Individuals with severe mental illness (SMI) experience higher mortality rates than the general population. We conducted a comprehensive, population-based study to quantify the excess life-years lost (LYLs), mortality risks, and cause of death among SMIs.

Using data from the Jiangsu Province Management Information System for Patients with SMI, we identified 321,436 SMI patients in Jiangsu, China, between January 2014 and December 2022; 33,947 of them died during follow-up. We calculated the excess LYLs for each type of SMI and estimated the standardized mortality ratios (SMRs) for all-cause and specific-cause mortality among individuals with four types of SMIs.

The excess LYLs in four types of SMIs were as follows: for schizophrenia, 12.87 years in males and 11.92 years in females; for bipolar disorder, 8.43 years in males and 5.54 years in females; for paranoid disorder, 13.90 years in males and 14.03 years in females; and for schizoaffective disorder, 16.69 years in males and 15.82 years in females, when compared to the general population. Individuals with SMI had nearly twice the all-cause mortality of the general population (adjusted SMR 1.96, 95% CI 1.94-1.98). Elevated mortality rates were observed across all types of SMIs, with SMRs varying from 1.23 to 2.96. The SMRs for specific causes showed distinct patterns, with infectious diseases ranging from 1.60 to 3.08, neoplasms from 0.73 to 1.15, diabetes from 1.82 to 3.89, nervous system diseases from 1.44 to 3.15, cardiovascular diseases from 1.05 to 2.35, cerebrovascular diseases from 1.26 to 2.18, respiratory diseases from 0.86 to 1.69, digestive diseases from 1.25 to 2.66, accidents from 2.15 to 4.01, suicide from 6.16 to 14.81, and all other causes from 2.06 to 4.33 across four types of SMIs; alcohol misuse was elevated only in schizophrenia (SMR 6.57), with < 10 events in the other disorders.

Individuals with SMI in China experienced marked loss of life expectancy and faced nearly double the mortality, with cause-specific patterns varying by diagnosis. Our findings provide insights into the need for our government to continually enhance physical and mental health policies to prevent and decrease premature mortality in individuals with SMI.

Since 2020, co-testing for women over the age of 35 has been a new strategy for cervical cancer screening in Germany. In this study, we investigated the added value of co-testing in organized cervical screening for the German screening population.

A total of 1956 women were referred to our large dysplasia consultation institution due to abnormal screening results in the first round of screening between 2020 and 2022. The endpoint of this retrospective study was the posttest probability of a CIN 2+/CIN 3 + lesion on the cervix uteri. In addition, the influence of HPV subtypes on the endpoints was investigated.

CIN 2 + or CIN 3 + lesions were diagnosed in 21.6% and 13.0%, respectively, of the patients. The probability of severe dysplasia increases with the grade of cytological abnormality and is dependent on the HPV subtype. The probability for detecting invasive cervical cancer (16 patients in total) was significantly higher for women with HPV16 infection than for women with HPV infection of other high-risk types (odds ratio 11.18; 95% confidence interval 3.01, 40.81). CIN 2 + lesions were diagnosed in 10.1% of patients with normal cytology.

CIN 2 + lesions could be detected in more than every 5th woman with an abnormal screening result. The proportion of CIN 3 + patients was significantly higher in the presence of HPV 16.

Discoveries of actionable gene alterations (AGAs) have driven significant advances in targeted therapies, enhancing survival for non-small cell lung cancer (NSCLC) patients. In cases of AGAs other than common EGFR, ALK, and ROS1 mutations, target therapies have various strengths of the recommendation depending on their previous studies. Meanwhile, the immune checkpoint inhibitors (ICIs) combined with chemotherapy is still widely used as a first-line treatment for most of NSCLC with AGAs due to limited accessibility of target therapy.

This study included patients with NSCLC harboring AGAs who received chemotherapy plus ICIs (CT + IO) or chemotherapy alone (CT) as a first-line treatment between January 2019 and May 2023 at Samsung Medical Center. We assessed the objective response rate (ORR), progression-free survival (PFS), time to next treatment (TTNT), and overall survival (OS) between treatment groups. Subgroup analyses were conducted in the CT + IO group according to the type of AGAs and PD-L1 expression.

