Objective To preliminary explore the imaging manifestations of digital breast tomosynthesis (DBT) and contrast-enhanced mammography (CEM) in triple-negative breast cancer (TNBC) patients with different levels of human epidermal growth factor receptor 2 (HER2) expression. Methods A retrospective analysis was conducted on TNBC patients who underwent preoperative DBT or CEM examinations at Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from January 2018 to December 2019 and Shanghai Second People’s Hospital from January 2022 to May 2025. Clinical data, pathological and immunohistochemical results, and imaging data were collected. Results A total of 69 TNBC patients pathologically confirmed as invasive ductal carcinoma were included, among which 34 underwent DBT and 35 underwent CEM. Among these patients, 34 (49.28%) had HER2-low expression and 35 (50.72%) had HER2-zero expression. DBT results showed that the proportion of spiculation signs in HER2-low group (n＝14) was significantly higher than that in HER2-zero group (n＝20; P＝0.009, P_adj＝0.045). However, there were no significant differences in breast density type, mass shape, or calcification between the two groups. CEM results showed that on low-energy images, the proportion of spiculation signs in the HER2-low group (n＝20) was higher than that in the HER2-zero group (n＝15; P＝0.011, P_adj＝0.077). Results of CEM showed that on reconstructed images, differences in background parenchymal enhancement and mass enhancement patterns between the two groups were not statistically significant; in both groups, heterogeneous enhancement was the most common, followed by homogeneous enhancement, with ring enhancement being the least common. Conclusions TNBC with low HER2 expression and TNBC with zero HER2 expression may have potential differences in the presentation of spiculation signs on DBT. However, the correlation between CEM manifestations and TNBC with different HER2 expression levels requires further research.

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.

Objective: To evaluate the diagnostic performance of magnetic resonance spectroscopy (MRS) in discriminating fumarate hydratase-deficient (FH-d) uterine leiomyomas (ULs) from FH-preserved ULs. Materials and Methods: This study consisted of three stages, with independent cohorts recruited for each stage: 1) sample-size estimation was retrospectively performed on UL specimens (diameter ≥3 cm; age, 20–40 years) from our database with immunohistochemistry (IHC) for 2-succinocysteine (2-SC) as the reference, without genetic testing, 2) MRS sequence optimization in confirmed FH germline mutation participants with ultrasound-detected ULs (diameter ≥3 cm), without IHC analysis, and 3) prospective diagnostic test accuracy was evaluated in consecutive participants with ultrasound-detected ULs (diameter ≥3 cm;age, 20–40 years), using IHC for 2-SC for determining the FH status and subsequent genetic testing in those with positive 2-SC results to identify whether FH mutations were germline or somatic in origin. The choline and fumarate peaks in MRS were classified as positive, negative, or technical failure (TF). TFs were analyzed separately and excluded from the primary diagnostic accuracy calculations. T1-, T2-, and diffusion-weighted images were interpreted as hyperintense or hypointense. The enhancement rate and apparent diffusion coefficient were also acquired. Diagnostic performance was compared between MRS and various magnetic resonance imaging (MRI) features. Results: The optimal MRS parameters for the fumarate peak were echo time (TE) = 140 ms and an average of 256. Among the 360 prospective participants, 37 were confirmed to have FH-dULs. MRS showed positive fumarate peaks in 35 of 37 FH-dULs.After excluding six TFs, the positive fumarate peak on MRS showed 94.6% (35/37) sensitivity, 99.7% (316/317) specificity, and 99.2% (351/354) accuracy, all of which were significantly superior to those of other MRI features (P ≤ 0.002). Conclusion A positive fumarate peak on MRS may be a useful imaging biomarker for diagnosing FH-dULs.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

