Traditional Chinese medicine and its bioactive components have garnered increasing attention as potential therapeutic options for allergic asthma. By targeting the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway, these natural compounds exhibit unique advantages in multilevel immunomodulation and inflammation suppression compared with single-target synthetic drugs. Accumulating pharmacological evidence supports their capacity to restore pathway homeostasis, positioning them as promising candidates for complementary strategies in asthma management. Allergic asthma, a heterogeneous respiratory disorder affecting approximately 150 million individuals worldwide, arises from a complex interplay of genetic predisposition, environmental exposures, and lifestyle factors. Its pathological progression is marked by aberrant activation of the PI3K/AKT signaling cascade, with the mechanistic target of rapamycin serving as a key downstream regulatory node. This evolutionarily conserved pathway orchestrates fundamental cellular processes that contribute to three hallmark pathological features of allergic asthma: chronic airway inflammation, structural remodeling of the bronchial architecture, and airway hyperresponsiveness. This review has 3 primary objectives: (1) to evaluate the role of the PI3K/AKT pathway in allergic asthma pathogenesis, (2) to analyze the molecular mechanisms of representative traditional Chinese medicine preparations and their active ingredients, and (3) to identify novel bioactive inhibitors derived from natural products. Collectively, these investigations provide a conceptual framework for the development of next-generation targeted therapies and for optimizing clinical management strategies for allergic asthma.

Objective To construct a lung cancer surgery-oriented disease-specific database covering the entire perioperative care pathway, thereby improving the quality and usability of key surgical data elements. Methods Real-world clinical data were extracted from a single-center thoracic surgery department. A standardized data model was established based on the open electronic health record (openEHR) standard. Large language model (LLM), optical character recognition (OCR), and artificial intelligence (AI)-driven techniques were employed to extract, structure, and perform quality control on unstructured clinical narratives, imaging reports, and radiological data, with a focus on capturing surgically relevant perioperative indicator. Results A multimodal database comprising 19 917 patients was established, including 7 930 males and 11 987 females, with ages ranging from 15 to 97 (61.7±9.7) years. The database includes 582 structured data variables, textual report data corresponding to 69 clinical indicators, 13 000 pulmonary function test PDF reports, and chest CT imaging data from 16 884 patients. This database comprehensively covers major information relevant to surgical diagnosis and treatment of lung cancer, significantly improving the completeness and granularity of surgical detail data. Large language models (LLMs) and optical character recognition (OCR) technologies enhanced the efficiency of converting unstructured data into structured formats, while a multi-level manual verification process ensured data accuracy and traceability. The database supports real-world research including comparisons of surgical procedures, prediction of postoperative complications, prognosis assessment, and multimodal data association analyses.

Childhood adversity is a risk factor for developing depressive symptoms or suffering from depressive disorders across the life course. However， existing evidence has focused on the analysis of the association between childhood adversity and depression from a single theoretical perspective， and there is a lack of comprehensive understanding of its multiple action pathways. By systematically summarizing the cumulative risk model， sensitive-period model， recency model， risk chain model， and the stress sensitization， stress amplification， and stress inoculation hypotheses， as well as the emerging pubertal stress recalibration hypothesis， this article attempts to construct an integrated theoretical framework to elucidate the internal logical synergy among these models. Furthermore， by reviewing relevant empirical evidence， this article analyzes the applicability and limitations of different theoretical models in illustrating the underlying psychopathological mechanisms of depression. The current evidence suggests that the impact of childhood adversity on the risk of psychopathology may be driven by a complex dynamic process involving the interaction of exposure timing， cumulative load， response boundaries of biological systems， and modification by later environments. Future research should utilize longitudinal cohorts and multimodal data within a unified analytical framework to comprehensively examine the multiple pathways through which childhood adversity affects the risk of depression. This will provide a reference for precisely identifying high-risk populations， targeting "recalibration windows" represented by adolescence， developing focused intervention strategies， and ultimately blocking the cascading effects of early life adversity as early as possible. ［Funded by the "Pioneer" and "Leading Goose" R&D program of Zhejiang Province （number， 2024C03006）］

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.

