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.

Chronic diabetic wounds, severely complicated by multidrug-resistant (MDR) bacterial infections, represent a profound and escalating global health crisis. The intrinsically hostile microenvironment of diabetic wounds, characterized by localized hypoxia, persistent oxidative stress, and poor vascularization, creates an ideal niche for opportunistic pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa. These bacteria readily construct dense extracellular polymeric substance (EPS) biofilms, which not only physically shield the microbes from host immune responses but also actively trap the wound in a state of chronic, unresolved inflammation. Consequently, conventional systemic and topical antibiotic therapies are becoming increasingly futile, as poor perfusion at the wound site restricts drug bioavailability, while the rapid genetic evolution of bacteria and the impenetrable nature of biofilms lead to catastrophic treatment failures, often culminating in severe tissue necrosis and lower-extremity amputations. To circumvent the limitations of traditional antimicrobials, therapeutic gas delivery has emerged as a highly promising, paradigm-shifting strategy. Gaseous signaling molecules, particularly nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H₂S), and hydrogen (H₂), possess unique physicochemical properties that allow them to seamlessly penetrate dense biofilm matrices and cellular membranes. Once inside, these gases operate via multi-targeted mechanisms that are incredibly difficult for bacteria to develop resistance against; for instance, NO induces severe lipid peroxidation and DNA cleavage in bacteria, CO downregulates pro-inflammatory cytokines, H₂S significantly accelerates endothelial cell migration for neovascularization, and H₂ acts as a powerful selective antioxidant to neutralize tissue-damaging reactive oxygen species (ROS). Together, these therapeutic gases not only exert broad-spectrum bactericidal effects but also actively reprogram the wound bed by promoting the critical M1-to-M2 macrophage polarization and stimulating angiogenesis. Despite their immense biological potential, the direct clinical translation of gas therapies is severely hindered by inherent physicochemical drawbacks, including extreme volatility, short physiological half-lives, poor aqueous solubility, and the high risk of off-target systemic toxicity, if applied indiscriminately. To conquer these immense pharmacokinetic barriers, cutting-edge advancements in materials science have driven the development of gas-releasing micro- and nanoplatforms. Utilizing sophisticated carriers such as metal-organic frameworks (MOFs), mesoporous silica, polymeric nanoparticles, liposomes, and injectable hydrogels, researchers can now encapsulate gas-donor molecules to achieve sustained, localized delivery. More importantly, these advanced nanoplatforms are ingeniously engineered to be stimuli-responsive. By exploiting the pathological hallmarks of the diabetic wound environment, such as elevated glucose concentrations, acidic pH, and overexpressed ROS, or by utilizing external triggers like near-infrared (NIR) light irradiation and ultrasound, these intelligent platforms ensure on-demand, precise spatio-temporal gas release. This often allows for powerful synergistic combinations, such as photothermal or photodynamic therapy coupled with gas release, thereby obliterating biofilms while sparing healthy tissue. While the therapeutic outcomes of these smart delivery systems in eradicating MDR infections and accelerating tissue repair are unprecedented, several critical challenges remain before widespread clinical adoption, as long-term biosafety profiles of the carrier nanomaterials, complexities in large-scale good manufacturing practice (GMP) production, and stringent regulatory hurdles must be rigorously addressed. Looking forward, the next frontier lies in the realm of precision medicine and theranostics, where future research must focus on the seamless integration of these gas-releasing platforms with flexible, wearable biosensors capable of continuously monitoring wound biomarkers (e.g., pH, temperature, uric acid) in real-time. Coupled with artificial intelligence algorithms to govern automated, closed-loop adaptive dosing, these next-generation smart dressings hold the ultimate potential to comprehensively transform the clinical management of complex, infected diabetic wounds.