In total, 163 NSCLC patients with AGAs other than common EGFR, ALK, and ROS1 were included, with a median age of 62.4 years. 57 (35.0%) patients received CT + IO and 106 (65.0%) received CT. The proportions of patients with EGFR exon 20 insertion (E20I), HER2 mutation (mHER2), RET fusion (RET), and MET exon 14 skipping (METex14) were 28.8% (n = 47), 39.9% (n = 65), 16.6% (n = 27), and 14.7% (n = 24), respectively. With 32.0 months of median follow-up duration (range: 28.5-37.0), median PFS was 8.0 months (95% CI, 6.0-12.8) in CT + IO and 6.4 months (95% CI, 5.5-8.0) in CT (HR: 0.71, 95% CI, 0.49-1.03). In CT + IO group, median PFS by AGAs were 5.0 months (95% CI, 3.2-NA), 8.3 months (95% CI, 6.0-NA), 5.8 months (95% CI, 4.5-NA), and 17.1 months (95% CI, 10.7-NA) for E20I, mHER2, RET and METex14 respectively. Among these, 44.4% of METex14 patients had a PD-L1 TPS of 50% or higher. 24-month OS rate according to PD-L1 expression were 45.4%, 56.3%, and 81.5% in PD-L1 TPS < 1%, 1 to 49%, and ≥ 50%.

The combination of chemotherapy with ICIs in NSCLC patients with AGAs other than common EGFR, ALK, and ROS1 mutations demonstrated similar clinical outcomes compared to conventional chemotherapy. Patients with METex14 were associated with higher response rate and longer PFS among patients with AGAs when treated with chemotherapy plus ICIs. Also, the efficacy of the chemotherapy plus ICIs is positively correlate to the PD-L1 expression.

To identify risk factors for PICC-related bloodstream infections (PICC-CRBSI) in patients with hematologic malignancies and to develop and validate a predictive nomogram for clinical risk assessment.

This retrospective cohort study included 764 patients with hematologic malignancy who had PICC from a tertiary center (2021-2024). LASSO regression identified key predictors after addressing class imbalance via SMOTE-NC in the training set (n = 534). A multivariate logistic regression was used to construct a predictive nomogram, subsequently validated in a validation set (n = 230). Model performance was assessed by area under the receiver operating characteristic curve (AUC), calibration curves with bootstrap resampling, and decision curve analysis (DCA).

In our cohort, the occurrence of PICC-CRBSI infections was 6.02% (46/764) and Gram-negative bacteria was the major causative pathogen. Multivariate regression analysis showed that History of diabetes, Age, Time of PICC placement, PICC insertion attempts, Catheterized Diameter, Catheterized vein, ANC, ALC and D-dimer were independent risk factors associated with PICC-CRBSI in hematologic malignancy patients. Our nomogram model demonstrated a good calibration and discrimination in both training and validation sets, with AUC values of 0.883 and 0.822, The DCA suggested potential clinical utility.

This validated nomogram integrates patient, catheter, and biomarker-specific factors to individualize PICC-CRBSI risk stratification in hematologic malignancies, potentially guiding targeted prevention strategies.

This study was aimed to identify and investigate changes in serum metabolites across the natural progression of chronic hepatitis B (CHB) patients, discover differential metabolites and metabolic pathways in different disease phases, and provide an experimental foundation for clinical diagnosis, staging, treatment, and prognosis of CHB.

A total of 279 CHB patients hospitalized at Hunan Provincial People's Hospital from June 2020 to March 2021 were enrolled, including 47 in the immune-tolerant (IT) group, 44 in the immune-clearance (IC) group, 73 in the low-replication (LR) group, 57 in the enhanced-replication (ENH) group, 32 in the liver cirrhosis (LC) group, and 26 in the hepatocellular carcinoma (HCC) group, along with 44 healthy controls. Serum metabolites were analyzed using liquid chromatography-mass spectrometry (LC-MS). Preprocessed metabolic data were subjected to univariate and multivariate statistical analyses via MetaboAnalyst 6.0. The metabolites were mapped to the KEGG database to identify key differential metabolic pathways (Impact value > 0.02).

