Objective To analyze the infection status and epidemic trends of soil-borne nematode infections in Yubei, Chongqing City, in 2010,2021, and 2022. Methods The local populations from four survey sites of four towns in 2010 and five sites of five towns in 2021 and 2022 were surveyed regarding their basic information using a unified form. Fecal samples of the participants were collected and tested for soil-borne nematode infections using the modified Kato-Katz thick smear method. Results In 2010, 2 049 participants were surveyed, followed by 1 000 participants in 2021 and 2022. The overall prevalence of parasitic infections declined significantly from 3.86% to 0.20%. In 2010, soil-transmitted nematode included hookworms(3.81%) and roundworms(0.29%). In 2021, the infection rates of roundworms and hookworms were 1.70% and 0.10% respectively. Notably, only Ascaris was identified in 2022(0.20%). The≥60 age group consistently exhibited the highest infection rates across all surveys, followed by the 40-59 age group. The infection rates of males in the three surveys were 3.31%,1.92%, and 0.20% respectively, and those of females were 4.37%, 1.46%, and 0.20% respectively. There was no statistically significant difference in the infection rates between males and females. Educational attainment was inversely associated with infection; in 2010, the highest prevalence was observed among those with primary education or below, whereas in 2021, illiterate or semi-literate individuals showed the highest susceptibility. The occupational distribution of infections in 2010 indicated that retirees (8.33%), farmers(4.86%), and homemakers or unemployed individuals(3.45%)were the most affected. However, in 2021 and 2022, farmers emerged as the predominant occupational group with soil-transmitted nematode infections. Conclusions The infection rate of soil-borne nematodes showed a decreasing trend in Yubei, and the infection species changed from hookworms in 2010 to Ascaris in 2022. Farmers, the elderly, and people with low education levels should continue to be the focus of preventive and control efforts.

Objective To understand mosquito density, species composition, and seasonal variations around the Beijing Capital International Airport(BCIA)and provide scientific evidence for mosquito-borne disease prevention and control. Methods Meteorological data were collected from 2016 to 2022, and mosquito density was monitored at seven surveillance sites around the BCIA from May to October each year using CO2-baited mosquito traps to analyze the relationship between mosquito density and meteorological factors. Results In total,68 518 female mosquitoes were captured, with a mosquito density of 10.20 per light · hour. The dominant species was Culex pipiens pallens(73.90%), followed by Aedes albopictus(9.71%)and Ae. vexans(9.29%);the mosquito density was highest in 2016(14.8 per light·hour)and lowest in 2022(4.46 per light·hour). Significant statistical differences were observed in mosquito density among different species(F=18.118, P<0.05). The density of Cx. pipiens pallens and other mosquito species showed a mutually exclusive trend; the peak of mosquito density varied based on years and habitats, with significant statistical differences in mosquito density among different habitats(F=8.504, P<0.05). The highest mosquito density was observed near the Wenyu River(21.50 per light·hour), whereas the lowest was recorded at the BCIA construction site(2.44 per light· hour). The monthly average temperature(r=0.595), monthly average highest temperature(r=0.575), and monthly average lowest temperature(r=0.624)showed moderate positive correlations, whereas the monthly average air pressure(r=-0.484)showed a moderate negative correlation. The monthly average minimum temperature was included in the regression model(F=25.575, P<0.000), and the equation Y=1.029X4-8.181 was used. The monthly average air pressure 2 months prior(b=-2.418, β=-1.619, P<0.05)and the monthly average relative humidity(b=-0.739, β=-1.201, P<0.05)significantly negatively predicted the mosquito density. Notably, the regression equation used was y=2526.170+(-2.418)X9+(-0.739)X13. The exposure-response analysis revealed that the density of mosquitoes was not linearly related to the average monthly air pressure, rainfall, and duration of sunshine. Conclusions The dominant mosquito species around the BCIA was Cx. pipiens pallens, Ae. albopictus, and Ae. vexans. During the control of Cx. pipiens pallens, the impact on other mosquito species must also be considered. Environmental management, breeding sites, and scientific use of pesticides should be prioritized based on the activity periods of mosquitoes. Further studies on mosquitoes and meteorological factors should be conducted to provide references and novel avenues for mosquito control.

