Objective Magnetoreception, the remarkable ability of diverse animals to sense and utilize the geomagnetic field for orientation and navigation, remains a molecularly unresolved mystery in sensory biology. The putative magnetoreceptor (MagR, previously known as IscA1) is a highly conserved iron-sulfur protein implicated in both magnetoreception and iron metabolism; however, the functional diversity among its cross-species homologs remains poorly understood. Cellular morphology is a key genetically determined trait that can be altered through genetic or environmental modifications—a process known as cell morphology engineering. Constructing engineered cells with specific morphological features and magnetic sensitivity to achieve remote, non-invasive magnetic modulation represents a crucial goal in this field with significant application potential. Therefore, this study aims to systematically investigate the effects of MagR heterologous expression on bacterial morphology and magnetic sensing capabilities, screen for MagR-based magnetically sensitive morphology engineering pathways, and reveal the underlying molecular mechanisms. Methods We systematically screened 28 MagR homologous genes from diverse prokaryotic and animal taxa to evaluate their expression and corresponding phenotypic effects in Escherichia coli (E. coli). To compare the differential magnetic responses among bacteria expressing various recombinant MagR proteins, we utilized high-throughput automated bright-field microscopic imaging and scanning electron microscopy (SEM). Furthermore, comprehensive biochemical and biophysical characterizations of iron and iron-sulfur cluster binding were performed using Ferrozine colorimetric assays, electron paramagnetic resonance (EPR) spectroscopy, ultraviolet-visible (UV-Vis) absorption, and circular dichroism (CD) spectroscopy. Additionally, 100 mT static magnetic field (SMF) exposure experiments were conducted to assess magnetically tunable phenotypes, while the intrinsic magnetic properties of purified MagR proteins were directly measured using a superconducting quantum interference device (SQUID) magnetometer. Results Our results demonstrated that the heterologous expression of MagR homologs induced varying degrees of bacterial filamentation. From this comprehensive screen, two distinct morphological patterns were identified: hydra (Hydra vulgaris) MagR (hyMagR) promoted uniform cell elongation and filamentation, exhibiting robust magnetic sensitivity manifested as significantly enhanced filamentation under the 100 mT SMF. In contrast, pigeon (Columba livia) MagR (clMagR) induced only low-frequency, extreme filamentation (sporadically exceeding 80 μm) with a relatively weaker magnetic morphological response. Mechanistically, our data unambiguously proved that these phenotypic differences are primarily driven by distinct iron redox preferences rather than total cellular iron accumulation. Specifically, hyMagR preferentially binds ferrous iron (Fe²⁺), whereas clMagR favors ferric iron (Fe³⁺) and forms more stable iron-sulfur clusters. Intriguingly, although SQUID magnetometry showed that purified clMagR exhibited approximately five-fold higher mass magnetic susceptibility than hyMagR, its cellular magnetic response was weaker. We hypothesize that the Fe²⁺-preferred intracellular environment associated with hyMagR overexpression primes the cell for enhanced generation of reactive oxygen species (ROS) via the Fenton reaction. Exposure to an SMF synergizes with this primed redox state, triggering the bacterial SOS response and upregulating cell division inhibitors to efficiently induce uniform filamentation. Conclusion Our findings identify the Fe²⁺/Fe³⁺ redox state as a critical determinant of MagR-mediated morphological remodeling and magnetic responsiveness. This discovery suggests a potential strategy for engineering magnetically responsive cellular systems for synthetic biology applications, and provides a plausible framework, which potentially combines intrinsic protein magnetism with redox-state modulation, for further investigating the evolutionary mechanisms of MagR-mediated magnetoreception.