Objective To explore the prevalence of depressive symptoms in postoperative patients with ovarian cancer and to analyze its influencing factors from multiple dimensions, including clinical characteristics, psychological factors, and laboratory indicators. Methods A cross-sectional study was conducted, which enrolled 235 postoperative patients with ovarian cancer. Depressive status was assessed using the patient health questionnaire, and the demographic, pathological, and medical record data of the patients were collected using the generalized anxiety disorder scale, Pittsburgh sleep quality index, European organization for research and treatment of cancer quality of life questionnaire core 30, and ECOG performance status score. Peripheral blood tumor marker (CA125), routine blood test, lymphocyte subsets, and serum cytokine levels were measured. Univariate and multivariate binary logistic regression analysis were used for statistical analysis. Results The prevalence of depression in postoperative patients with ovarian cancer was 39.15% (92/235). Univariate analysis showed that ECOG score ≥ 2 points, pain, anxiety, poor sleep quality, low quality of life, low life satisfaction, tumor recurrence, six or more cycles of chemotherapy, as well as higher levels of CA125, NLR, and NAR, and lower hemoglobin levels were significantly associated with depression (all P<0.05). Multivariate binary Logistic regression analysis showed that anxiety (OR=1.975, 95%CI: 1.231-3.170), sleep efficiency (OR=4.181, 95%CI: 1.211-14.43), sleep latency (OR=34.806, 95%CI: 4.258-284.542), ECOG performance status score, cognitive function (OR=0.918, 95%CI: 0.868-0.97), and life satisfaction were independent risk factors for depression (all P<0.05). Laboratory indicators were not independent influencing factors in the multivariate Logistic regression model. Conclusion Depression in postoperative patients with ovarian cancer is influenced by physiological, psychological, and social factors. Clinical management should focus on patients with anxiety, sleep disorders, poor physical condition, and low life satisfaction, and a comprehensive prevention and treatment strategy centered on psychological intervention and taking into account symptom management and social support should be implemented.

Chronic diabetic wounds, severely complicated by multidrug-resistant (MDR) bacterial infections, represent a profound and escalating global health crisis. The intrinsically hostile microenvironment of diabetic wounds, characterized by localized hypoxia, persistent oxidative stress, and poor vascularization, creates an ideal niche for opportunistic pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa. These bacteria readily construct dense extracellular polymeric substance (EPS) biofilms, which not only physically shield the microbes from host immune responses but also actively trap the wound in a state of chronic, unresolved inflammation. Consequently, conventional systemic and topical antibiotic therapies are becoming increasingly futile, as poor perfusion at the wound site restricts drug bioavailability, while the rapid genetic evolution of bacteria and the impenetrable nature of biofilms lead to catastrophic treatment failures, often culminating in severe tissue necrosis and lower-extremity amputations. To circumvent the limitations of traditional antimicrobials, therapeutic gas delivery has emerged as a highly promising, paradigm-shifting strategy. Gaseous signaling molecules, particularly nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H₂S), and hydrogen (H₂), possess unique physicochemical properties that allow them to seamlessly penetrate dense biofilm matrices and cellular membranes. Once inside, these gases operate via multi-targeted mechanisms that are incredibly difficult for bacteria to develop resistance against; for instance, NO induces severe lipid peroxidation and DNA cleavage in bacteria, CO downregulates pro-inflammatory cytokines, H₂S significantly accelerates endothelial cell migration for neovascularization, and H₂ acts as a powerful selective antioxidant to neutralize tissue-damaging reactive oxygen species (ROS). Together, these therapeutic gases not only exert broad-spectrum bactericidal effects but also actively reprogram the wound bed by promoting the critical M1-to-M2 macrophage polarization and stimulating angiogenesis. Despite their immense biological potential, the direct clinical translation of gas therapies is severely hindered by inherent physicochemical drawbacks, including extreme volatility, short physiological half-lives, poor aqueous solubility, and the high risk of off-target systemic toxicity, if applied indiscriminately. To conquer these immense pharmacokinetic barriers, cutting-edge advancements in materials science have driven the development of gas-releasing micro- and nanoplatforms. Utilizing sophisticated carriers such as metal-organic frameworks (MOFs), mesoporous silica, polymeric nanoparticles, liposomes, and injectable hydrogels, researchers can now encapsulate gas-donor molecules to achieve sustained, localized delivery. More importantly, these advanced nanoplatforms are ingeniously engineered to be stimuli-responsive. By exploiting the pathological hallmarks of the diabetic wound environment, such as elevated glucose concentrations, acidic pH, and overexpressed ROS, or by utilizing external triggers like near-infrared (NIR) light irradiation and ultrasound, these intelligent platforms ensure on-demand, precise spatio-temporal gas release. This often allows for powerful synergistic combinations, such as photothermal or photodynamic therapy coupled with gas release, thereby obliterating biofilms while sparing healthy tissue. While the therapeutic outcomes of these smart delivery systems in eradicating MDR infections and accelerating tissue repair are unprecedented, several critical challenges remain before widespread clinical adoption, as long-term biosafety profiles of the carrier nanomaterials, complexities in large-scale good manufacturing practice (GMP) production, and stringent regulatory hurdles must be rigorously addressed. Looking forward, the next frontier lies in the realm of precision medicine and theranostics, where future research must focus on the seamless integration of these gas-releasing platforms with flexible, wearable biosensors capable of continuously monitoring wound biomarkers (e.g., pH, temperature, uric acid) in real-time. Coupled with artificial intelligence algorithms to govern automated, closed-loop adaptive dosing, these next-generation smart dressings hold the ultimate potential to comprehensively transform the clinical management of complex, infected diabetic wounds.

