This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

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.

In patients with differentiated thyroid cancer (DTC), initial recurrence risk stratification based on clinical, histopathological, and perioperative data remains the key determinant for guiding management strategies during the first 1-2 years post-treatment. However, the adoption of ongoing risk stratification (ORS), which dynamically reassesses risk by integrating longitudinal clinical data and treatment response, enables more precise long-term prognostic assessment and facilitates highly individualized management. Building upon recent guidelines, the 2026 KTA guideline has been further refined by incorporating robust evidence from large-scale national cohorts and comprehensive systematic reviews. These updated recommendations outline contemporary concepts of ORS, risk-adapted TSH suppression targets, optimized surveillance modalities for recurrence detection, and disease-specific long-term follow-up strategies. Reflecting the paradigm shift toward de-escalated treatment, this revision integrates evolved perspectives on TSH suppression intensity, the clinical interpretation of thyroglobulin levels, and tailored follow-up intervals. These evidence-based recommendations aim to minimize unnecessary treatment and excessive surveillance in the large proportion of patients with excellent prognosis after initial therapy, while ensuring that each patient receives appropriately tailored and effective long-term management.

Objective: This study aimed to develop a reliable and valid Mixed State Severity Index (MSSI) to assess mood instability in patients with mood disorders and determine cutoff scores. Methods: Twenty-one items were selected based on Koukopoulos’ criteria for mixed depressive episode, historically referred to as agitated depression, and Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision mixed features criteria. The MSSI was administered to 242 patients (major depressive disorder [n=92], bipolar disorder [BD] I [n=78], and BD II [n=72]) and 726 controls. Results: The MSSI demonstrated high internal consistency (α=0.78–0.90). Exploratory factor analysis revealed a stable four-factor structure. Based on receiver operating characteristic analysis, optimal cutoff scores were identified to distinguish mood disorder groups from controls, ranging from 19.5 to 27.5 depending on diagnosis. Conclusion The MSSI is a reliable and valid instrument for assessing the severity of mixed features in patients with mood disorders. The established cutoff scores enhance its clinical utility, providing robust diagnosis and treatment planning support.

Objective To investigate the association between sleep behaviors （including bedtime， sleep quality， and nap duration） and academic performance among college students， as well as the regulatory effect of test anxiety and exercise habit on the association between sleep and academic performance. Methods A questionnaire survey was conducted to collect data. The grade of compulsory courses （with higher grades indicating better academic performance） was used as an ordinal dependent variable. All independent variables were coded by levels： for bedtime during exam and non-exam periods， higher levels indicated later bedtimes； for anxiety during the exam period， higher levels indicated more severe anxiety； for sleep quality during both exam and non-exam periods， higher levels indicated better sleep quality； for nap duration， higher levels indicated longer naps. Exercise habit was treated as a binary variable （yes/no）. Ordinal logistic regression （proportional odds model） was used for multivariable analysis， and OR and 95%CIwere calculated. In addition， sex-stratified models were established to investigate sex differences in the associations between these factors and academic performance. Results A total of 1 221 college students were included， among whom male students accounted for 33.7%. After controlling for confounding factors， bedtime during the exam period was significantly positively associated with the grade of compulsory courses， indicating that later bedtime was associated with a greater likelihood of achieving a higher grade （OR=1.32，95%CI 1.08‒1.62， P=0.007）. Sex was also an independent predictive factor， and the OR of achieving a higher grade in female students was 2.4 times that in male students （OR=2.41， 95%CI 1.90‒3.06， P<0.001）. Sleep quality during the exam period （OR=1.18，95%CI 0.99‒1.42， P=0.070） and exercise habit （OR=1.24， 95%CI 0.99‒1.55， P=0.064） showed marginally significant associations with grade. In contrast， bedtime during the non-exam period， nap duration， anxiety during the exam period， and self-rated sleep quality during the non-exam period showed no statistical significance （all P>0.05）. Sex-stratified analysis showed that among the male students， later bedtime during the exam period was associated with a greater likelihood of achieving a higher grade （OR=1.48， 95%CI 1.05‒2.09， P=0.025）， whereas higher anxiety during the exam period was associated with a lower likelihood （OR=0.79，95%CI 0.63‒0.98， P=0.032）； among the female students， better sleep quality during the exam period was associated with a greater likelihood of achieving a higher grade （OR=1.27， 95%CI 1.02‒1.57， P=0.033）， and bedtime during the exam period showed a positive association with academic performance， but without statistical significance （OR=1.25，95%CI 0.96‒1.62，P=0.091）. Conclusion The association between sleep behaviors and academic performance among college students varies with academic period and sex. Later bedtime during the exam period is associated with better academic performance， but this effect is observed only among male students. In addition， the academic performance of male students is negatively affected by test anxiety， whereas the academic performance of female students is positively associated with subjective sleep quality. Future interventions aimed at improving academic health should consider sex-specific strategies.
Anxiety ; Exercise

