The rising incidence of thyroid cancer presents a growing diagnostic and therapeutic challenge. Various risk stratification systems have sought to integrate clinical, ultrasonographic, and, in some cases, cytological features to aid malignancy prognostication. This systematic review aims to critically evaluate risk stratification tools (RSTs) for patients with thyroid nodules, which incorporate multimodal inputs to assess their diagnostic performance and clinical utility in supporting surgical decision-making.

PubMed, Embase, and Cochrane databases were searched from inception to 04/13/2026, identifying studies evaluating multivariable risk prediction models for adult patients undergoing assessment of thyroid nodules. Studies were excluded if the proposed tool failed to incorporate clinical features, ultrasound findings, and cytology results or was not validated with histology. Data extraction encompassed methodology of model development, performance metrics, and approaches to validation. Risk of bias was assessed using the PROBAST+AI tool.

Seven studies describing five distinct RSTs met inclusion criteria Thyroid Nodule App (TNAPP), the McGill Thyroid Nodule Score (MTNS), CUT Score, Memorial Sloan Kettering Cancer Centre (MSKCC) nomogram, and Thyroid Prediction Score (TiPS). TiPS demonstrated the highest sensitivity (96.2%) and specificity (97.5%) with area under the curve (AUC) >0.9. The CUT score also showed strong performance (AUC >0.9), particularly in low-to-intermediate risk nodules. TNAPP underperformed (accuracy 50.5%; specificity 27.5%) despite broad clinical inputs. The MTNS and MSKCC, although promising for indeterminate cytology, lacked robust validation. Most models were derived from single-center, retrospective cohorts, limiting generalizability.

RSTs integrating multimodal data may improve thyroid nodule risk stratification, particularly in cases of indeterminate cytology. However, methodological limitations and lack of external validation currently restrict clinical utility. Prospective evaluation in diverse populations is required to identify the most effective and generalizable tools. Until then, RSTs should be used as adjuncts to, not replacements for, clinical judgment and shared decision-making in thyroid nodule assessment.

Analyzing the network properties of cancer biomarkers within protein-protein interaction (PPI) networks is valuable for discovering novel biomarker candidates. Therefore, we constructed PPI networks using breast cancer (BC)-associated gene sets and performed 12 distinct centrality analyses to characterize the topological features of clinically validated biomarkers. Our reference set of biomarkers comprised genes from five clinical genetic testing panels-MammaPrint, Oncotype DX, PAM50, EndoPredict, and the BC Index-that were also present in the STRING database. The PPI networks were constructed from the top 2,000 BC-associated genes, ranked by disease score from the DISEASES database. These networks were then subjected to centrality analysis using five local and seven global measures. The top 5% centrality rankings were evaluated, demonstrating that maximum clique centrality (MCC) identified the highest proportion of known biomarkers, with an inclusion rate of approximately 36%. Furthermore, MCC generated a unique biomarker-ranking pattern, exhibiting a Spearman's rank correlation coefficient below 0.8 when compared with all other metrics. Consequently, a high MCC score is a key topological feature of many validated biomarkers. Genes with the highest MCC scores (top 5%) were significantly enriched for gene-ontology terms related to the cell cycle and fibroblast growth factor receptor signaling pathway. Additionally, biomarkers with high MCC scores exhibited significantly greater evolutionary conservation and potential for protein complex formation. Collectively, our findings indicate that many effective BC biomarkers are components of large, evolutionarily conserved cliques within cell-cycle-associated regions of the PPI network. Finally, based on this MCC-centric approach, we identified 11 novel candidate biomarkers.

Deep learning (DL) algorithms for digital breast tomosynthesis (DBT) have proliferated, demonstrating emerging potential in enhancing lesion detection and classification.

This study aimed to compare the diagnostic performance of DL algorithms for DBT with that of radiologists of varying experience and assess the clinical impact of DL assistance.

A systematic search of PubMed, Embase, Web of Science, and the Cochrane Library was conducted up to November 8, 2025. Included studies compared the performance of stand-alone DL algorithms for DBT, radiologist interpretation alone, and DL-assisted diagnosis. Study quality was assessed using the Prediction Model Risk of Bias Assessment Tool+Artificial Intelligence (PROBAST+AI). Performance metrics were pooled using bivariate random effects and generalized linear mixed models.

A total of 13 studies with 38,565 patients were included in the final analysis. Stand-alone DL algorithms achieved a pooled sensitivity of 0.88 (95% CI 0.80-0.93), specificity of 0.74 (95% CI 0.59-0.85), and area under the receiver operating characteristic curve (AUC) of 0.89 (95% CI 0.86-0.92). While DL performance showed no statistically significant difference compared to all radiologists (AUC=0.89 vs 0.88; P=.64) or senior radiologists (AUC=0.89 vs 0.90; P=.48), DL demonstrated significantly superior sensitivity compared to junior radiologists (0.88 vs 0.76; P=.03). Notably, DL assistance did not statistically improve diagnostic metrics for radiologists across any experience level. Meta-regression identified validation methods as a significant source of heterogeneity.

