Objective To reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. Methods Publications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. Results A marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. Conclusion Over the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.

T7 RNA polymerase (T7 RNAP) is one of the simplest known RNA polymerases. Its unique structural features make it a critical model for studying the mechanisms of RNA synthesis. This review systematically examines the static crystal structure of T7 RNAP, beginning with an in-depth examination of its characteristic “thumb”, “palm”, and “finger” domains, which form the classic “right-hand-like” architecture. By detailing these structural elements, this review establishes a foundation for understanding the overall organization of T7 RNAP. This review systematically maps the functional roles of secondary structural elements and their subdomains in transcriptional catalysis, progressively elucidating the fundamental relationships between structure and function. Further, the intrinsic flexibility of T7 RNAP and its applications in research are also discussed. Additionally, the review presents the structural diagrams of the enzyme at different stages of the transcription process, and through these diagrams, it provides a detailed description of the complete transcription process of T7 RNAP. By integrating structural dynamics and kinetics analyses, the review constructs a comprehensive framework that bridges static structure to dynamic processes. Despite its advantages, T7 RNAP has a notable limitation: it generates double-stranded RNA (dsRNA) as a byproduct. The presence of dsRNA not only compromises the purity of mRNA products but also elicits nonspecific immune responses, which pose significant challenges for biotechnological and therapeutic applications. The review provides a detailed exploration of the mechanisms underlying dsRNA formation during T7 RNAP catalysis, reviews current strategies to mitigate this issue, and highlights recent progress in the field. A key focus is the semi-rational design of T7 RNAP mutants engineered to minimize dsRNA generation and enhance catalytic performance. Beyond its role in transcription, T7 RNAP exhibits rapid development and extensive application in fields, including gene editing, biosensing, and mRNA vaccines. This review systematically examines the structure-function relationships of T7 RNAP, elucidates the mechanisms of dsRNA formation, and discusses engineering strategies to optimize its performance. It further explores the engineering optimization and functional expansion of T7 RNAP. Furthermore, this review also addresses the pressing issues that currently need resolution, discusses the major challenges in the practical application of T7 RNAP, and provides an outlook on potential future research directions. In summary, this review provides a comprehensive analysis of T7 RNAP, ranging from its structural architecture to cutting-edge applications. We systematically examine: (1) the characteristic right-hand domains (thumb, palm, fingers) that define its minimalistic structure; (2) the structure-function relationships underlying transcriptional catalysis; and (3) the dynamic transitions during the complete transcription cycle. While highlighting T7 RNAP’s versatility in gene editing, biosensing, and mRNA vaccine production, we critically address its major limitation—dsRNA byproduct formation—and evaluate engineering solutions including semi-rationally designed mutants. By synthesizing current knowledge and identifying key challenges, this work aims to provide novel insights for the development and application of T7 RNAP and to foster further thought and progress in related fields.

Objective To analyze the epidemiological distribution,microbiological characteristics,drug-resistance status,and risk factors for mortality in adult intensive care unit(ICU)patients with Klebsiella pneumoniae infection.Methods This multi-center prospective cohort study included ICU patients with suspected infection from 67 hospitals across 16 Chinese provinces/municipalities between July 1,2021 and December 31,2022.Clinical data and microbiological results were collected,and patients were divided into survival and non-survival groups according to their survival status and drug-resistance situation.Risk factors for mortality and drug resistance in ICU patients with Klebsiella pneumonia infection were determined through univariate and multivariate logistic regression analyses.Results A total of 2964 ICU-infected patients were enrolled,with 12 175 microbial specimens submitted for testing.Among these,487 specimens tested positive for Klebsiella pneumoniae.Ultimately,314 patients with Klebsiella pneumoniae infection were identified,primarily from lung infections,with a drug-resistance rate of 78.3%.The in-hospital mortality rate of ICU patients infected with Klebsiella pneumoniae was 19.8%.Univariate and multivariate logistic regression analyses revealed that older age(P=0.027),high drug-resistance rate(P=0.028),and low clinical-effectiveness rate(P<0.001)were independent risk factors for mortality in ICU patients infected with Klebsiella pneumoniae.Drug-resistance analysis showed that,compared with non-resistant cases,ICU patients with drug-resistant Klebsiella pneumoniae infection had lower pathogen-clearance rates(P=0.003),clinical-effectiveness rates(P=0.004),and antibiotic-effectiveness rates(P<0.010),and higher mortality rates(P=0.006).Patients with Klebsiella pneumoniae abdominal infection(P=0.003)and urinary tract infection(P=0.007)had higher drug-resistance incidences.There were no statistically significant differences in clinical-effectiveness rate,Klebsiella pneumoniae clearance,drug-resistance incidence,mortality rate,or hospital-stay length between patients with lung infection and those with non-lung infection of Klebsiella pneumoniae(P>0.05).Compared with patients with non-bloodstream infection,patients with bloodstream infection of Klebsiella pneumoniae had lower clinical-effectiveness rates(P=0.027)and higher mortality rates(P=0.021).Conclusions Older age,high drug-resistance rate,and low clinical-effectiveness rate are independent risk factors for mortality in ICU patients infected with Klebsiella pneumoniae.ICU patients with bloodstream infection of Klebsiella pneumoniae may have lower clinical-effectiveness rates and higher mortality rates.ICU patients with abdominal and urinary tract infections caused by Klebsiella pneumoniae are more likely to develop drug resistance.