Background Benzothiazoles and benzotriazoles are ubiquitously detected in drinking water, posing potential health risks. Developing a reliable and sensitive analytical method is critical for assessing their exposure levels and associated risks. Objective To establish an automated solid-phase extraction (SPE) coupled with liquid chromatography-tandem mass spectrometry method for the simultaneous determination of benzothiazole, 2-aminobenzothiazole, benzotriazole, and 5-chlorobenzotriazole in drinking water. Methods Key parameters were systematically optimized. Three mobile phase systems were compared to evaluate their effects on chromatographic peak shape and separation; three SPE cartridges were tested for extraction efficiency; the influences of water pH on extraction recoveries and matrix effects were investigated; the contributions of pretreatment steps to benzothiazole blank were analyzed, and control measures were established; nitrogen evaporation temperatures were tested to ensure analyte stability. The optimized parameters were used to develop the method, validate its performance, and analyze drinking water samples. Results Drinking water samples were first adjusted to pH 6.0 with formic acid, and then extracted using HLB cartridges. After elution with 6 mL methanol, the eluate was concentrated under nitrogen evaporation at 45 °C, followed by separation on a C18 column using a gradient elution of acetonitrile-0.1% formic acid solution, and tandem mass spectrometry detection. Avoiding nitrogen blow-drying during SPE and eliminating the use of polypropylene materials during nitrogen evaporation can effectively minimize benzothiazole contamination. Good calibration linearity was obtained for the target analytes in the concentration range of 5.0-250 μg·L⁻¹, with a correlation coefficient of r > 0.995. The method detection limits were in the range of 1.0-5.0 ng·L⁻¹. The recoveries of the target analytes in pure water and tap water were 80.2%-119.5% and 72.2%-115.6%, respectively, with relative standard deviations of were 3.2%-12.8% and 2.3%-11.6%, respectively. When applying this method to actual water samples, benzotriazole was detected in 100% of treated and tap water samples, with median concentrations of 79.4 ng·L⁻¹ and 114 ng·L⁻¹, respectively. Benzothiazole was detected in 83.3% of treated water samples and 100% of tap water samples, with median concentrations of 48.3 ng·L⁻¹ and 65.4 ng·L⁻¹, respectively. In addition, 5-chloro-benzotriazole exhibited low detection rates and concentrations, while 2-amino-benzothiazole was undetected. Conclusion The developed method demonstrates high accuracy, reliability, and sensitivity, making it suitable for the analysis of trace levels of benzothiazoles and benzotriazoles in drinking water.

Objective: To determine the clinical efficacy and mechanism of Piwei Peiyuan Pill (PPP) combined with moxibustion for treating patients with chronic atrophic gastritis (CAG) with spleen and stomach weakness syndrome. Methods: Ninety-six CAG patients with spleen and stomach weakness syndrome who met the inclusion and exclusion criteria were enrolled at the Department of Spleen and Stomach Diseases of the Second Affiliated Hospital of Anhui University of Chinese Medicine from June 2022 to December 2023. The patients were randomly divided into a control, a Chinese medicine, and a combined group using a random number table method, with 32 cases in each group (two cases per group were excluded). The control group was treated with rabeprazole combined with folic acid tablets (both thrice daily), the Chinese medicine group was treated with PPP (8 g, thrice daily), and the combined group was treated with moxa stick moxibustion (once daily) on the basis of the Chinese medicine group for 12 consecutive weeks. Gastric mucosa atrophy in the three groups was observed before and after treatment. The gastric mucosal pathological score was evaluated. The Patient Reported Outcome (PRO) scale was used to evaluate the patients′ physical and mental health status and quality of life.An enzyme-linked immunosorbent assay was used to detect serum tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-4, IL-10, IL-37, and transforming growth factor (TGF)-β levels in each group. Real-time fluorescence PCR was used to detect the relative expression levels of signal transducer and activator of transcription 3 (STAT3) and mammalian target of rapamycin (mTOR) mRNA in each group. Western blotting was used to detect the relative expression levels of proteins related to the STAT3/mTOR signaling pathway, and the adverse drug reactions and events were recorded and compared. Results: There was no statistical difference in age, gender, disease duration, family history of gastrointestinal tumors, alcohol consumption history, and body mass index among the three groups of patients.The total therapeutic efficacy rates of the control, Chinese medicine, and combined groups in treating gastric mucosal atrophy were 66.67% (20/30), 86.67% (26/30), and 90.00% (27/30), respectively (P<0.05). Compared to before treatment, the pathological and PRO scale scores of gastric mucosa in each group decreased after treatment, and TNF-α, IL-1β, IL-37, and TGF-β levels decreased. The relative STAT3 and mTOR mRNA expression levels, as well as the relative STAT3, p-STAT3, mTOR, and p-mTOR protein expression levels decreased (P<0.05), whereas the IL-4 and IL-10 levels increased (P<0.05). After treatment, compared to the control group, the pathological score of gastric mucosa, PRO scale score, TNF-α, IL-1β, IL-37, TGF-β content, relative STAT3 and mTOR mRNA expression levels, and relative STAT3, p-STAT3, mTOR, and p-mTOR protein expression levels in the Chinese medicine and combined groups after treatment were reduced (P<0.05), whereas the IL-4 and IL-10 levels increased (P<0.05). After treatment, compared to the Chinese medicine group, the combined group showed a decrease in relative STAT3, mTOR mRNA expression levels, and STAT3, p-STAT3, mTOR, and p-mTOR protein expression levels (P<0.05). Conclusion The combination of PPP and moxibustion may regulate the inflammatory mechanism of the body by inhibiting the abnormal activation of the STAT3/mTOR signaling pathway, upregulating related anti-inflammatory factor levels, downregulating pro-inflammatory factor expression, and increasing related repair factor expression, thereby promoting the recovery of atrophic gastric mucosa, reducing discomfort symptoms, and improving the physical and mental state of CAG patients with spleen and stomach weakness syndrome.

