Acute kidney injury (AKI) is a prevalent and life-threatening clinical syndrome characterised by a rapid decline in renal function and diverse pathological etiologies. The condition has been demonstrated to be associated with elevated mortality rates and an increased risk of progression to chronic kidney disease. At present, clinicians depend heavily on conventional functional markers, such as serum creatinine and urine output, for the diagnosis and staging of the disease. It is evident that these conventional indicators characteristically manifest a considerable temporal delay and only undergo modification subsequent to considerable tissue damage. This severely restricts the timeframe for early detection and timely therapeutic intervention. Furthermore, standard markers fail to provide specific biological information regarding the underlying cellular injury mechanisms. The utilisation of advanced probe technologies in molecular imaging offers a robust alternative to overcome these inherent diagnostic limitations.This comprehensive review systematically evaluates recent progress in the design and application of two primary categories of molecular imaging tools for acute kidney disease, specifically reactive probes and enzyme-activated probes. Reactive probes are engineered to specifically interact with redox-active chemical species, including hydrogen peroxide, peroxynitrite, hypochlorous acid, and sulfur dioxide. Because oxidative stress constitutes a primary early event in acute renal tubular damage, these probes enable researchers and clinicians to visualize early cellular injury and radical accumulation well before global renal functional decline becomes evident. We discuss the application of these reactive probes across multiple imaging modalities including fluorescence imaging, magnetic resonance imaging (MRI), positron emission tomography (PET), and photoacoustic techniques. Photoacoustic imaging combines high spatial resolution with deep tissue penetration and has successfully demonstrated the ability to provide diagnostic alerts up to 12 h before any detectable rise in serum creatinine levels. Additionally, specific reactive probes have shown promising translational potential when tested by high-throughput screening in clinical human urine samples. Enzyme-activated probes target the specific catalytic activity of disease-relevant enzymes. These include well-documented renal tubular structural biomarkers such as NAG, GGT, and ALP, along with apoptosis-related caspases and specific nitroreductases. By responding only to enzymatic cleavage, these tools provide highly specific and pathology-directed imaging readouts. Recent structural design strategies in this field have advanced significantly beyond single-enzyme detection. Researchers are now focusing on sophisticated dual-target recognition to minimize background noise, multimodal integration to cross-validate imaging signals, and theranostic applications where probes simultaneously deliver diagnostic feedback and therapeutic agents to injured tissues. Nanotechnology serves as a fundamental enabler for realizing these advanced probe functions. By precisely optimizing nanoparticle parameters such as hydrodynamic size, surface charge, and targeting ligands, researchers can achieve amplified signal output, highly precise kidney delivery, and protection against premature degradation in the systemic circulation. For example, modifying surface charges can significantly enhance the active uptake of nanoprobes by damaged renal tubular epithelial cells.While preclinical probe development has progressed rapidly, moving these technologies into routine clinical practice remains a major challenge. We analyze the translational feasibility and current obstacles from biological, technological, and regulatory perspectives. Although biological targets such as KIM-1, FAP, and ALP have been validated in extensive patient cohorts, practical barriers severely limit their immediate clinical application. These obstacles involve complex changes in in vivo pharmacokinetics. During an acute injury episode, the extreme drop in the glomerular filtration rate alters probe clearance and can cause unwanted systemic accumulation or confusing background imaging signals. Other major hurdles include a lack of comprehensive long-term toxicity data and the absence of standardized manufacturing protocols to ensure batch-to-batch consistency. Future successful translation will require rigorous multi-center clinical studies to confirm the true diagnostic value of these probes over traditional markers. Researchers must also establish strict standardization of imaging procedures and comprehensive safety evaluations. Ultimately, this review provides a thorough reference framework for designing clinically translatable molecular probes and building a precision diagnostic imaging system for acute kidney injury.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