Peripheral nerve injury (PNI) severs peripheral connections and induces profound reorganization of primary somatosensory (S1) and motor (M1) cortical maps, a dynamic process that critically shapes the extent and quality of functional recovery. Brain-derived neurotrophic factor (BDNF) has emerged as a central molecular hub linking the initial peripheral insult to subsequent central plastic changes. This review systematically examines BDNF-mediated cortical remodeling following PNI, with a particular emphasis on its spatiotemporally specific regulatory mechanisms and the therapeutic opportunities they present. Immediately after PNI, deafferented cortical territories undergo rapid functional silencing and, over time, become progressively encroached upon by adjacent intact representations. This maladaptive reorganization—characterized by sensory map fusion and motor compensatory invasion—actively impedes successful reinnervation and optimal recovery. Such phenomena have been consistently documented in both non-human primate models and human fMRI studies, spanning sensory and motor modalities. Motor remapping is also prominently observed in facial nerve injury and chronic nerve compression models, underscoring the universality of these plastic changes. Notably, a range of interventions including regional local anesthesia, auditory-tactile substitution, and guided tactile imagery can effectively mitigate this aberrant remodeling, revealing promising avenues for the therapeutic modulation of cortical plasticity during critical post-injury windows. BDNF coordinates post-PNI cortical remodeling through three mechanistically interconnected pathways. First, BDNF drives dendritic and axonal growth via TrkB receptor signaling: retrogradely transported axonal signaling endosomes integrate CREB-dependent transcriptional programs with mTOR-mediated local protein synthesis to bolster dendritic arborization and axonal elongation, while BDNF-induced Limk1 translation precisely fine-tunes actin cytoskeletal dynamics, thereby stabilizing dendritic spine morphology. Second, BDNF governs GABAergic interneuron maturation and perineuronal net formation through the JNK signaling cascade, thereby acting as a “plasticity brake” that preserves the delicate excitatory/inhibitory balance and constrains indiscriminate, maladaptive rewiring. Third, BDNF robustly potentiates NMDAR-dependent long-term potentiation (LTP) by elevating NMDA channel open probability and increasing synaptic receptor density, thus tightly coupling structural remodeling with functional synaptic strengthening. Concurrently, long-term depression pathways are subject to modulation, with NMDAR antagonism exerting time-dependent, bidirectional effects that vary with the post-injury phase. Superimposed on these mechanisms, the functionally antagonistic actions of proBDNF/p75NTR (favoring pruning and growth cone collapse) and mature BDNF/TrkB (promoting survival and stabilization) add further layers of regulatory complexity, highlighting the absolute necessity for precise spatiotemporal control in any therapeutic strategy. Extending our previous experimental findings, we introduce a “cortical reclamation threshold” model. In this framework, early upregulation of BDNF-TrkB signaling enhances inhibitory tone to restrict cortical encroachment by neighboring intact regions, whereas delayed but precisely timed BDNF delivery following surgical nerve repair actively facilitates the reclamation of the original, deafferented cortical maps by regenerating afferent fibers. Importantly, the common BDNF Val66Met polymorphism may significantly modulate individual thresholds, providing a biologically grounded rationale for patient stratification and personalized timing of intervention. Despite this mechanistic promise, clinical translation faces considerable hurdles. These include inherently poor pharmacokinetics of BDNF, severely limited penetration across the blood-brain barrier, dose-dependent neurotoxicity mediated through the p75NTR receptor, and the overarching dual-edged nature of BDNF signaling, which can simultaneously promote both adaptive and maladaptive plasticity. Emerging delivery platforms——including engineered exosomes, polymeric nanoparticles, cryogel microcarriers, and adeno-associated virus (AAV)-based gene therapy——offer potential solutions, but all require rigorous pharmacokinetic, toxicological, and functional validation. Future research priorities should encompass: (1) comprehensive single-cell and spatial transcriptomic mapping to resolve BDNF cellular sources and real-time signaling dynamics across distinct cortical layers and cell types; (2) genotype-stratified dose-response studies to establish safe, effective, and personalized delivery protocols; and (3) large-scale multicenter clinical trials that seamlessly integrate BDNF-targeted interventions with multimodal rehabilitation strategies, such as repetitive transcranial magnetic stimulation and mirror therapy, with longitudinal neuroimaging serving as a biomarker for cortical reclamation. By systematically addressing these priorities, we aim to transform BDNF from a critical endogenous regulator into a precisely controllable and titratable therapeutic target, ultimately enabling a paradigm shift from mere structural repair to true functional neural reconstruction following PNI.