The results of principal component analysis (PCA) and Partial least squares discriminant analysis (PLS-DA) pattern recognition showed clustered distribution patterns of metabolites between CHB patients at different natural history phases and healthy controls. Venn diagrams of differential metabolites (DMs) across the six phases of CHB patients revealed that there were phase-specific DMs in each phase: retinyl beta-glucuronide and APGPR Enterostatin in the IT group; creatinine, deoxycorticosterone, O-phosphoethanolamine, and tetradecanedioic acid in the IC group; 4-Pyridoxic acid, sulfasalazine, calcium, and dodecanoylcarnitine in the LR group; oxoglutaric acid, creatine, and 8-Hydroxy-7-methylguanine in the ENH group; umbelliferone, leukotriene D4, malic acid, 3-methylcrotonylglycine, and sulfathiazole in the LC group; 4'-O-methyl-(-)-epicatechin-3'-O-β-glucuronide and DG(16:0/18:1(9Z)/0:0) in the HCC group. All these DMs exhibited AUC values > 0.7, indicating their potential as phase-specific biomarkers for CHB staging. Additionally, compared to healthy controls, there were 6 differential metabolic pathways in the IT group, 8 differential metabolic pathways in the IC group, 6 differential metabolic pathways in the LR group, 7 differential metabolic pathways in the ENH group, 9 differential metabolic pathways in the LC group, and 8 differential metabolic pathways in the HCC group. These differential metabolic pathways were primarily enriched in amino acid metabolism, glycerophospholipid metabolism, hormone metabolism, and energy metabolism.

Serum DMs and metabolic pathways existed in CHB patients across natural progression phases, demonstrating dynamic variations and potential as staging biomarkers.

Determining tumor-infiltrating lymphocyte (TIL) expression in breast cancer prior to treatment initiation is of considerable clinical significance. MRI demonstrates potential as a valuable adjunct to histopathological assessment. This study investigated the feasibility of developing a radiomics-based predictive model incorporating MRI and clinical features to determine TIL levels in breast cancer.

This multicenter retrospective cohort study enrolled 501 patients across two institutions. A total of 453 patients were utilized for model development, comprising a training cohort (n = 317) and internal validation cohort (n = 136), while 48 patients from an external center constituted the independent test cohort. Radiomics features were extracted and subsequently selected using ANOVA and LASSO regression. Logistic regression algorithms were employed for model construction. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminative performance between low-level and intermediate-high-level TILs, comparing the radiomics model with the integrated clinical-radiomics model.

The MRI-based radiomics model demonstrated robust performance for TIL level prediction across both training and internal validation cohorts, achieving areas under the curve (AUC) of 0.810 (95% confidence interval [CI]: 0.781-0.874) and 0.756 (95% CI: 0.676-0.837), respectively. The integrated clinical-radiomics model exhibited superior discriminative performance with AUCs of 0.828 (95% CI: 0.762-0.858) and 0.824 (95% CI: 0.755-0.893) for the training and validation cohorts, respectively. In the external independent test cohort, the radiomics model and integrated model achieved AUCs of 0.704 (95% CI: 0.558-0.850) and 0.767 (95% CI: 0.633-0.901), respectively.

Radiomics features extracted from dynamic contrast-enhanced MRI, when integrated with clinical characteristics, represent a promising non-invasive approach for predicting TIL levels in breast cancer. This methodology may facilitate clinical decision-making through enhanced pretreatment tumor characterization without requiring invasive tissue sampling.

Patients with differentiated thyroid cancer (DTC) and bone metastases (BM) exhibit heterogeneous radioactive iodine (RAI) and fluorine-18-fluorodeoxyglucose (¹⁸F-FDG) uptake patterns at diagnosis. This study is aimed to evaluate clinical outcomes influenced these distinct uptake patterns.

Patients confirmed DTC with BM were involved in this study between 2006 and 2021. All patients received ¹³¹I treatment and performed ¹⁸F-FDG positron emission tomography with computed tomography (PET/CT) at diagnosis. Variables including patient's gender, age, pathology, laboratory examination, uptake pattern of bone lesions, treatment protocols, and metabolic parameters of PET/CT were analyzed for the prognosis.

Among 67 enrolled DTC patients with BM, three uptake patterns were identified: RAI+/PET- (18 patients, 26.9%), RAI+/PET+ (40 patients, 59.7%), and RAI-/PET+ (9 patients, 13.4%). Univariate analysis revealed that age stratification, serum thyroglobulin (Tg) levels, and BM uptake pattern significantly impacted progression-free survival (PFS) and overall survival (OS). Critically, RAI+/PET + patients with RAI(+) ratio > 50% showed better PFS (range 12-156 months, median 38.5 months, p = 0.000) and OS (range 20-156 months, median 61.5 months, and p = 0.012) than those with RAI(+) ratio [Formula: see text]50% (PFS: range 6-92months, median 24 months; OS: range 28-112 months, median 51 months). Multivariate analysis identified total total lesion glycolysis of all bone lesions (tTLG) from PET/CT as an independent prognostic factor for both PFS and OS (p = 0.021 and p = 0.035, respectively).

The RAI(+) ratio determines clinical outcomes, and the prognosis of RAI+/PET + patients resembles that of RAI+/PET- patients, suggesting similar biological behavior in DTC patients with BM. Pretreatment tTLG is a significant independent prognostic marker for PFS and OS.