As the number of patients with compromised immune function increases and fungal resistance develops, so does the risk of contracting deadly fungi in humans. Both fungi and humans are eukaryotes, so identifying unique targets for antifungal drug development is difficult. In addition, the existing antifungal drugs are limited by toxicity, drug interaction and drug resistance in practical application, which leads to the increasing incidence and fatal rate of fungal infections. Therefore, it is urgent to develop new antifungal drugs. The semi-synthetic technology using microbial fermentation products from natural sources as lead compounds has become the most used method in structural modification of antifungal drugs due to its advantages of few reaction steps and easy operation. This paper will introduce the current status of natural antifungal drugs in clinical use, as well as the latest progress in the research and development of new semi-synthetic antifungal drugs, and summarize their mechanism of action, structural modifications, advantages and disadvantages, so as to provide reference for the subsequent development of new antifungal drugs.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To investigate application effect of liquid-based cytology screening based on artificial intelligence(AI)technique in diagnosing precancerous lesions of cervix,so as to provide basis for early diagnosis and treatment about cervical cancer.Methods:A total of 2 406 cervical liquid-based cytology specimens that were collected from the Health Medical Physical Examination Center of Peking Union Medical College Hospital during January and February 2024.AI-powered pathology platforms were employed to perform pathological diagnosis on submitted tissue samples,and the AI-assisted diagnostic results were compared with microscopic diagnosis results by senior cytopathologists.The pathological images of liquid-based cell sheet were automatically screened through deep convolutional neural network(DCNN),and the results of senior cytopathologists were used as"gold standard"to judge all diagnostic results.The accuracy of AI was analyzed in rapidly identifying precancerous lesions or early-stage cervical cancer.Results:Among 2 406 pathological diagnoses,2 054 specimens were negative and 352 positive.Of the 352 positive cases,123 cases Atypical squamous cells cannot exclude high-grade squamous intraepithelial lesions(ASCH)66 cases showed atypical squamous cells(ASCUS),83 cases showed low-grade squamous intraepithelial neoplasia(LSIL),9 cases showed high-grade squamous intraepithelial neoplasia(HSIL),70 cases showed infectious lesions(including bacterial vaginosis,Candida albicans,trichomoniasis,and actinomycosis),and 1 case showed atypical glandular cells(AGC).The AI-assisted imaging system achieved an overall diagnostic positive accuracy rate of 95.20%(335/352)and 90.76%,and the sensitivities of that in diagnosing ASCUS and LSIL were respectively 36.72%and 98.00%,all of which were higher than the diagnostic results of physicians with junior and intermediate professional titles,and the differences were significant(x2=11.81,21.97,P<0.05).The time of AI system in reading images was 49 s,while those of pathologists with junior and intermediate professional titles were respectively 125 s and 80 s.Conclusion:The AI system has higher sensitivity in liquid-based cytology screening for cervix,and its diagnostic efficiency has a certain of advantages.The application of AI-assisted diagnosis has substantially improved the accuracy and reliability of diagnosing precancerous lesions of cervix.

Objective:This study investigated the independent risk factors for lymph node metastasis（LNM）in high-risk prostate cancer（HRPCa）patients undergoing robot-assisted radical prostatectomy（RARP），and constructed a nomogram model based on clinical data to improve the accuracy and clinical practicality of preoperative prediction of LNM.Methods:A retrospective analysis was conducted on the clinical data of 218 HRPCa patients who received RARP treatment at the First Medical Center of the PLA General Hospital from January 2020 to March 2025 as the modeling group. The age of the modeling group was（66.91±6.94）years old. 75 cases（34.40%）had a history of smoking，and 48 cases（22.02%）had a history of drinking. There were a body mass index（BMI）of 25.55（23.58，27.00）kg/m 2，a total prostate-specific antigen（tPSA）of 20.59（10.42，30.61）ng/ml，a free prostate-specific antigen（fPSA）of 1.87（1.04，3.26）ng/ml，a prostate volume（PV）of（41.19±21.00）ml，a prostate-specific antigen density（PSAD）of 0.52（0.30，0.84）ng/ml 2. Among the patients，60 cases（27.52%）had a preoperative biopsy Gleason score >8，and the percentage of positive biopsy cores（PPBC）was 50%（31%，80%）. Thirty-one patients（14.22%）were staged clinically as >T 2c. The diagnostic criteria for high-risk prostate cancer（HRPCa）were defined as meeting any one of the following：PSA >20 ng/ml，Gleason score on prostate biopsy ≥8，or clinical stage ≥T 3. Among the 218 patients in the modeling cohort，67 cases（30.73%）met two of the criteria，and 7 cases（3.21%）met all three criteria. All 218 patients underwent RARP，and based on postoperative pathology，they were divided into the LNM group and the non-LNM group. The relationship between the number of diagnostic criteria met and the occurrence of LNM was analyzed. An external validation cohort included 42 HRPCa patients who underwent RARP at the Third，Fifth Medical Centers of the PLA General Hospital between