Background: Mycoplasma pneumoniae is a major cause of community-acquired pneumonia (CAP) in children, with a rising incidence of macrolide resistance. Early diagnosis is crucial for reducing the disease burden; however, current diagnostic tools have limitations.We evaluated the diagnostic accuracy of serological assays and their performance based on symptom onset in children with CAP. Methods: From September 2023 to September 2024, we prospectively enrolled children with CAP, classified as M. pneumoniae pneumonia (MPP) or non-MPP, from 16 hospitals in Korea. Serological testing included chemiluminescence immunoassay (CLIA) and ELISA for detecting IgM and IgG, along with particle agglutination (PA) for total antibody measurements. Serological responses were analyzed at different times after symptom onset (0–4, 5–9, and 10–21 days). Results: Among 472 children with CAP (362 MPP, 110 non-MPP), 138 (29.2%) underwent PA testing, and 334 (70.8%) underwent IgM testing. PA at a 1:640 cutoff showed 48.0% sensitivity and 100% specificity. CLIA and ELISA showed comparable sensitivities (69.1% vs. 69.2%) and specificities (76.9% vs. 66.7%) for IgM testing. Seropositivity increased significantly with time since symptom onset (P for trend < 0.001), reaching 97.9% for IgM, 62.5% for IgG, and 94.7% for PA at 10–21 days. Conclusions The time post-symptom onset significantly influenced the diagnostic utility of serological tests for pediatric MPP, which showed limited value during the early stage of illness. These findings emphasize the importance of symptom onset-based interpretation of serological test results and their utility in complementing PCR when optimizing MPP diagnosis in children.

Objective: To evaluate the impact of adding CT to ultrasound (US) for nodal assessment in patients with papillary thyroid microcarcinoma (PTMC), particularly in those with US-node-negative disease. Materials and Methods: This single-center retrospective study included consecutive patients with PTMC (≤1 cm on US) who underwent both US and CT for PTMC staging between August 2016 and January 2020, and subsequently underwent surgery including neck dissection. The number of patients with clinical N1 and pathological N1 disease was assessed. The diagnostic performance of US, CT, and combined US + CT (positive if either was positive) for macroscopic lymph node metastasis (LNM) (i.e., metastatic tumor foci >2 mm) was evaluated. Cases with discordant nodal staging between US and CT were identified.The diagnostic utility of CT was also assessed in a subgroup of patients with node-negative findings on US. Results: Among 982 patients (mean age ± standard deviation, 47.3 ± 11.5 years; 774 female), pathological analysis confirmed cervical LNM in 377 patients, including macroscopic, microscopic, and size-unknown LNM in 187, 175, and 15 patients, respectively. The addition of CT to US improved sensitivity for detecting macroscopic LNM compared to US alone (68.4% [128/187] vs. 26.7% [50/187]; P < 0.001), while maintaining high specificity despite a significant decrease (90.9% [709/780] vs. 97.2% [758/780]; P < 0.001). Discordant nodal staging between US and CT regarding macroscopic LNM was observed in 149 cases (15.2% [149/982]), with 131 patients (87.9% [131/149]) being upstaged by CT. In patients with node-negative US findings, CT detected US-undetected macroscopic LNM in 78 patients (8.7% [78/895]) and exhibited a sensitivity of 56.9% (78/137) and specificity of 93.5% (709/758) for macroscopic LNM. Conclusion The integration of CT with US improved sensitivity for detecting macroscopic LNM in patients with PTMC, identifying those who would otherwise be inappropriately considered candidates for active surveillance based solely on US findings. This may assist in refining patient management.

Purpose: Distant metastasis of breast cancer significantly affects the prognosis. Serum markers such as CEA and cancer antigen (CA) 15-3 are used for surveillance. However, the nonspecificity and nonmalignant elevation of CEA limit its absolute value in predicting metastatic recurrence. We reevaluated the clinical value of CEA kinetics in predicting metastatic recurrence in breast cancer. Methods: In this multicenter retrospective cohort study, we utilized electronic medical record data from eight institutions (2008–2023), including 4,872 surgically treated patients with invasive breast cancer. Novel CEA kinetic indices were defined.Predictive capabilities were assessed using logistic regression, Kaplan-Meier survival curves, and Cox proportional hazards models. Results: CEA velocity (vCEA) and last-interval vCEA (vCEA-L) showed higher accuracy and sensitivity in predicting distant metastasis than absolute CEA indices. All tumor marker indices were independent predictors; vCEA, vCEA-L, and maximum vCEA (vCEAmax) showed the highest association. In the 5-year distant disease-free survival analysis of patients, vCEA showed the most significant difference based on whether its cutoff value was exceeded (84.1% vs. 47.2%) (P < 0.001).Cox analysis showed that vCEA was the strongest predictor (hazard ratio, 4.509; P < 0.001). vCEAmax remained prognostic even when CEAmax was below its cutoff value. Conclusion Analysis of dynamic CEA changes, particularly velocity-based indices, offers superior predictive power than the static-based CEA indices for metastatic recurrence in breast cancer. These kinetic markers enhance prognostic accuracy when combined with CA 15-3. Future research should integrate these markers with imaging, liquid biopsy, and artificial intelligence for personalized follow-up.

It is extensively documented that tumor hypoxia contributes to the failure of chemotherapy and radiotherapy. Recent evidence suggests that hypoxia is also closely involved in the resistance to immunotherapy. In this review, we highlight how immune cells that are essential for the maximized immunotherapy efficacy, including cytotoxic T cells, dendritic cells, and natural killer cells, can adapt to tumor hypoxia. We then outline previous attempts targeting tumor hypoxia (for example, modulators of tumor cell oxygen consumption, perfusion modulators, hypoxia-activated prodrugs, hypoxia-inducible factor inhibitors, and hypoxia-responsive chimeric antigen receptor T cells) discussing how these approaches have resulted in an improvement of the antitumor response to immunotherapy in preclinical or clinical settings. Lastly, we review various non-invasive techniques to detect the tumor hypoxia and immune responses. We believe that an integration of the biological knowledge of immune cell adaptation to tumor hypoxia with the cutting edge non-invasive imaging technologies may ultimately allow us not only to select for patients who would benefit the most from the immunotherapy but also to monitor their responses in a real-time manner so that we can offer them an optimal personalized medicine in the clinic.