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Background: The aim of this study is to evaluate the prevalence and associated psychosocial factors of behavioral addictions among school adolescents living in the Gwangju and Jeonnam regions in Korea. Methods: A self-reported survey was conducted from December 4, 2023, to January 31, 2024, including 855 middle and high school students residing in the Gwangju-Jeonnam regions. Aside from the information on demographic characteristics, data on depression, anxiety, Internet gaming addiction, gambling problems, and resilience was obtained. Results: The prevalence of Internet gaming addiction among adolescents was 5.4%, while the prevalence of gambling problems was 3.3%. The male adolescents had a significantly higher risk of behavioral addiction compared with the female adolescents. The logistic regression analysis revealed that male and depression were significant risk factors for Internet gaming addiction. For gambling problems, male was identified as a significant risk factor. Conclusion The findings of this study suggested that the prevalence of behavioral addiction among school adolescents has been relatively higher than that of previous studies, emphasizing the need for community-based prevention and intervention strategies tailored to the sex difference and psychological factors associated with adolescent behavioral addictions.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Background: The aim of this study is to evaluate the prevalence and associated psychosocial factors of behavioral addictions among school adolescents living in the Gwangju and Jeonnam regions in Korea. Methods: A self-reported survey was conducted from December 4, 2023, to January 31, 2024, including 855 middle and high school students residing in the Gwangju-Jeonnam regions. Aside from the information on demographic characteristics, data on depression, anxiety, Internet gaming addiction, gambling problems, and resilience was obtained. Results: The prevalence of Internet gaming addiction among adolescents was 5.4%, while the prevalence of gambling problems was 3.3%. The male adolescents had a significantly higher risk of behavioral addiction compared with the female adolescents. The logistic regression analysis revealed that male and depression were significant risk factors for Internet gaming addiction. For gambling problems, male was identified as a significant risk factor. Conclusion The findings of this study suggested that the prevalence of behavioral addiction among school adolescents has been relatively higher than that of previous studies, emphasizing the need for community-based prevention and intervention strategies tailored to the sex difference and psychological factors associated with adolescent behavioral addictions.

Objective: To systematically analyze the revisions content and technological development trends of microbiological standards in the Chinese Pharmacopoeia (ChP) 2025 Edition, and explore its novel requirements in risk-based pharmaceutical product lifecycle management.
Methods: A comprehensive review was conducted on 26 microbiological-related standards to summarize the revision directions and scientific implications from perspectives including the revision overview, international harmonization of microbiological standards, risk-based quality management system, and novel tools and methods with Chinese characteristics.
Results: The ChP 2025 edition demonstrates three prominent features in microbiological-related standards: enhanced international harmonization, introduced emerging molecular biological technologies, and established a risk-based microbiological quality control system.
Conclusion: The new edition of the Pharmacopoeia has systematically constructed a microbiological standard system, which significantly improves the scientificity, standardization and applicability of the standards, providing a crucial support for advancing the microbiological quality control in pharmaceutical industries of China.

BACKGROUND:Preliminary research by our group suggests that targeting fibroblast growth factor receptor 1(FGFR1)may be an effective strategy for treating RA. OBJECTIVE:To investigate the effects of an FGFR1 inhibitor(PD173074)on bone destruction in rats with collagen-induced arthritis. METHODS:Twenty-five female Sprague-Dawley rats were randomly divided into five groups:normal control group,model group,methotrexate group,low-dose PD173074 group,and high-dose PD173074 group.Except for the normal control group,rat models of type Ⅱ collagen-induced arthritis were made in each group.After successful modeling,rats were injected intraperitoneally with sterile PBS in the normal and model groups,1.04 mg/kg methotrexate in the methotrexate group,and 5 and 20 mg/kg in the low-dose group and high-dose PD173074 groups,once a week.After 4 weeks of drug administration,clinical symptoms and joint swelling in rats were observed.Micro-CT was used for three-dimensional reconstruction and analysis of the ankle joints.Pathological changes in the ankle joints were observed.Periarticular angiogenesis and the expression of receptor activator of nuclear factor-Κb ligand were detected.The expression levels of p-FGFR1,vascular endothelial growth factor A,and tartrate-resistant acid phosphatase in the synovial membrane were measured.Pathological changes in the liver,spleen,and kidney were observed and liver,spleen,and kidney indices were calculated. RESULTS AND CONCLUSION:PD173074 could alleviate clinical symptoms and joint swelling,delay bone loss,improve bone structure,reduce synovial invasion and cartilage bone erosion,reduce the number of periarticular osteoclasts,inhibit angiogenesis in synovial tissues,reduce the expression of receptor activator of nuclear factor-Κb ligand,and inhibit the expression of FGFR1 phosphorylated protein,tartrate-resistant acid phosphatase and vascular endothelial growth factor A.Pathologic observation of the liver,spleen and kidney in rats showed no obvious toxic side effects after PD173074 treatment.To conclude,the FGFR1 inhibitor can delay the progression of joint inflammation and bone destruction and inhibit angiogenesis in the rat model of type Ⅱ collagen-induced arthritis.The therapeutic effect of PD173074 has been preliminarily validated in the type Ⅱ collagen-induced arthritis model and may act by inhibiting FGFR1 phosphorylation,which provides a direction for the search of new therapeutic targets for rheumatoid arthritis.