January 2023 and May 2025. Their mean age was（66.79±5.92）years. Eighteen patients（42.86%）had a smoking history，and nine（21.43%）had a history of alcohol consumption. The median BMI was 26.00（23.80，27.13）kg/m 2. The median tPSA level was 17.34（8.97，27.30）ng/ml. The median fPSA was 1.51（0.83，2.52）ng/ml，and the median PV was（35.57 ± 15.25）ml. The median PSAD was 0.57（0.23，0.87）ng/ml 2，and the median PPBC was 58%（36%，71%）. Three patients（7.14%）had a clinical stage >T 2c，and 12 patients（28.57%）had a Gleason score >8 on preoperative biopsy. Univariate and multivariate binary logistic regression analyses were used to identify independent risk factors for LNM，and a nomogram model was constructed based on these factors. The predictive performance of the model was evaluated using receiver operating characteristic（ROC）curves and calibration plots，and the model was validated in the external cohort. Result:According to postoperative pathology，45 patients were classified into the LNM group，and 173 into the non-LNM group. The probability of LNM increased proportionally with the number of diagnostic criteria met for HRPCa（meeting two criteria： OR = 4.762，95% CI 2.323-9.761， P < 0.01；meeting three criteria： OR = 10.667，95% CI 2.187-52.025， P=0.003）. Binary logistic regression analysis revealed that age（ OR=0.913，95% CI 0.859-0.971， P = 0.004），tPSA（ OR=1.039，95% CI 1.018-1.061， P<0.01），PPBC（ OR = 5.656，95% CI 1.101-29.056， P = 0.038），and clinical T stage（T 2c stage： OR=2.945，95% CI 0.888-9.769， P=0.077；>T 2c stage OR = 18.351，95% CI 4.790-70.306， P < 0.01）were independent risk factors for postoperative LNM in HRPCa patients after RARP. The ROC curve of the nomogram model based on these factors showed an area under the curve（AUC）of 0.853（95% CI 0.790-0.917）. In the external validation cohort，the nomogram achieved an AUC of 0.743（95% CI 0.556-0.929）. The calibration plots demonstrated good agreement between the predicted probabilities and actual observations. Conclusions:Age，tPSA，PPBC，and clinical T stage were independent predictors of postoperative LNM in HRPCa patients undergoing RARP. The greater the number of HRPCa diagnostic criteria met，the higher the likelihood of postoperative LNM. The nomogram developed in this study could effectively predict the risk of LNM in HRPCa patients after RARP.

Objective:To analyze the association between unhealthy lifestyles and depressive symptoms, anxiety symptoms and stress among secondary school students in Jiangsu Province.Methods:From September to November 2023, a multistage stratified cluster random sampling method was used to select secondary school students from 13 districts and cities in Jiangsu Province. A questionnaire survey was conducted on their unhealthy lifestyles (low physical activity, smoking, drinking, internet addiction, poor sleep quality, and unhealthy diet), as well as their depressive symptoms, anxiety symptoms and stress. The multivariate logistic regression model and mixed graph model were used to construct a network and analyze the association between unhealthy lifestyles and depressive symptoms, anxiety symptoms and stress.Results:A total of 81 414 secondary school students were finally included in this study, including 39 725 (48.79%) female students and 41 689 (51.21%) male students. The prevalence of depressive symptoms, anxiety symptoms and stress were 18.55%, 32.09% and 12.91%, respectively. The multivariate logistic regression model showed that after adjusting for age, gender, urban-rural status, residential status, and family type, compared with students without unhealthy lifestyles, students with low physical activity, smoking, alcohol consumption, internet addiction, poor sleep quality, and unhealthy diet had a significantly increased risk of depressive symptoms ( OR=1.12, 95% CI:1.07-1.17; OR=1.60, 95% CI: 1.49-1.72; OR=1.79, 95% CI: 1.71-1.88; OR=3.05, 95% CI: 2.77-3.36; OR=6.66, 95% CI: 6.40-6.93; OR=1.29, 95% CI: 1.24-1.34) and a significantly increased risk of anxiety symptoms ( OR=1.09, 95% CI: 1.05-1.13; OR=1.42, 95% CI: 1.33-1.52; OR=1.76, 95% CI: 1.69-1.83; OR=2.40, 95% CI: 2.17-2.65; OR=5.79, 95% CI: 5.59-6.00; OR=1.16, 95% CI: 1.12-1.21). Students who smoked, drank alcohol, had internet addiction, and had poor sleep quality had a significantly increased risk of stress ( OR=1.49, 95% CI: 1.38-1.61; OR=1.79, 95% CI: 1.70-1.89; OR=2.25, 95% CI: 2.04-2.48; OR=6.74, 95% CI: 6.43-7.06). The node with poor sleep quality (bridge strength=0.48) in the network constructed by the mixed graph model had the strongest centrality of the bridge connecting unhealthy lifestyles with depressive symptoms, anxiety symptoms and stress. Conclusion:Low physical activity, smoking, alcohol consumption, internet addiction, poor sleep quality, and unhealthy diet increase the risk of depressive symptoms and anxiety symptoms among Jiangsu Province secondary school students. Smoking, alcohol consumption, internet addiction, and poor sleep quality increase the risk of stress among Jiangsu Province secondary school students. Sleep quality is an important intervention target for Jiangsu Province secondary school students to alleviate their negative emotions.

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.