With the rapid development of competitive sports, the incidence of anterior cruciate ligament (ACL) injury is on the rise. Such injuries may shorten athletes′ career and lead to other long-term adverse consequences. Although athletes generally recover well after ACL reconstruction, many still struggle to return to their pre-injury performance levels. Advances in the understanding of ACL anatomy and injury mechanisms, along with the evolution of surgical techniques and rehabilitation methods, have provided more individualized and tailored options for athletes following ACL injuries. However, there is currently no consensus in China regarding surgical and rehabilitation strategies for competitive athletes aiming to return to sports after ACL injuries. To this end, the Sports Medicine Committee of the Chinese Research Hospital Association and the Editorial Board of the Chinese Journal of Trauma jointly formulated the Expert consensus on surgical treatment and rehabilitation for competitive sports athletes returning to sports after anterior cruciate ligament injury ( version 2025), and presented 14 recommendations covering surgical indications, preoperative rehabilitation, surgical timing, surgical strategies and postoperative rehabilitation strategies, aiming to improve the surgical treatment and rehabilitation system for ACL injuries in competitive athletes and facilitate their return to high-level sports performance after injury.

AIM To establish the quantitative models for gallic acid,mononucleoside,loganin,resveratrol,and rhein in Yishen Xiezhuo Mixture.METHODS HPLC was adopted in the content determination of various effective components,after which the near-infrared spectroscopy(NIRS)data were collected in 128 batches of samples and pretreatment was conducted,competitive adaptive reweighting sampling(CARS)algorithm was used for screening wavelength,partial least square method(PLS)regression analysis was performed.RESULTS There were no significant differences between the predicted values obtained by PLS models and measured values obtained by HPLC for various effective components(P＞0.05).CONCLUSION The quantitative models established by NIRS combined with chemometrics display good predictive performance,which can be used for the rapid determination of effective components in Yishen Xiezhuo Mixture,and provide a reference for the rapid monitoring of other traditional Chinese medicine preparations in production processes.