DL algorithms for DBT exhibited strong diagnostic proficiency and showed higher sensitivity than junior radiologists, suggesting their potential utility as adjunctive tools to help reduce oversight in less experienced settings. However, given that DL assistance did not significantly elevate overall human performance, current models act primarily as supplementary aids rather than definitive clinical tools. Future prospective multimodal studies are warranted to validate these findings and optimize clinical integration.

Numerous proteins display pleiotropic functions in different clinical contexts. However, the molecular mechanism underlying such effects is rarely understood. Speckle-type POZ protein (SPOP) is a typical example, exhibiting tumor-suppressing or tumor-promoting effects in different tumor types in accordance with different amino acid changes; specifically, two distinct sets of variants in SPOP are commonly found in subsets of prostate cancer and endometrial cancer patients. To comprehensively characterize the functional landscape of SPOP alteration, we performed a deep mutational screening (DMS), elucidating the functionality of 7,933 out of 8,228 possible single amino acid changes (96.4% coverage). Leveraging the observation that overexpression of human SPOP leads to yeast growth arrest, we assessed the functionality of each variant using a yeast proliferation assay. In addition, our approach combined long-read and short-read sequencing. Finally, our DMS model enables a clear distinction of likely-loss-of-function variants that are enriched in prostate cancers and reveals their differential characteristics in both protein structure and genetic assessments. These results demonstrate the utility of our approach in high-resolution mapping and amino acid-level interpretation of protein function.

A 67-year-old woman with a history of thyroid and ovarian cancer presented with recurrent right upper eyelid ptosis after 3 blepharoplasties. Slit-lamp exam revealed a superior subtarsal and subconjunctival lesion (25×15 mm). MRI showed eyelid and conjunctival involvement extending into the extraconal orbital space, lacrimal gland, and superior rectus muscle. Histopathology and immunohistochemistry confirmed extranodal marginal zone MALT lymphoma. She received 4 cycles of systemic R-CHOP immunochemotherapy, achieving complete lesion remission, though ptosis persisted. She remains under surveillance without recurrence. Conjunctival MALT lymphoma represents the most common malignant neoplasm of the conjunctiva. It generally exhibits an indolent clinical course and few symptoms, frequently resulting in delayed recognition. Recurrent eyelid ptosis is an uncommon presenting feature, underscoring the need for a high index of clinical suspicion. Definitive diagnosis depends on tissue biopsy followed by histopathologic and immunohistochemical characterization. Current therapeutic approaches include radiotherapy, chemotherapy, and biological agents, all of which achieve excellent rates of local control.

Pregnancy-related breast cancer is relatively rare, but its incidence is increasing as the age of childbearing advances. The impact of placenta-specific microRNAs (miRNAs) derived from the chromosome 19 miRNA cluster (C19MC) on pregnancy-associated breast cancer is unclear. Nuclear protein high mobility group box 3 (HMGB3) plays a role in cancer progression. This study examined the effects of placenta-specific C19MC miRNAs on the cancer-related gene HMGB3 in the human breast cancer cell line MCF-7.

We used target gene prediction programs to identify C19MC miRNAs that modulate HMGB3 and then validated them using analytical procedures (i.e., quantitative PCR, Western blot, and luciferase reporter assay). We investigated how inhibition of HMGB3 by C19MC miRNAs affects the invasive and proliferative ability of MCF-7 cells and explored the downstream effectors of this axis.

C19MC miRNA miR-515-3p targeted HMGB3. In MCF-7 cells, reduction of HMGB3 expression by miR-515-3p increased cell invasion and proliferation. Furthermore, miR-515-3p-mediated HMGB3 inhibition led to the upregulation of CTNNB1 and GRB2, implicating invasion- and proliferation-related signaling pathways (e.g., WNT/β-catenin, Ras/MAPK, and PI3K/AKT/mTOR) in MCF-7 cells.

The impact of C19MC miRNA miR-515-3p on the cancer-related gene HMGB3 in MCF-7 cells suggests a potential tumor-suppressive role for HMGB3 that contrasts with previous reports of oncogenic activity. The present findings raise the possibility that placenta-specific C19MC miRNAs play a role in pregnancy-related breast cancer during pregnancy.

This study aimed to evaluate whether MYCN amplification enhances ferroptosis susceptibility in retinoblastoma (RB) and to elucidate the key regulatory mechanisms.

MYCN-amplified Y79 and MYCN copy-number gain WERI-RB1 cells were treated with ferroptosis inducers imidazole ketone erastin (IKE) and RSL3. MYCN was silenced using short hairpin RNA (shRNA) lentiviral transduction, followed by RNA sequencing, real-time quantitative PCR, and western blot. Amino acid deprivation and propargylglycine (PAG) treatment were applied to probe cysteine metabolic pathways. Glutathione (GSH), malondialdehyde (MDA), and ferrous iron (Fe2+) levels were measured, and ultrastructural changes were examined by transmission electron microscopy. An orthotopic xenograft model was used to assess the effect of IKE, PAG, and their combination on tumor growth.