ObjectiveTo construct a prediction model for high responsiveness to short-term aerobic training in patients with Parkinson's disease (PD) based on dual-task gait assessment, and to provide evidence for identifying individuals who are most likely to benefit from such interventions. MethodsA total of 42 patients with PD who visited the outpatient clinic of Tianjin Huanhu Hospital between August 29, 2023 and December 19, 2024 were enrolled. All participants completed a 4-week home-based aerobic cycling training program and underwent dual-task gait assessments pre- and post-training. Patients were classified into high-response group (n = 12) and low-response group (n = 30) according to whether the improvement in gait speed reached the minimal clinically important difference of 0.1 m/s. The clinical validity of this grouping was verified by comparing differences in cardiopulmonary exercise testing parameters and dual-task gait indicators between the two groups pre- and post-training. Based on baseline characteristics, Firth's penalized maximum likelihood logistic regression was applied to construct a predictive model for high responsiveness, and the discriminative performance of the model was assessed preliminarily. ResultsThe high-response group showed significantly greater improvement in gait speed, step length, cadence and the anaerobic threshold to maximal oxygen uptake ratio after training compared with the low-response group (P < 0.05), confirming the clinical validity of the grouping. The baseline anaerobic threshold to maximal oxygen uptake ratio (OR = 3.348), body mass index (OR = 2.229) and age (OR = 0.428) demonstrated certain discriminative ability in the present sample. The area under the receiver operating characteristic curve was 0.831, with an overall prediction accuracy of 81.0%, specificity of 83.3% and sensitivity of 75.0%. Robustness of the model was confirmed via internal validation using the Bootstrap method, which suggested that the results were informative, but further validation was still required. ConclusionThe anaerobic threshold/maximal oxygen uptake ratio, body mass index and age may predict the response to short-term aerobic training in patients with PD.

ObjectiveThe present study aimed to investigate alterations in white matter microstructure and spontaneous neural activity in male college smokers, and to further explore their associations with nicotine dependence. Given that adolescence and early adulthood represent critical periods for brain maturation, particularly for white matter development, understanding the neural correlates of smoking behavior during this stage is of substantial importance for both neuroscience and public health. MethodsA total of 115 male undergraduate students were initially recruited for this study. After quality control and exclusion procedures, 52 male college smokers and 42 demographically matched healthy non-smokers were included in the final analysis. All participants underwent multimodal magnetic resonance imaging (MRI), including diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI). White matter fiber tracts were reconstructed using the automated fiber quantification (AFQ) method, which enables precise identification and quantification of major fiber bundles. Eighteen major white matter tracts were segmented for each participant. Along the core trajectory of each tract, 100 equidistant nodes were sampled. Fractional anisotropy (FA) was calculated at each node to assess white matter microstructural integrity, while amplitude of low-frequency fluctuation (ALFF) was computed to evaluate spontaneous neural activity within white matter tracts. Between-group differences in FA and ALFF were assessed using two-sample t-tests, with appropriate corrections applied for multiple comparisons. Furthermore, Pearson correlation analyses were conducted to examine the relationships between imaging-derived metrics (FA and ALFF values in regions showing significant group differences) and nicotine dependence severity, as measured by the Fagerström test for nicotine dependence (FTND). ResultsCompared with healthy non-smokers, male college smokers exhibited significantly increased FA values in several white matter tracts, including the left thalamic radiation, right corticospinal tract, forceps major of the corpus callosum, left uncinate fasciculus, and right arcuate fasciculus. These findings suggest altered microstructural organization or increased directional coherence within these pathways. In addition, smokers demonstrated significantly elevated ALFF values in the forceps major, right uncinate fasciculus, and left arcuate fasciculus, indicating enhanced spontaneous neural activity in these white matter regions. Correlation analyses revealed that FA values in the left thalamic radiation and right corticospinal tract were negatively correlated with FTND scores, suggesting that higher levels of nicotine dependence were associated with reduced microstructural integrity or altered fiber organization in these regions. In contrast, ALFF values in the forceps major and right uncinate fasciculus were positively correlated with FTND scores, indicating that greater nicotine dependence was associated with increased spontaneous neural activity in specific white matter pathways. ConclusionThe present study provides evidence that male college smokers exhibit distinct alterations in both white matter microstructure and functional activity. These abnormalities are not uniformly distributed but rather localized to specific fiber tracts implicated in sensorimotor processing, interhemispheric communication, and higher-order cognitive and emotional regulation. Importantly, the observed associations between imaging metrics and nicotine dependence severity suggest that these structural and functional alterations may reflect neurobiological mechanisms underlying addiction. The combination of AFQ-based tract profiling and multimodal MRI offers a sensitive approach for detecting subtle changes along white matter pathways, highlighting its potential utility in identifying neuroimaging biomarkers of nicotine dependence. Overall, these findings indicate that smoking during early adulthood may disrupt ongoing white matter maturation, potentially leading to long-term consequences for brain function. This study provides novel insights into the neural basis of nicotine dependence and underscores the importance of early intervention and prevention strategies targeting young smokers.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Background: The association of changes in metabolic syndrome (MetS) with cognitive function remains unclear. We explored this association using prospective and Mendelian randomization (MR) studies. Methods: MetS components including high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), waist circumference (WC), fasting plasma glucose (FPG), and triglycerides were measured at baseline and two follow-ups, constructing a MetS index. Immediate, delayed memory recall, and cognitive function along with its dimensions were assessed by immediate 10- word recall test (IWRT) and delayed 10-word recall test (DWRT), and mini-mental state examination (MMSE), respectively, at baseline and follow-ups. Linear mixed-effect model was used. Additionally, the genome-wide association study (GWAS) of MetS was conducted and one-sample MR was performed to assess the causality between MetS and cognitive function. Results: Elevated MetS index was associated with decreasing annual change rates (decrease) in DWRT and MMSE scores, and with decreases in attention, calculation and recall dimensions. HDL-C was positively associated with an increase in DWRT scores, while SBP and FPG were negatively associated. HDL-C showed a positive association, whereas WC was negatively associated with increases in MMSE scores, including attention, calculation and recall dimensions. Interaction analysis indicated that the association of MetS index on cognitive decline was predominantly observed in low family income group. The GWAS of MetS identified some genetic variants. MR results showed a non-significant causality between MetS and decrease in DWRT, IWRT, nor MMSE scores. Conclusion Our study indicated a significant association of MetS and its components with declines in memory and cognitive function, especially in delayed memory recall.