Objective:This study aims to analyze the gamma band effective connectivity characteristics of the prefrontal-striatal circuitry in bipolar disorder patients with and without a history of manic episodes, as well as in major depressive disorder patients, during the recognition of positive emotional faces, this study aims to identify unique neurophysiological features that may aid in the early detection of bipolar disorder.Methods:This retrospective study collected clinical data and magnetoencephalography (MEG) imaging data from patients performing a positive emotional face recognition task at the Affiliated Brain Hospital of Nanjing Medical University from May 2009 to December 2019. The study included 75 patients with major depressive disorder and 29 patients with bipolar disorder in a depressive episode (rBD group). Concurrently, 39 age-and gender-matched healthy controls (HC group) were recruited. After a follow-up period of at least 5 years, 23 out of the 75 patients with major depressive disorder converted to bipolar disorder (ctBD group), while the remaining 52 who did not convert maintained a diagnosis of major depressive disorder.Results:There were statistically significant differences in gamma-band effective connectivity in the prefrontal-striatal circuit when recognizing positive emotional faces among the converted to bipolar disorder (ctBD), raw bipolar disorder, major depressive disorder, and HC groups ( H=9.04, 10.30, 8.30, 13.43, 14.38, 12.62, 9.82, 8.94, 24.62, 7.89, 18.53, 9.97, 9.58, 12.79, P<0.05). The ctBD group, rBD group, and major depressive group all showed reduction in effective connectivity from the right orbital inferior frontal gyrus (ORBinf.R) to the left orbital inferior frontal gyrus (ORBinf.L) [ Z=-1.98, -3.38, -2.88], from the right orbital inferior frontal gyrus to the right ventral striatum (VS.R) ( Z=-2.05, -2.76, -2.11; P<0.05) and from the left ventral striatum (VS.L) to the left orbital middle frontal gyrus (ORBmid.L) ( Z=-2.76, -1.98, -2.43; P<0.05). Among the disease groups, the ctBD group showed significantly enhanced effective connectivity strength compared to the major depressive group from the right amygdala (AMYG.R) to the left orbital inferior frontal gyrus(0.04(0.03, 0.08)), from the right amygdala to the left ventral striatum(0.05(0.03, 0.09)), and from the right ventral striatum to the right anterior cingulate and paracingulate gyri (ACG.R) (0.04(0.02, 0.08)) ( Z=4.17, 3.70, 3.35; P<0.001).The ctBD group also exhibited enhanced effective connectivity compared to the rBD group from ORBinf.R to the ACG.R, fron the AMYG.R to the ORBinf.L, from the AMYG.R to the VS.L, and from the VS.R to the ACG.R ( Z=2.05, 4.61, 3.60, 3.04; P<0.05).The rBD group demonstrated reduced effective connectivity compared to the major depressive disorder group from the right orbital middle frontal gyrus(ORBmid.R) to the left anterior cingulate and paracingulate gyri (ACG.L), ORBinf.R to the ACG.R and from the ORBinf.R to the AMYG.R ( Z=-2.12, -2.40, -2.22; P<0.05). Conclusion:There are significant differences in the gamma-band effective connectivity characteristics of the prefrontal-striatal pathway when recognizing positive emotional faces between patients with bipolar disorder in depressive episodes and those with depression, as well as differences between bipolar depressed patients with and without a history of manic episodes.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To explore the neurophysiological features of the prefrontal-limbic-striatal circuit in patients with early-stage bipolar disorder without manic or hypomanic episodes, and its role in identifying early-stage bipolar disorder.Methods:From 2009 to 2019, a total of 155 hospitalized patients with major depressive disorder (MDD) from Nanjing Brain Hospital were selected after at least 5 years of follow-up, 31 patients with depression transitioned to bipolar disorder(ctBD group) and 76 patients remained the diagnosis of MDD(MDD group) were recruited.Sixty-two healthy controls matched for age, gender, and education years were selected as control group(HC group). Resting-state magnetoencephalography (MEG) data in eyes-open state of all subjects were collected.Data were analyzed based on the fieldtrip toolkit on the MATLAB platform. The key brain area of the prefrontal-limbic-striatal circuit were selected. Inter-group statistical analysis were conducted on the spectral energy and power-correlated functional connectivity at the theta, alpha, beta, and gamma frequency bands in the brain area of interest. In addition, the prediction model was constructed to early recognize bipolar disorder.Results:(1)There were statistically significant differences in the spectral energy of theta and alpha frequency bands in the prefrontal-limbic-striatal circuit among the 3 groups (cluster- F=120.50, 112.39, both P<0.05). The spectral energy of theta and alpha frequency bands in interest brain regions of prefrontal-limbic-striatal circuit in MDD group was lower than that in HC group (cluster- t=89.52, P<0.05). The spectral energy of theta band in prefrontal-limbic-striatal circuit in ctBD group was lower than that in HC group(cluster- t=105.82, P<0.05), and the spectral energy of alpha band in inferior frontal gyrus, orbitofrontal gyrus and caudate nucleus was lower than that in HC group (cluster- t=75.78, P<0.05), while there was no significant difference between the MDD group and the ctBD group ( P>0.05).(2)After FDR correction, there were statistically significant differences in functional connectivity between the left orbitofrontal gyrus and the right ventral striatum among the three groups (0.26 (0.13, 0.34), 0.12 (0.09, 0.24), 0.27 (0.20, 0.37), H=13.51, P<0.05, FDR correction). The strength of functional connectivity between the left orbitofrontal gyrus and the right ventral striatum in the MDD group was weaker than that in the HC group and the ctBD group (all P<0.05).(3)Binary Logistic regression analysis showed that the functional connectivity of beta frequency band between the left orbitofrontal gyrus and the right ventral striatum ( B=1.50, OR=4.50, 95% CI=1.73-11.70), the functional connectivity between the right orbitofrontal gyrus and the right amygdala( B=0.98, OR=2.68, 95% CI=1.18-6.13), the total HAMD score ( B=0.80, OR=2.28, 95% CI=1.36-3.67), the body weight factor score ( B=-1.99, OR=0.14, 95% CI=0.04-0.45), the anxiety factor score ( B=-0.99, OR=0.37, 95% CI=0.19-0.71), and sleep factor score( B=-1.14, OR=0.32, 95% CI=0.16-0.65)were the influencing factors for depression transitioned to bipolar disorder. Conclusion:The decreased resting low-frequency energy in the prefrontal-limbic-striatal circuit may be the common neural basis for the onset of unipolar and bipolar depression, and enhanced functional connectivity may be a potential neural circuit mechanism for depression transitioned to bipolar disorder. Functional connectivity combined with clinical manifestations is helpful for early recognition of bipolar disorder.