Objective Magnetoreception, the remarkable ability of diverse animals to sense and utilize the geomagnetic field for orientation and navigation, remains a molecularly unresolved mystery in sensory biology. The putative magnetoreceptor (MagR, previously known as IscA1) is a highly conserved iron-sulfur protein implicated in both magnetoreception and iron metabolism; however, the functional diversity among its cross-species homologs remains poorly understood. Cellular morphology is a key genetically determined trait that can be altered through genetic or environmental modifications—a process known as cell morphology engineering. Constructing engineered cells with specific morphological features and magnetic sensitivity to achieve remote, non-invasive magnetic modulation represents a crucial goal in this field with significant application potential. Therefore, this study aims to systematically investigate the effects of MagR heterologous expression on bacterial morphology and magnetic sensing capabilities, screen for MagR-based magnetically sensitive morphology engineering pathways, and reveal the underlying molecular mechanisms. Methods We systematically screened 28 MagR homologous genes from diverse prokaryotic and animal taxa to evaluate their expression and corresponding phenotypic effects in Escherichia coli (E. coli). To compare the differential magnetic responses among bacteria expressing various recombinant MagR proteins, we utilized high-throughput automated bright-field microscopic imaging and scanning electron microscopy (SEM). Furthermore, comprehensive biochemical and biophysical characterizations of iron and iron-sulfur cluster binding were performed using Ferrozine colorimetric assays, electron paramagnetic resonance (EPR) spectroscopy, ultraviolet-visible (UV-Vis) absorption, and circular dichroism (CD) spectroscopy. Additionally, 100 mT static magnetic field (SMF) exposure experiments were conducted to assess magnetically tunable phenotypes, while the intrinsic magnetic properties of purified MagR proteins were directly measured using a superconducting quantum interference device (SQUID) magnetometer. Results Our results demonstrated that the heterologous expression of MagR homologs induced varying degrees of bacterial filamentation. From this comprehensive screen, two distinct morphological patterns were identified: hydra (Hydra vulgaris) MagR (hyMagR) promoted uniform cell elongation and filamentation, exhibiting robust magnetic sensitivity manifested as significantly enhanced filamentation under the 100 mT SMF. In contrast, pigeon (Columba livia) MagR (clMagR) induced only low-frequency, extreme filamentation (sporadically exceeding 80 μm) with a relatively weaker magnetic morphological response. Mechanistically, our data unambiguously proved that these phenotypic differences are primarily driven by distinct iron redox preferences rather than total cellular iron accumulation. Specifically, hyMagR preferentially binds ferrous iron (Fe²⁺), whereas clMagR favors ferric iron (Fe³⁺) and forms more stable iron-sulfur clusters. Intriguingly, although SQUID magnetometry showed that purified clMagR exhibited approximately five-fold higher mass magnetic susceptibility than hyMagR, its cellular magnetic response was weaker. We hypothesize that the Fe²⁺-preferred intracellular environment associated with hyMagR overexpression primes the cell for enhanced generation of reactive oxygen species (ROS) via the Fenton reaction. Exposure to an SMF synergizes with this primed redox state, triggering the bacterial SOS response and upregulating cell division inhibitors to efficiently induce uniform filamentation. Conclusion Our findings identify the Fe²⁺/Fe³⁺ redox state as a critical determinant of MagR-mediated morphological remodeling and magnetic responsiveness. This discovery suggests a potential strategy for engineering magnetically responsive cellular systems for synthetic biology applications, and provides a plausible framework, which potentially combines intrinsic protein magnetism with redox-state modulation, for further investigating the evolutionary mechanisms of MagR-mediated magnetoreception.