ObjectiveTo explore the mechanism of action of Wendantang on ApoE^-/- hyperlipidemic mice using non-targeted metabolomics technology. MethodsMale C57BL/6J mice served as the normal control group （n=6）， and they were fed with regular chow， while male ApoE^-/- mice constituted the high-fat group （n=30）， and they were fed with a 60% high-fat diet. After 11 weeks of model establishment， the mice in the high-fat group were randomly divided into the model group， simvastatin group （3.3 mg·kg^-1）， and high-dose， medium-dose， and low-dose groups of Wendantang （26， 13， 6.5 g·kg^-1， respectively， in terms of crude drug amount）， with six mice in each group. The normal control group and the model group were gavaged with an equivalent volume of normal saline， and all groups continued to be fed their respective diets， receiving daily medication for 10 weeks with weekly body weight measurements. Serum levels of total cholesterol （TC）， triglycerides （TG）， high-density lipoprotein cholesterol （HDL-C）， low-density lipoprotein cholesterol （LDL-C）， free fatty acids （NEFA）， blood glucose （GLU）， alanine aminotransferase （ALT）， and aspartate aminotransferase （AST） were detected in the mice. Pathological changes in liver tissue were observed using hematoxylin-eosin （HE） staining， and ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry （UPLC-Q-TOF-MS/MS） was employed for metabolomic analysis of mouse liver tissue. ResultsCompared to the normal control group， the model group exhibited significantly increased body weight， blood lipid levels， and liver function （P<0.05， P<0.01）， with disordered liver tissue structure， swollen hepatocytes， and accompanying vacuolar fatty degeneration and inflammatory cell infiltration. Compared to the model group， the simvastatin group and Wendantang groups showed significantly reduced body weight， TG， NEFA， GLU， ALT， and AST levels （P<0.05， P<0.01）， with a significant increase in HDL-C levels （P<0.05， P<0.01）， demonstrating a dose-dependent effect. The lesion of the liver tissue section was obviously improved after administration， tending towards a normal liver tissue morphology. Analysis of liver metabolites revealed 86 differential metabolites between the normal control group and the model group， with the high-dose group of Wendantang able to regulate 56 of these metabolites. Twenty-two differential metabolites associated with hyperlipidemia were identified， mainly including chenodeoxycholic acid， hyocholic acid， taurine， glycocholic acid， dihydroceramide， hydroxy sphingomyelin C14∶1， arachidonic acid， and linoleic acid， enriching 22 metabolic pathways， with 4 being the most significant （P<0.05）， namely primary bile acid biosynthesis， sphingolipid metabolism， unsaturated fatty acid biosynthesis， and linoleic acid metabolism pathways. ConclusionWendantang can improve blood lipid levels and liver function in ApoE^-/- hyperlipidemic mice， which may be related to the regulation of primary bile acid biosynthesis， sphingolipid metabolism， unsaturated fatty acid biosynthesis， and linoleic acid metabolism pathways.

Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

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.