ObjectiveTo explore the 28-day mortality risk of Klebsiella pneumoniae (KP) bloodstream infections (BSI) and its related influencing factors, thereby providing a scientific basis for the effective control of KP-BSI and improvement of patient prognosis. MethodsFrom January 2018 to December 2024, a retrospective review was conducted on hospitalized patients aged >18 years old treated for KP-BSI at a tertiary hospital in Hangzhou. Logistic and Cox regression analyses were performed to identify the epidemiological characteristics and the risk factors for the 28-day mortality associated with KP-BSI. ResultsA total of 123 patients with KP-BSI were included in this study, comprising 64 cases infected with carbapenem-resistant Klebsiella pneumoniae (CRKP) and 59 cases infected with carbapenem-susceptible Klebsiella pneumoniae (CSKP). Compared with CSKP-BSI, patients with CRKP-BSI more frequently presented with chronic pulmonary disease (χ²=4.29, P=0.038), concomitant infections at other sites (χ²=10.90, P=0.001), and a higher frequency of invasive procedures prior to infection (central venous catheterization, mechanical ventilation, and indwelling urinary catheter), as well as glucocorticoid use, hemodialysis, and blood transfusion (all P<0.05). The 28-day mortality was significantly higher in BSI cases caused by CRKP compared to that caused by CSKP (37.50% vs 5.08%, P<0.001). Cox regression analyses revealed that carbapenem resistance (HR=6.67, 95%CI: 1.48‒30.08, P=0.014) and blood transfusion (HR=3.58, 95%CI: 1.15‒11.19, P=0.028) were risk factors for the 28-day mortality in KP-BSI, while removal of central venous catheters after infection (HR=0.24, 95%CI: 0.08‒0.67, P=0.006) was associated with a reduced risk for the 28-day mortality. ConclusionCarbapenem resistance is associated with mortality outcomes in patients with KP-BSI. Strengthening infection control measures targeting the identified risk factors for CRKP-BSI may improve patient prognosis.

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.

Atherosclerotic plaques arise within the vessels， yet their morphological evolution parallels that of carbuncles and abscesses. Drawing on the pathomechanism progression of carbuncles and abscesses， this paper proposes a dynamic pathogenesis model of "constraint-putrefaction-ulceration" for atherosclerotic plaques. The formation of atherosclerotic plaque begins with phlegm-turbidity obstruction and qi-blood stagnation （constraint stage）； prolonged constraint transforms into heat that scorches the vessels and forms plaques （putrefaction stage）； ultimately， toxin invades inward and ruptures the vessels， injuring the heart （ulceration stage）. Accordingly， staged differentiation and treatments are proposed. During the constraint stage， the focus is venting constraint and unblocking collaterals， clearing and relieving constraint-heat， for which Yue Jyu Pills （越鞠丸） and Danzhi Xiaoyao Powder （丹栀逍遥散） with modifications can be used. During the putrefaction stage， the method is clearing heat and dispelling abscesses， resolving stasis and dissipating knot， with modified Wuwei Xiaodu Beverage （五味消毒饮） and Xuefu Zhuyu Decoction （血府逐瘀汤）. During the ulceration stage， it is suggested to expell toxin and stasis， benefit qi and nourish yin using modified Gualou Xiebai Banxia Decoction （瓜蒌薤白半夏汤） and Sheng Mai Powder （生脉散）. This framework offers new perspectives for the differentiation and treatment of atherosclerotic plaques in traditional Chinese medicine.

Background Lead (Pb) is a highly toxic heavy metal that accumulates in the body, potentially leading to multi-systemic impairment. Compared with traditional statistical methods, machine learning techniques offer unique advantages, opening new avenues for occupational health risk assessment and the exploratory analysis of complex associations. Objective To examine the association between occupational lead exposure and multiple health indicators and to identify key risk factors for lead toxicity. Methods A cross-sectional study was conducted, integrating occupational hygiene investigation results from 16 lead-acid battery enterprises in Jiangsu Province with occupational health examination data from 1914 lead-exposed workers. Inter-group differences were analyzed using the χ² test or Fisher's exact test. Binary logistic regression and machine learning algorithms [CatBoost, Naive Bayes model (NBM), and random forest (RF)] were employed to evaluate the association between blood lead (PbB), urine lead (PbU), and health indicators including blood pressure (BP), red blood cell count (RBC), and alanine aminotransferase (ALT). Results The prevalence of abnormal PbB and PbU were 14.52% and 9.35%, respectively. The risks of abnormal BP, RBC, and ALT were significantly increased in the population with high lead levels (P<0.05). PbB abnormalities were closely associated with gender, environmental lead concentration, wearing masks, smoking, and alcohol consumption (P<0.05). Regarding occupational hazards, workers exposed to lead dust had a 1.98-fold risk of PbU abnormality compared to those exposed to lead fumes. The plate coating and acid leaching process posed the highest risk for both PbB (OR=8.81) and PbU (OR=5.46) abnormalities compared with assembly process. Furthermore, the risks of PbB and PbU abnormalities were significantly elevated among workers with abnormal BP, RBC or ALT (P<0.05). Among the models, CatBoost performed best in predicting RBC abnormality (accuracy: 95.8%; precision: 44.9%; F1 score: 0.952; AUC: 0.981). Feature importance analysis identified PbB and PbU as the core factors affecting abnormal RBC and ALT, while RBC and ALT abnormalities as key features for predicting the risk of PbB and PbU abnormalities. Conclusion By integrating traditional statistical methods with machine learning, this study reveals a complex bidirectional association between occupational lead exposure and multiple health indicators, and identifies gender, job category, and environmental Pb concentration as the key factors influencing PbB abnormalities. These findings provide a scientific foundation for the implementation of precision occupational health management models.