Immunotherapy has significantly improved survival in patients with metastatic melanoma, achieving objective response rates of 45-60% and long-term survival. However, there is scope and a need to further improve outcomes. Preclinical and early clinical data suggest synergistic effects between stereotactic body radiotherapy (SBRT) and checkpoint inhibitor immunotherapy with an acceptable safety profile.

AXIOM is a phase II, multicentre, randomised trial designed to evaluate the effectiveness and safety of upfront SBRT to all radiologically identified metastasis with immunotherapy over historical immunotherapy alone (standard of care) in patients with 1-5 extracranial melanoma oligometastases. The sample size calculation is based on a single-arm design along with a contemporary control arm to confirm historical data, without a formal comparison between the two arms. The study is 80% powered to detect a 15% absolute improvement in OS rate at the 3-year landmark in the interventional arm. A total of 129 patients will be randomised in a 2:1 ratio to receive either SBRT combined with immunotherapy (n = 86) or immunotherapy (n = 43). SBRT delivers minimum biologically effective dose of 48 Gy₁₀ to all lesions between cycles 1-3 of immunotherapy. Immunotherapy options include anti-PD-1 monotherapy or combination with anti-CTLA-4 or anti-LAG-3 agents. The primary endpoint is overall survival. Secondary endpoints include progression-free survival, overall response rate, local control, safety, and quality of life assessment. Correlative translational studies will explore potential predictive and prognostic biomarkers related to response, resistance or toxicity to treatment.

The results will inform the design of larger phase III trials and may identify patient subgroups most likely to benefit from combination therapy.

ClinicalTrials.gov: NCT06767306.

The purpose of this study was to evaluate the utility of fluorine-18-fibroblast activation protein inhibitor (¹⁸F-FAPI) PET/CT for detecting brain metastasis (BM) in different pathological types of lung cancer using craniocerebral MRI as the standard.

From December 2020 to October 2021, patients with pathologically confirmed lung cancer and suspected BM were prospectively enrolled and underwent paired ¹⁸F-FAPI PET/CT and MRI. The number of BMs and maximum tumor diameter were measured by MRI. The maximum and peak standardized uptake values (SUVmax and SUVpeak, respectively) and tumor-to-background ratio (TBR) on ¹⁸F-FAPI PET/CT in BMs were evaluated.

A total of 76 BM lesions from 18 patients (11 males and 7 females) were evaluated. Among these, 23 lesions were detected by ¹⁸F-FAPI PET/CT. The detection rate of BM in adenocarcinoma was 48.28%, which was significantly higher than that in large cell carcinoma (16.67%, P = 0.016) and small cell carcinoma (0%, P = 0.009), but showed no significant difference from that in squamous carcinoma (35.71%, P = 0.437). The detection rate in squamous carcinoma was significantly higher than that in small cell carcinoma (P = 0.043), while no significant differences were observed between large cell carcinoma and small cell carcinoma (P = 0.183), or between large cell carcinoma and squamous carcinoma (P = 0.191).

This study revealed differences in the detection rates of BM in lung cancer types by ¹⁸F-FAPI PET/CT, with the highest and lowest detection rates in adenocarcinoma and small cell carcinoma, respectively, which may be valuable for predicting the prognosis of lung cancer patients with BM.

Institutional review board approval NO. SDZLEC2021-112-02.

Tumor-associated cells in liquid biopsy are promising biomarkers for cancer detection, diagnosis, prognosis, and monitoring. Yet, their rarity, heterogeneity, and plasticity pose challenges for accurate identification and characterization. Enrichment-free whole slide imaging of all circulating cells offers a comprehensive, unbiased approach to capture this phenotypic diversity. However, current analysis methods often rely on engineered features and manual expert review, making them prone to technical variability and subjective bias. To address this, we present a deep contrastive learning framework for feature extraction from whole slide immunofluorescence microscopy images, enabling robust identification and stratification of single circulating cells. Our learned features achieve 92.64% accuracy in classifying diverse cell phenotypes and improve downstream tasks such as outlier detection and clustering. Additionally, our model enables automated identification and enumeration of rare phenotypes, reaching an average F1-score of 0.93 on contrived samples mimicking circulating tumor and endothelial cells, and 0.858 across circulating tumor cell phenotypes in clinical samples. This workflow provides a scalable, reproducible solution for analyzing tumor-associated cellular biomarkers, with strong potential to enhance clinical prognosis and guide personalized treatment strategies.