OBJECTIVE To investigate the effects of loganin on inflammatory response and intestinal barrier damage in septic rats by regulating the Ras homolog gene family member A （RhoA）/Rho-associated coiled-coil forming protein kinase 1 （ROCK1） signaling pathway. METHODS A sepsis rat model was established by cecal ligation and puncture， and randomly divided into sepsis group， loganin low-dose group （50 mg/kg loganin， gavage）， loganin high-dose group （200 mg/kg loganin， gavage）， positive control group （0.2 mg/kg atorvastatin， intraperitoneal injection）， and loganin high-dose + lysophosphatidic acid （LPA） group （200 mg/kg loganin gavage and intraperitoneal injection of 10 mg/kg RohA activator LPA）. An additional sham surgery group was established. Each group consisted of 10 rats， and medications were administered once every 6 hours for 4 times. After 24 hours of the last intervention， the levels of serum inflammatory factors interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， and IL-1β were detected. The pathological changes of ileal tissue were observed and Chiu’s intestinal mucosal injury score was also performed. The levels of intestinal function-lactate dehydrogenase （D-lactate）， D-amino acid oxidase （DAO） and endotoxin， the percentages of zonula occludens-1 protein （ZO-1） and Occludin positive staining area， as well as protein expressions of RhoA， and ROCK1 were all detected. com RESULTS Compared with the sepsis group， the percentages of ZO-1 and Occludin positive areas increased significantly in loganin low-dose and high-dose groups； while the levels of IL-6， TNF-α， IL-1β， DAO， D-lactate and endotoxin， Chiu’s intestinal mucosal injury score as well as protein expressions of RhoA and ROCK1 decreased significantly （P＜0.05）； the destruction of rat ileal tissue was alleviated， and tissue edema and inflammatory infiltration were significantly reduced； moreover， the improvement effect in loganin high-dose group was superior to that in loganin low-dose group （P＜0.05）. Compared with loganin high-dose group， RhoA activator LPA reversed the trend of changes in the above indicators （P＜0.05）. CONCLUSIONS Loganin can alleviate inflammatory response and intestinal barrier damage in septic rats， the mechanism of which may be associated with inhibiting RhoA/ROCK1 signaling pathway.

Objective To investigate the application value of 320-slice wide-detector multi-phase left at-rial appendage computed tomography angiography(LAA-CTA)with pulmonary artery(PA)monitoring in the preoperative evaluation of left atrial appendage closure.Methods A retrospective analysis was conducted on the clinical data of 110 patients who underwent LAA-CTA before left atrial appendage closure.Among them,47 patients underwent single-phase enhanced scanning with superior vena cava(SVC)monitoring(con-trol group),and 63 patients underwent multi-phase enhanced scanning with pulmonary artery monitoring(study group).The differences in imaging effects of the left atrial appendage under different monitoring points and phase imaging methods were compared,as well as the accuracy of comparing with the diagnostic results of transesophageal ultrasound(TEE),and the differences in the presentation of thrombus,perithrombus,and hypoperfusion areas in the left atrial appendage.Results The study group could comprehensively display the multi-phase CT value changes of different components(thrombus,peri-thrombotic viscosity,normal blood)within the left atrial appendage cavity,and its evaluation of lesion size and progression was superior to that of the control group.Using TEE as the gold standard,the study group demonstrated better diagnostic ac-curacy for different components and normal regions within the left atrial appendage cavity compared to the control group(P<0.001).Additionally,the study group improved the detection rate of peri-thrombotic vis-cosity,clearly delineated thrombus boundaries,and enhanced diagnostic accuracy(P<0.001).Conclusion Multi-phase LAA-CTA with pulmonary artery monitoring can effectively evaluate the morphological dimensions,thrombus,and peri-thrombotic CT manifestations of the left atrial appendage.It is simple to operate,with an accuracy rate close to the gold standard,providing reliable imaging evidence for preoperative evaluation of left atrial appendage closure.

Objective Exploring the diagnostic value of T-cell enzyme-linked immunospot assay(T-SPOT.TB)combined with rifampicin-resistant Mycobacterium tuberculosis real-time fluorescence quantitative nucleic acid ampli-fication detection(XpertMTB/RIF)in geriatric AIDS patients with Mycobacterium tuberculosis(MTB)infection.Methods From May 2022 to May 2024,86 elderly patients with AIDS suspected MTB in Hengshui Third People's Hospital were gathered and separated into AIDS complicated with MTB(research group)and AIDS without MTB(control group)according to the pathological examination results.MTB culture,T-SPOT.TB and XpertMTB/RIF were performed.Kappa analysis was applied to evaluate the consistency between T-SPOT.TB combined with Xpert-MTB/RIF and the gold standard for diagnosing MTB coinfection in AIDS patients.ROC curve and four grid table were plotted to analyze the value of the combination of T-SPOT.TB and XpertMTB/RIF in the diagnosis of AIDS complicated with MTB infection.Results The blood γ-interferon,the positive detection rates of T-SPOT.TB and XpertMTB/RIF in the research group were higher than those in the control group(P＜0.05).The AUC of T-SPOT.TB in diagnosing AIDS with MTB infection was 0.810,that of Xpert MTB/RIF in diagnosing AIDS with MTB infection was 0.835,and the AUC of the two in diagnosing AIDS with MTB infection was 0.910.The Kappa values of T-SPOT.TB,Xpert MTB/RIF and their combined diagnosis for AIDS with MTB infection were 0.624,0.674 and 0.825,respectively.The accuracy of T-SPOT.TB in the diagnosis of AIDS with MTB was 82.56%,the accuracy of XpertMTB/RIF in the diagnosis of AIDS with MTB was 84.88%,and the accuracy of the combined di-agnosis for AIDS with MTB was 91.86%.Conclusions T-SPOT.TB combined with XpertMTB/RIF can improve the accuracy of diagnosis of AIDS with MTB,and can be used as a clinical auxiliary diagnosis method for AIDS pa-tients complicated with MTB.