Background Benign prostatic hyperplasia (BPH) is a common chronic urinary disease in middle-aged and older men, yet the impact of long-term exposure to atmospheric particulate matter (PM) on its pathogenesis remains unclear. Objective To investigate the association between PM exposure and the risk of incident BPH in middle-aged and older men. Methods Based on four waves of follow-up data (2011–2018) from the China Health and Retirement Longitudinal Study (CHARLS), 4766 participants were enrolled. Robust Poisson regression models were employed to assess the association between exposure to PM (PM₁, PM_2.5, and PM₁₀) and the risk of incident BPH. Relative risks (RR) and their corresponding 95% confidence intervals (95%CI) were calculated. Dose-response relationships were fitted using restricted cubic splines (RCS). Subgroup analyses were performed to explore potential effect modifications, and multiple imputation was used to handle missing data. Results Over a mean follow-up of 6 years, 914 incident BPH cases were identified among the4766 participants (cumulative incidence: 19.18%). After adjusting for confounders, each 10 μg·m⁻³ increase in PM₁, PM_2.5, and PM₁₀ concentrations was associated with a 13.1% (RR=1.131, 95%CI: 1.063, 1.203), 8.5% (RR=1.085, 95%CI: 1.050, 1.122), and 5.1% (RR=1.051, 95%CI: 1.034, 1.069) increased risk of BPH, respectively. RCS analysis showed that no nonlinear relationship was found between PM₁ and PM_2.5 and the risk of BPH (P>0.05); however, a nonlinear association was observed for PM₁₀ (P=0.03), with the risk increment slowing beyond 100 μg·m⁻³. Subgroup and sensitivity analyses confirmed the robustness of these findings. Conclusion Long-term exposure to ambient particulate matter may be associated with an increased risk of incident BPH in middle-aged and older men.

Background Lead (Pb) is a highly toxic heavy metal that accumulates in the body, potentially leading to multi-systemic impairment. Compared with traditional statistical methods, machine learning techniques offer unique advantages, opening new avenues for occupational health risk assessment and the exploratory analysis of complex associations. Objective To examine the association between occupational lead exposure and multiple health indicators and to identify key risk factors for lead toxicity. Methods A cross-sectional study was conducted, integrating occupational hygiene investigation results from 16 lead-acid battery enterprises in Jiangsu Province with occupational health examination data from 1914 lead-exposed workers. Inter-group differences were analyzed using the χ² test or Fisher's exact test. Binary logistic regression and machine learning algorithms [CatBoost, Naive Bayes model (NBM), and random forest (RF)] were employed to evaluate the association between blood lead (PbB), urine lead (PbU), and health indicators including blood pressure (BP), red blood cell count (RBC), and alanine aminotransferase (ALT). Results The prevalence of abnormal PbB and PbU were 14.52% and 9.35%, respectively. The risks of abnormal BP, RBC, and ALT were significantly increased in the population with high lead levels (P<0.05). PbB abnormalities were closely associated with gender, environmental lead concentration, wearing masks, smoking, and alcohol consumption (P<0.05). Regarding occupational hazards, workers exposed to lead dust had a 1.98-fold risk of PbU abnormality compared to those exposed to lead fumes. The plate coating and acid leaching process posed the highest risk for both PbB (OR=8.81) and PbU (OR=5.46) abnormalities compared with assembly process. Furthermore, the risks of PbB and PbU abnormalities were significantly elevated among workers with abnormal BP, RBC or ALT (P<0.05). Among the models, CatBoost performed best in predicting RBC abnormality (accuracy: 95.8%; precision: 44.9%; F1 score: 0.952; AUC: 0.981). Feature importance analysis identified PbB and PbU as the core factors affecting abnormal RBC and ALT, while RBC and ALT abnormalities as key features for predicting the risk of PbB and PbU abnormalities. Conclusion By integrating traditional statistical methods with machine learning, this study reveals a complex bidirectional association between occupational lead exposure and multiple health indicators, and identifies gender, job category, and environmental Pb concentration as the key factors influencing PbB abnormalities. These findings provide a scientific foundation for the implementation of precision occupational health management models.