MYCN amplification correlated with increased ferroptosis sensitivity, which was reduced by MYCN knockdown. Transcriptome analysis identified dysregulation of cysteine/methionine metabolism, with decreased xCT subunits and cystathionine γ-lyase (CTH) expression. PAG potentiated IKE-induced ferroptosis and triggered cell death alone, while MYCN depletion attenuated lipid peroxidation, restored GSH, and reduced Fe2+ accumulation. In vivo, IKE, PAG, and IKE+PAG all significantly inhibited intraocular tumor growth.

MYCN promotes ferroptosis in RB via xCT and transsulfuration pathways. Targeting the MYCN-xCT-transsulfuration axis may offer a novel therapeutic approach for MYCN-amplified RB.

Asbestos is a risk factor for occupational lung cancer. This article reported a case of asbestosis. During regular chest CT scans, an enlargement of nodules in the left lower lobe was observed. After undergoing relevant examinations, the patient underwent left lower lobe resection. Postoperative pathology confirmed the diagnosis as SMARCA4-deficient undifferentiated tumor. The patient was subsequently treated with immunotherapy, radiotherapy and chemotherapy and currently has a good prognosis.

Hydroxychloroquine (HCQ) usage in COVID patients was a popular topic of study, especially during the first wave of the pandemic. However, the long-term impact of HCQ therapy on infected COVID-19 patients remains unclear.

Holding a PROSPERO registration (CRD42025113906), this study aimed to investigate the impact of long-term treatment with HCQ in patients with autoimmune diseases on mortality, as well as on the development of disease-related complications.

A comprehensive search was conducted across multiple databases. Full-text reports were included for clinical trials and observational studies on adult patients with autoimmune disease and confirmed COVID-19 infection subjected to HCQ therapy.

The search process has identified 1,126 studies, of which 17 observational studies were included.No randomized controlled trials meeting the inclusion criteria were found. Eligible studies involved 229,142 autoimmune patients treated with HCQ, of which 197,118 patients were diagnosed with COVID-19. In 14 observational studies (196,965 patients), HCQ use was associated with a lower overall mortality rate by 21% in patients with autoimmune diseases and COVID-19 (RR 0.79; 95% CI: 0.64-0.97, p = 0.02). This association may reflect a potential survival benefit; however, given the observational nature of the studies included, causal inference cannot be established, and the findings should be interpreted cautiously. There was no significant difference between HCQ-treated patients and untreated patients regarding hospitalization (12 studies with 2,238 patients included), ICU admission (8 studies with 527 patients included), mechanical ventilation (8 studies with 546 patients included), sepsis (2 studies with 132 patients included), or thrombo-embolic events rates (2 studies with 195 patients included) (RR 0.92, 95% CI 0.75- 1.14; p = 0.46), (RR 1.45; 95% CI; 0.82- 2.56, p = 0.2) and (RR 1.28; 95% CI; 0.68- 2.4, p = 0.44), (RR 1.44; 95% CI; 0.57 - 3.65, p = 0.44), (RR 0.89; 95% CI; 0.16-4.97, p = 0.89), respectively. Nonetheless, the incidence of Acute Kidney Injury (AKI) in 2 studies (136 patients) was higher in HCQ-treated groups compared to the untreated groups (RR 2.31; 95% CI; 1.29-4.12, p = 0.0047).

HCQ was associated with a significantly lower overall mortality rate in patients with autoimmune diseases and COVID-19; this association is consistent with its known immunomodulatory properties. On the other hand, it does not prevent COVID-19-related complications and could be associated with an increased risk for developing AKI. However, given the observational nature of all included studies, causal inference cannot be established. Future research is needed to confirm these observed survival benefits and to establish clear safety parameters regarding renal toxicity.

Depressive symptoms and sleep problems are prevalent among older adults with depression. To reduce their adverse impact, it is important to understand the changes in symptom patterns as the Coronavirus disease 2019 (COVID-19) pandemic emerged. This longitudinal study examined the interactive changes between depressive symptoms and sleep problems among older adults with depression before and during the COVID-19 pandemic from a network perspective in the USA.

This network analysis study was based on data from the three waves (2016, 2018, and 2020) of the Health and Retirement Study (HRS). Depressive symptoms were measured using the eight-item version of the Center for Epidemiologic Studies Depression Scale (CESD-8), and sleep problems were assessed with the four-item Jenkins Sleep Scale (JSS-4). The study examined central symptoms and bridge symptoms within the network model.

A total of 2905 older adults with depression were included in the analyses. The prevalence of depressive symptoms did not significantly change in the study wave during the COVID-19 compared to the pre-pandemic waves. "Feeling Depressed" was the most central symptom of the depression-sleep problems network in the 2016 wave, while "Feeling Sad" was the most central symptom in both the 2018 and 2020 waves. Additionally, "Feeling Loneliness" was the key bridge symptom of the depression-sleep problems network in the 2016 wave, while "Not Enjoying Life" was the key bridge symptom in the 2018 wave, and "Feeling Rested in Morning" was the key bridge symptom in the 2020 wave.

The findings highlighted that central and bridge symptoms were potential targets in treating depressive symptoms and sleep problems among older adults with depression across the study period in the USA.