Objective:To explore the efficacy of deep learning-based automatic segmentation technology in the segmentation of hepatocellular carcinoma (HCC) lesions using gadoxetate disodium-enhanced MRI (EOB-MRI), and to investigate the prognostic value of radiomics analysis in predicting patient outcomes.Methods:This was a cross-sectional, retrospective study that collected data from 352 patients with solitary HCC who underwent imaging at the Harbin Medical University Cancer Hospital between June 2015 and May 2023. The patients were randomly divided into a training set ( n=213) and a validation set ( n=139) in a 3∶2 ratio using weighted random sampling. Two radiologists manually annotated the lesions. Hepatobiliary-phase EOB-MRI images were standardized, and six deep learning models,nnU-Net, nnFormer, UnetR, Swin-UnetR, UnetR++ and MedNeXt,were trained for automatic segmentation on the training set. The segmentation performance was evaluated on the validation set, and the segmentation efficacy was assessed using the Dice coefficient and 95% Hausdorff distance (HD 95), identifying of the optimal model. Radiomics features were extracted from both manual and automatic segmentation regions, and the radiomics score (Radscore) was calculated to stratify patients into high-risk and low-risk groups. Kaplan-Meier curves and log-rank tests were used to analyze the differences in relapse-free survival (RFS) and overall survival (OS) between the different stratified groups. Results:Among the automatic segmentation models, the MedNeXt model performed best in the validation set, with a Dice coefficient of 76.0%, HD 95 of 7.2, and a segmentation success rate of 90.6% (126/139). The nnFormer model was the second-best, with a Dice coefficient of 75.3%, HD 95 of 10.1, and a segmentation success rate of 89.9% (125/139). Other models showed Dice coefficients ranging from 66.3% to 74.1%. A MedNext-nnF model was established by combining the MedNeXt and nnFormer models, achieving a Dice coefficient of 78.2%, HD 95 of 5.9, and a segmentation success rate of 92.1% (128/139) in the validation group. After constructing the automatic segmentation radiomics prognostic model, patients were stratified by Radscore. Both manual and automatic segmentation models showed statistically significant differences in RFS and OS between different risk groups ( P<0.001). Conclusions:The Mednext-nnF fusion model enables efficient and automated segmentation of HCC lesions in EOB-MRI. The radiomics model constructed based on the automated segmentation demonstrates strong performance in predicting and stratifying prognostic risk.

AIM To investigate the variations in mercury dissolution from HgS-containing traditional medicines in three kinds of simulated gastrointestinal dissolution media.METHODS 39 batches of 15 types of HgS-containing traditional medicines were collected,total mercury content and dissolved mercury concentrations in simulated gastric fluid,simulated intestinal fluid,and L-cysteine-containing simulated intestinal fluid were measured.The maximum daily intake of total mercury and soluble mercury was calculated based on the maximum daily clinical dosage.RESULTS Among the 15 types of medicines,the maximum daily intake of total mercury varied by 156 times,the daily intake of soluble mercury varied by 3 502 times in simulated gastric fluid,313 times in simulated intestinal fluid,and 10 663 times in L-cysteine-containing simulated intestinal fluid,approximately.CONCLUSION For the 15 types of HgS-containing traditional medicines,the daily maximum intake of soluble mercury showed greater variations than that of total mercury.Soluble mercury concentration is more closely correlated with intestinal absorption of mercury and thus represents a more rational quality control indicator for HgS-containing traditional medicines.