Abuse of amphetamine-based stimulants is a primary public health concern. Recent studies have underscored a troubling escalation in the inappropriate use of prescription amphetamine-based stimulants. However, the neurophysiological mechanisms underlying the impact of acute methamphetamine exposure (AME) on sleep homeostasis remain to be explored. This study employed non-human primates and electroencephalogram (EEG) sleep staging to evaluate the influence of AME on neural oscillations. The primary focus was on alterations in spindles, delta oscillations, and slow oscillations (SOs) and their interactions as conduits through which AME influences sleep stability. AME predominantly diminishes sleep-spindle waves in the non-rapid eye movement 2 (NREM2) stage, and impacts SOs and delta waves differentially. Furthermore, the competitive relationships between SO/delta waves nesting with sleep spindles were selectively strengthened by methamphetamine. Complexity analysis also revealed that the SO-nested spindles had lost their ability to maintain sleep depth and stability. In summary, this finding could be one of the intrinsic electrophysiological mechanisms by which AME disrupted sleep homeostasis.
Animals ; Methamphetamine ; Electroencephalography ; Male ; Sleep/drug effects* ; Central Nervous System Stimulants ; Delta Rhythm/drug effects* ; Sleep Stages/drug effects*

Dry eye disease (DED) is a prevalent and intractable ocular disease induced by a variety of causes. Elevated sphingomyelin (SM) levels and pro-inflammatory cytokines were detected on the ocular surface of DED patients, particularly in the meibomian glands. Sphingomyelin synthase 2 (SMS2), one of the proteins involved in SM synthesis, would light a novel way of developing a DED therapy strategy. Herein, we report the design and optimization of a series of novel thiophene carboxamide derivatives to afford 14l with an improved highly potent inhibitory activity on SM synthesis (IC_{50, SMS2} = 28 nmol/L). Moreover, 14l exhibited a notable protective effect of anti-inflammation and anti-apoptosis on human corneal epithelial cells (HCEC) under TNF-α-hyperosmotic stress conditions in vitro, with an acceptable ocular specific distribution (corneas and meibomian glands) and pharmacokinetics (PK) profiles (t _{1/2, cornea} = 1.11 h; t _{1/2, meibomian glands} = 4.32 h) in rats. Furthermore, 14l alleviated the dry eye symptoms including corneal fluorescein staining scores and tear secretion in a dose-dependent manner in mice. Mechanically, 14l reduced the mRNA expression of Tnf-α, Il-1β and Mmp-9 in corneas, as well as the proportion of very long chain SM in meibomian glands. Our findings provide a new strategy for DED therapy based on selective SMS2 inhibitors.