Objective:To investigate the clinical characteristics of patients with multiple myeloma(MM)complicated by bone lesions and the risk factors associated with bone lesions.Methods:The clinical data of 294 newly diagnosed MM patients in Gansu Provincial Hospital from January 2017 to June 2021 were retrospectively analyzed.The patients were divided into the bone lesion group(154 cases)and the non-bone lesions group(140 cases)based on the presence of absence of bone lesions at diagnosis.The general data and laboratory parameters were compared between the two groups.The risk factors for bone lesions in MM patients were analyzed by logistic regression analysis,and the characteristic(ROC)curves were plotted to assess the predictive value of each risk factor for the occurrence of bone lesions in MM patients.Results:Compared to the non-bone lesion group,the bone lesion group had significantly higher serum calcium levels and significantly greater proportions of patients with Durie-Salmon(DS)stage Ⅲ,and bone pain(all P＜0.05).Logistic regression analysis showed that elevated serum calcium(OR=5.135,95％CI:1.931-13.653,P=0.001),DS stage Ⅲ(OR=1.841,95％CI:1.019-3.328,P=0.043),and bone pain(OR=8.208,95％CI:4.761-14.151,P＜0.001)were independent risk factors for bone lesions in MM patients.ROC curve analysis showed that serum calcium(AUC=0.619,95％CI:0.555-0.683,P＜0.001)and bone pain(AUC=0.743,95％CI:0.692-0.793,P＜0.001)had predictive value for bone lesions in MM patients.Conclusion:MM patients have a high incidence of bone lesions,and active monitoring and management of risk factors may improve treatment outcomes and prognosis.

Objective:To explore the neurophysiological features of the prefrontal-limbic-striatal circuit in patients with early-stage bipolar disorder without manic or hypomanic episodes, and its role in identifying early-stage bipolar disorder.Methods:From 2009 to 2019, a total of 155 hospitalized patients with major depressive disorder (MDD) from Nanjing Brain Hospital were selected after at least 5 years of follow-up, 31 patients with depression transitioned to bipolar disorder(ctBD group) and 76 patients remained the diagnosis of MDD(MDD group) were recruited.Sixty-two healthy controls matched for age, gender, and education years were selected as control group(HC group). Resting-state magnetoencephalography (MEG) data in eyes-open state of all subjects were collected.Data were analyzed based on the fieldtrip toolkit on the MATLAB platform. The key brain area of the prefrontal-limbic-striatal circuit were selected. Inter-group statistical analysis were conducted on the spectral energy and power-correlated functional connectivity at the theta, alpha, beta, and gamma frequency bands in the brain area of interest. In addition, the prediction model was constructed to early recognize bipolar disorder.Results:(1)There were statistically significant differences in the spectral energy of theta and alpha frequency bands in the prefrontal-limbic-striatal circuit among the 3 groups (cluster- F=120.50, 112.39, both P<0.05). The spectral energy of theta and alpha frequency bands in interest brain regions of prefrontal-limbic-striatal circuit in MDD group was lower than that in HC group (cluster- t=89.52, P<0.05). The spectral energy of theta band in prefrontal-limbic-striatal circuit in ctBD group was lower than that in HC group(cluster- t=105.82, P<0.05), and the spectral energy of alpha band in inferior frontal gyrus, orbitofrontal gyrus and caudate nucleus was lower than that in HC group (cluster- t=75.78, P<0.05), while there was no significant difference between the MDD group and the ctBD group ( P>0.05).(2)After FDR correction, there were statistically significant differences in functional connectivity between the left orbitofrontal gyrus and the right ventral striatum among the three groups (0.26 (0.13, 0.34), 0.12 (0.09, 0.24), 0.27 (0.20, 0.37), H=13.51, P<0.05, FDR correction). The strength of functional connectivity between the left orbitofrontal gyrus and the right ventral striatum in the MDD group was weaker than that in the HC group and the ctBD group (all P<0.05).(3)Binary Logistic regression analysis showed that the functional connectivity of beta frequency band between the left orbitofrontal gyrus and the right ventral striatum ( B=1.50, OR=4.50, 95% CI=1.73-11.70), the functional connectivity between the right orbitofrontal gyrus and the right amygdala( B=0.98, OR=2.68, 95% CI=1.18-6.13), the total HAMD score ( B=0.80, OR=2.28, 95% CI=1.36-3.67), the body weight factor score ( B=-1.99, OR=0.14, 95% CI=0.04-0.45), the anxiety factor score ( B=-0.99, OR=0.37, 95% CI=0.19-0.71), and sleep factor score( B=-1.14, OR=0.32, 95% CI=0.16-0.65)were the influencing factors for depression transitioned to bipolar disorder. Conclusion:The decreased resting low-frequency energy in the prefrontal-limbic-striatal circuit may be the common neural basis for the onset of unipolar and bipolar depression, and enhanced functional connectivity may be a potential neural circuit mechanism for depression transitioned to bipolar disorder. Functional connectivity combined with clinical manifestations is helpful for early recognition of bipolar disorder.