OBJECTIVE: Evaluate the clinical application effect of low-field infant MRI. METHODS: Using literature review, expert consultation, and two rounds of Delphi to determine the evaluation index system. Then retrospectively analyze and compare the data of low-field infant MRI and high-field MRI from January 2023 to December 2024. RESULTS: There is a certain gap between low-field infant MRI and high-field MRI in terms of signal-to-noise ratio, image uniformity, software system reliability, scanning time, user interface friendliness and image result consistency. However, there was no difference in terms of spatial resolution and image quality. The noise, hardware system reliability, mean time between failure and the rate of examination completed without sedation are better than that of high-field MRI. CONCLUSION Low-field infant MRI meets needs of clinical diagnostic and has stable performance. It can be used as a routine screening tool for brain diseases near the bed.
Magnetic Resonance Imaging/methods* ; Humans ; Infant ; Retrospective Studies ; Signal-To-Noise Ratio ; Reproducibility of Results ; Brain Diseases/diagnostic imaging* ; Brain/diagnostic imaging* ; Software

OBJECTIVES: Keloids are fibrotic skin disorders characterized by excessive collagen deposition and a high recurrence rate, closely associated with inflammatory mediators. However, existing epidemiological studies are limited by confounding factors and reverse causality, making it difficult to establish causation. This study aims to investigate the causal relationship between circulating cytokines and keloids using Mendelian randomization analysis. METHODS: Significant single nucleotide polymorphisms (SNPs) associated with circulating cytokines (exposures) and keloids (outcomes) were extracted from genome-wide association study (GWAS) summary datasets. Eligible SNPs were selected as instrumental variables (IVs). Exposure data were derived from a cytokine GWAS including 8 293 Finnish participants, and outcome data from a keloid GWAS based on the UK Biobank. The inverse-variance weighted (IVW) method served as the primary analytical approach to estimate causal effects, supplemented by weighted median (WME), MR-Egger regression, and other sensitivity analyses. Horizontal pleiotropy was assessed using MR-Egger regression and the MR pleiotropy residual sum and outlier (MR-PRESSO) test, while Cochran's Q test evaluated heterogeneity. Leave-one-out analysis was used to verify robustness and consistency. A reverse MR analysis was also conducted, with keloid as the exposure and cytokines as outcomes, to rule out reverse causation. RESULTS: IVW analysis identified significant positive causal associations between two cytokines and keloids-macrophage migration inhibitory factor (MIF) [odds ratio (OR)=2.081, 95% confidence interval (CI) 1.219 to 3.552, P=0.007] and monocyte chemoattractant protein-1 (MCP-1) (OR=1.673, 95% CI 1.036 to 2.701, P=0.035). Conversely, stem cell factor (SCF) showed a negative causal relationship with keloids (OR=0.518, 95% CI 0.269 to 0.998, P=0.049). Results from the MR-Egger and weighted median analyses were consistent with IVW findings. No evidence of horizontal pleiotropy was observed (P>0.05). Except for interleukin-6 (P=0.014), no heterogeneity was detected in other cytokines. Leave-one-out analysis further confirmed the robustness of the causal associations. In reverse MR analysis, keloids were causally related only to β-nerve growth factor (beta-NGF) (OR=1.048, 95% CI 1.002 to 1.095, P=0.039), with no heterogeneity or pleiotropy detected in most cytokines (P>0.05). CONCLUSIONS MIF and MCP-1 exhibit positive causal associations with keloid formation, while SCF shows a negative causal relationship. These findings provide new evidence for the causal involvement of inflammatory cytokines in keloid pathogenesis and offer potential molecular targets for developing novel keloid therapies.
Humans ; Keloid/blood* ; Mendelian Randomization Analysis ; Cytokines/genetics* ; Polymorphism, Single Nucleotide ; Genome-Wide Association Study ; Chemokine CCL2/genetics* ; Interleukin-6/genetics* ; Macrophage Migration-Inhibitory Factors/genetics* ; Male ; Stem Cell Factor/blood* ; Female ; Intramolecular Oxidoreductases

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