between clinically isolated strains and environmentally isolated strains. ResultsFrom 2018 to 2023, a total of 1 416 samples were collected from the hospital and a total of 272 strains of AB were detected, with a positive detection rate of 19.21%. The detection rate gradually decreased year-on-year (χ²_trend=45.290, P<0.001). The majority of samples originated from patient-contacted items (34.56%, 94/272), followed by shared items (26.84%, 73/272) and healthcare worker-contacted items (15.07%, 41/272). From 2018 to 2023, the resistance rate of AB on environmental surfaces and healthcare workers’ hands to commonly tested antibiotics in the hospital ranged from 10% to 40%. The resistance rates to cefotaxime (42.52%) and piperacillin (38.58%) were relative high, while the resistance to polymyxin E (1.57%), polymyxin B (2.36%), and doxycycline (3.94%) maintained low. The annual fluctuations in resistance to cefotaxime, piperacillin, ceftriaxone, tobramycin, doxycycline, minocycline and cotrimoxazole were statistically significant (all P<0.05). There were statistically significant differences in the resistance of clinical and environmental isolates to ampicillin/sulbactam, cefepime, ceftazidime, subamphetamine, meropenem, piperacillin, aztreonam, gentamicin, tobramycin, minocycline, ciprofloxacin, levofloxacin, and cotrimoxazole in the hospital from 2018 to 2023 (all P<0.05). The resistance rate of clinical isolates was generally high, especially to β-lactam and quinolone drugs, which were mostly above 80% [such as cefepime (93.86%), cefotaxime (97.37%), imipenem (98.25%), and ciprofloxacin (99.12%)]. The resistance rate of environmental isolated strains to similar antibiotics was relatively lower, mostly concentrated at 10%‒30%. The whole-genome sequencing of 34 carbapenem-resistant Acinetobacter baumannii (CRAB) strains isolated from the hospital environment in 2023 revealed that the main resistance mechanism was overexpression of efflux pumps (51.97%), followed by changes in target sites (32.46%). Among the 34 CRAB strains, carbapenem resistance genes OXA-23 and OXA-51 were detected in 6 strains (17.65%), while genes such as KPC, IMP, VIM, and SIM were not detected. ConclusionFrom 2018 to 2023, AB in the hospital environment exhibited high resistance rates to certain antimicrobial agents and carried multiple resistance genes, indicating a potential transmission risk. It is necessary to further strengthen bacterial resistance monitoring and hospital infection control, and use antibiotics reasonably.

Myocardial fibrosis （MF） is a common pathological manifestation of various heart diseases. Due to the non-renewable nature of myocardial cells， the occurrence of MF represents irreversible damage to the myocardium. Previous studies have suggested that fibroblast-mediated collagen deposition is the main mechanism of MF. Recent studies have found that there is an immune regulation mechanism in the heart itself， and macrophage activation/polarization plays an important role in MF. With the deepening of traditional Chinese medicine research， scholars have found that traditional Chinese medicine can interfere with MF by regulating the renin-angiotensin-aldosterone system （RAAS） system and the inflammatory process， repairing the extracellular matrix， managing oxidative stress， and maintaining the balance of autophagy. This process is closely related to the activation and M1/M2 polarization of macrophages. Throughout the MF process， macrophage activation is beneficial， but excessive activation will be harmful. In the early stage of MF， appropriate M1 macrophage polarization is conducive to activating immunity and removing harmful substances. In the middle and late stages of MF， appropriate M2 macrophage polarization is conducive to remodeling the damaged myocardium. If macrophage activation is excessive/insufficient， or the balance of M1/M2 macrophage polarization is broken， the effect changes from improvement to destruction. Traditional Chinese medicines that regulate the activation/polarization of macrophages have the effects of replenishing Qi and nourishing Yin， as well as regulating Qi and activating blood， but there are also some heat-clearing， dampness-drying， and detoxification products. Therefore， the occurrence of MF may be caused by Qi and Yin deficiency， damp heat accumulation， and Qi stagnation and blood stasis. By summarizing the biological processes involved in macrophage activation/polarization in MF， this paper expounded on the research progress of traditional Chinese medicine in regulating macrophage activation and M1/M2 polarization from different angles to improve MF， so as to provide a reference for the treatment of MF with traditional Chinese medicine.

The chloroplast genome encodes many key proteins involved in photosynthesis and other metabolic processes, and metabolites synthesized in chloroplasts are essential for normal plant growth and development. Root-UVB (ultraviolet radiation B)-sensitive (RUS) family proteins composed of highly conserved DUF647 domain belong to chloroplast proteins. They play an important role in the regulation of various life activities such as plant morphogenesis, material transport and energy metabolism. This article summarizes the recent advances of the RUS family proteins in the growth and development of plants such as embryonic development, photomorphological construction, VB6 homeostasis, auxin transport and anther development, with the aim to facilitate further study of its molecular regulation mechanism in plant growth and development.
Female ; Pregnancy ; Humans ; Ultraviolet Rays ; Biological Transport ; Chloroplasts/genetics* ; Embryonic Development ; Plant Development/genetics*

The outer membrane composed predominantly of lipopolysaccharide (LPS) is an essential biological barrier for most Gram-negative (G^-) bacteria. Lipopolysaccharide transport protein (Lpt) complex LptDE is responsible for the critical final stage of LPS transport and outer membrane assembly. The structure and function of LptDE are highly conserved in most G^- bacteria but absent in mammalian cells, and thus LptDE complex is regarded as an attractive antibacterial target. In recent 10 years, the deciphering of the three-dimensional structure of LptDE protein facilities the drug discovery based on such "non-enzyme" proteins. Murepavadin, a peptidomimetic compound, was reported to be the first compound able to target LptD, enlightening a new class of antibacterial molecules with novel mechanisms of action. This article is devoted to summarize the molecular characteristics, structure-function of LptDE protein complex and review the development of murepavadin and related peptidomimetic compounds, in order to provide references for relevant researches.